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Archive for the ‘BioIT: BioInformatics’ Category


Bio-IT World Announces 2018 Best Of Show Award Winners

Reporter: Aviva Lev-Ari, PhD, RN

Aviva Lev-Ari Retweeted Mary-Ann Moore

Aviva Lev-Ari added,

  1. Award-winning Science Writer; Columnist, New York Times; Author of She Has Her Mother’s Laugh, 2018) Up to 30 top contributed the largest to

  2. Plenary Keynote Presentation: Height and Intelligence: Exploring the Complexity and Controversy of Heredity

  3. 2018 BioIT AWARDS Judges’ Prize WINNER Alexion Pharmaceuticala nominated by EPAM Systems – SmartPanel: Rare DIsease Diagnostics – Algorithm Competition – automate from MDs Notes

  4. Bio-IT World Personalized & Translational Medicine WINNER @MGH NeuroBank Patient-Centric Platform Clinical Research

  5. Bio-IT World IT OINfrastructure- WINNER Celgene

  6. Bio-IT World Clinical & Health IT – WINNER TakedaPharmaceutical Nominated by Deloitte

  7. Bio-IT World Best Practices Awards – WINNER AstraZeneca, Nominated by Genedata IMED BioTech Unit

  8. Franklin Award goes to Desmond G. Higgins, PhD, Professor, Biochemistry, University College Dublin Conway Institute of Biomolecular & Biomedical Research

  9. @BenjaminFranklin and @JWBizzaro, Managing Director, .org Presented Award to @DesmondGHiggins

May 16, 2018 | BOSTON—Bio-IT World announced the 2018 winners of the Best of Show Awards Program to a packed audience at the Bio-IT World Conference & Expo. The awards program recognizes the best of the innovative product solutions for the life sciences industry on display at the Bio-IT World conference in Boston.

“It’s always a treat to explore what’s new in our industry,” said Bio-IT World Editor, Allison Proffitt. “The innovation on display by Bio-IT World exhibitors never disappoints, and we are excited to shine a spotlight on the best life sciences has to offer.”

The Best of Show program relies on a panel of expert judges from academia and industry who screen eligible new products and hear presentations from a list of finalists on site. This year our judges considered 46 new products and viewed presentations on site from 18 finalists.

The judges named winners in six categories this year: Data Integration & Management; Analysis & Data Computing; Genomic Data Services; Data Visualization & Exploration; Storage Infrastructure & Hardware; and the Judges’ Prize. Attendees also voted on the People’s Choice Award, selecting products that they believe measurably improve workflow or capacity, enabling better research.

The 2018 judging panel included Joe Cerro, BostonCIO; Chris Dwan, Bridgeplate; Richard Holland, New Forest Ventures; Eleanor Howe, Diamond Age Data Science; Phillips Kuhl, Cambridge Healthtech Institute; Steve Marshall, Marshall Data Solutions; Michael Miller, Genentech; Art Morales, Analgesic Solutions; Nanguneri Nirmala, Tufts University School of Medicine; Alexander Sherman, Massachusetts General Hospital; Subi Subramanian, Vertex Pharmaceuticals; Bill Van Etten, BioTeam; and Proffitt.

 

2018 Bio-IT World Best of Show Winners

 

People’s Choice

OnRamp BioInformatics, Inc.
ROSALIND
onramp.bio/meet-rosalind

OnRamp BioInformatics provides ROSALIND™, the first-ever genomics analysis platform specifically designed for life science researchers to analyze and interpret datasets, while freeing up more time for bioinformaticians. Named in honor of pioneering researcher Rosalind Franklin, who made a major contribution to the discovery of the double-helix structure of DNA with her famous photograph 51, OnRamp’s ROSALIND platform aims to simplify the practice of genomic data interpretation.

ROSALIND puts the researcher into the driver’s seat of data analysis and democratizes bioinformatics by broadly expanding access to genomic and proteomic technologies for cancer research, precision medicine and sustainable agriculture.

While many open-source tools remain the lifeline of genomic analysis, a simplified and innovative user experience for the biologist can empower them to run their own analyses, while utilizing these tools without the need for typing any command-line instructions.

ROSALIND is powered in partnership with Google Cloud and features scalable compute power and economical cloud-based storage. ROSALIND is a swarming docker-based genomic analysis solution incorporating the industry’s most trusted open-source tools and algorithms, with an angular front-end and secure RESTful API. ROSALIND is also deployable on-premise.

We believe that by empowering biologists with an intuitive and comprehensive platform to explore their data and collaborate with colleagues and bioinformaticians, we can help accelerate our industry and the widespread adoption of genomic technologies by dramatically lowering costs, taking out complexity and, ultimately, putting more focus back on what to do with results, not how to get to them.

 

Data Integration & Management

The Hyve BV
RADAR-base
radar-cns.org

Developed in the framework of the IMI RADAR-CNS project, RADAR-base is an open source platform designed to securely collect, store and share readings from wearable devices and smartphone sensors to enable remote monitoring. The RADAR-base platform consists of three major categories of components:

Data ingestion: Recognizing and registering data-sources (including smartphones and wearable devices), collecting the data via a direct Bluetooth connection or through a 3rd party API and streaming in near real time to the server (green box in the figure). Using Apache Kafka, the collected data is streamed to dedicated topics in real-time where the data is optimally schematized using Apache Avro;

Data storage and management: Consists of two centralized storage systems behind an authorized security layer. A cold-storage based on HDFS that is scalable and fault-tolerant focusing on storing large volumes of high frequency raw-data, and a hot-storage based on MongoDB storing aggregated data to provide a near real-time overview of the raw-data. (blue box in the figure);

Data sharing: Visualizing aggregated data in a live dashboard and exporting raw data for further analyses in various formats including AVRO, JSON and CSV (yellow box in the figure).

The platform is highly secured by a centralized management system of users and their authorities, participants, allowed devices and their specifications. RADAR-Base platform is distributed as Docker containers with associated scripts and configuration files to enable easy installation.

 

Analysis & Data Computing

Sinequa
Sinequa ES v10
sinequa.com

The Sinequa Cognitive Search and Analytics platform handles all structured and unstructured data sources and uses Natural Language Processing (NLP), statistical analysis and Machine Learning (ML) in order to create an enriched “Logical Data Warehouse” (LDW). This LDW is optimized for performance in delivering rapid responses to users’ information needs. Users can ask questions in their native language or ask that relevant information be “pushed” to them in a timely fashion when it emerges.

More than 180 connectors ready for use “out of the box” make the process of connecting multiple data sources fast and seamless. Company and industry-specific dictionaries and ontologies can be easily integrated, putting specific knowledge “under the hood” of the Sinequa platform, making it an intelligent partner for anyone in search of relevant subject information.

 

Genomic Data Services

Diploid
Moon 1.0
diploid.com/moon

Moon is the first software to autonomously diagnose rare diseases from WES/WGS data. By applying AI to the domain of rare disease diagnostics, Moon brings speed and scalability to the genome interpretation process.

The software only requires the patient’s gender, age of onset and his/her symptoms – in addition to the genetic data. Moon then goes from whole genome variant data (VCF) to pinpointing the causal variant in less than 5 minutes.

The software highlights one or a few variants that could explain the patient’s phenotype. For every variant, Moon displays an extensive list of annotations that it mined from the literature, allowing geneticists to easily verify decisions from the AI algorithms. Moon’s speed does not only save a lot of time and money, it also saves lives: Moon has already proven its utility in the NICU at Rady Children’s Hospital (San Diego): https://goo.gl/7TDrQD.

Unfortunately, about 50% of rare disease patients remain undiagnosed, even after whole genome sequencing and expert interpretation. Most hospitals don’t have the resources to keep analyzing negative cases even though new correlations between genes and disorders are published every day. Moon changes all this: as the software autonomously mines the literature and analyses samples, it can reanalyze older, negative cases in the background. Only when new information that might lead to a diagnosis becomes available, the assigned geneticist is notified. That way, hospitals can frequently reanalyze thousands of cases with minimal labor, providing a perspective to undiagnosed patients.

 

Data Visualization & Exploration

Nanome
NanoPro
nanome.ai/

Nanome is helping to improve the drug discovery process through intuitive virtual reality interfaces. They developed applications for experimentation, collaboration, and learning at the nano-scale leveraging leading VR hardware like the Oculus Rift and HTC Vive to create immersive virtual workspaces wherein users can visualize, design, and simulate molecules, proteins, and more. To try the next generation of tools for small molecule design and macromolecular exploration, stop by the Nanome Booth: #711.

There you’ll have the ability to:

  • Import molecular structures from a local machine or an online database such as RCSB or DrugBank.
  • – Manipulate molecular structures by literally grabbing, rotating, or enlarging the area of interest with their hands.
  • – Apply different representations to their selection of Atoms, Residues, Chains, or Proteins such as Stick, Wire, Ball & Stick, or Van der Waals.
  • – Measure distances and angles between atoms.
  • – Mutate amino acids and cycle through rotamer libraries.
  • – Design small molecules by building with any element from the periodic table.
  • – Minimize manipulated molecules to prevent clashes and provide a local energy minimum conformation.
  • – Duplicate or Split any selected area of your structure to modify or export independently.
  • – Export your molecular structures to PDB.
  • – Join a virtual reality session as a guest with or without virtual reality hardware.
  • – Present and collaborate in the same virtual environment with colleagues to demonstrate proposals or compare before and after results.

 

Storage Infrastructure & Hardware

PetaGene
PetaSuite Cloud Edition – Version 1.2
petagene.com

Launching at Bio-IT World 2018, PetaSuite Cloud Edition (CE) combines two innovations: (i) the ability for a user’s software tools and pipelines to seamlessly integrate with a wide variety of cloud platforms without modification, and (ii) significantly improved, high-performance, scalable PetaSuite genomic compression technology.

For example, users can now directly run, without modification, their custom BWA-mem, GATK, Python, Java, shell scripts, and other POSIX-based software/pipelines streaming directly to/from AWS, Google Cloud, Azure, and private cloud storage, as though they were local filestores. PetaSuite CE supports each platform’s object encryption during transfer and at rest. User applications can connect to multiple cloud platforms, buckets and regions as desired, transparently, and on demand, in user-mode, without needing to modify their pipelines, setup mounts, or have administrator privileges.

Whether running on bare-metal, in VMs, or within Docker containers, for public, private or hybrid cloud, PetaSuite CE enables organizations to unlock the power of distributed object storage seamlessly from their POSIX-compliant tools and pipelines.

PetaSuite CE is built from the ground-up for the extremely high performance streaming and random-access workloads demanded by genomics applications. The integrated, transparent PetaGene compression has been significantly improved to deliver even faster compression and greater reductions of up to 6x of both BAM and FASTQ.GZ files, enabling large costs savings in cloud storage and data transfer times. Moreover, PetaGene compression can also preserve the MD5 checksum of the original BAM or FASTQ.GZ file and not just the internal raw SAM/FASTQ data.

 

Judges’ Prize

Linguamatics
iScite 2.0
iscite.com

Linguamatics iScite, a Software-as-a-Service search application, puts the power of text analytics directly into scientists’ hands.

Award-winning Natural Language Processing
Researchers can extract and analyze relevant data to rapidly answer business-critical questions. iScite utilizes Linguamatics’ award-winning Natural Language Processing (NLP) based blend of analytical methods. By understanding the semantics and structure of text, iScite handles the variety of ways people express the same information, ensuring searches are comprehensive and accurate.

Easy to use on any device
iScite’s intuitive HTML interface includes a simple search box and auto-complete suggestions. The innovative answer-routing engine lets users answer simple or complex questions using puzzle-piece building blocks – simplifying access to powerful queries that extract concepts, relationships, numerical data such as drug dosages, mutations and more.

Get answers to questions, not just documents
Data sources include Linguamatics’ cloud-hosted content. MEDLINE, Clinical Trials.gov, FDA Drug Labels, PubMed Central, and Patent Abstracts are annotated with curated terminologies for diseases, drugs, genes and organizations. Scientists can answer questions such as:

  • What genes are involved in breast cancer?
  • What protocol designs have been used for immuno-oncology trials?
  • What are the adverse events for kinase inhibitors?

Actionable results
Results are presented in structured form, with bar chart facets for dynamic, visual results-filtering, a document viewer that highlights key terms and relationships, and relevant link-outs. Users can curate, save, and export their results.

iScite allows users across drug discovery and development to cut through the vast information landscape and discover the most valuable insights.

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THURSDAY, MAY 17 | 8:00 – 9:45 AM

8:00 Organizer Remarks

Cindy Crowninshield, RDN, LDN, HHC, Senior Conference Director/Team Lead, Cambridge Healthtech Institute

 Kanda Software8:05 Awards Program Introduction

Alex Karpovsky, CEO, Kanda Software Inc.

8:10 Benjamin Franklin Awards and Laureate Presentation
J.W. Bizzaro, Managing Director, Bioinformatics.org 

Desmond G. Higgins, PhD, Professor, Biochemistry, University College Dublin Conway Institute of Biomolecular & Biomedical Research – FOR CONTRIBUTIONS to Open Access Softward for Genome Sequences FASTP, FASTN, FASTA, BLAST – development on word processors – Time O(L to the N) – & Seq – 2406 years to complete sequencing

Progressive Alignment: 1984 – 1988, Desmond G. Higgins and SHarp, 1988,1989

BEFORE WWW

  • 1984 Nucleotide Sequences – Part I – GenBank EMBL
  • Journal of Biological Chemistry
  • Condition of Publication in Journal – submit SEQUENCES in three copies before for the Nucleotide Sequences Book
  • Michael Ashbutner – The EBI – Open Access to Genome Sequences – Hixton Hall
  • EMBL – European Bioinformatics Institute
  • Dublin Conway Institute – Mainframe computer moving to PCs
  • PCs – Clustal1 – Clustal4, 1988, Paul Sharp, Dublin
  • 1992 – Interner distribution – Clustal V 1992: EMBL Heidelberg, Rainer Fuchs Alan Bleasby
  • Clustal W, Clustal X, 1994 – 2007
  • Clustal W and Clustal 2.0, 2007
  • Top 30 papers on ISI up to 2015: Citation is a measure of ACTIVITY not of IMPORTANCE
  • 2008 Plan: Start from scratch
  • Clustal Omega – Release 4/2011
  • http://ebi.ac.uk/Tools

 

8:35 Bio-IT World Best Practices Awards
Allison Proffitt, Editorial Director, Bio-IT World

8:50 Keynote Introduction

  • Fastest Lowest Cost of WGA in Fire Cloud —
  • Genomics Analytics GATK4 of Intel
  • 5X in 1/3 the Time
  • AI in Genomics with GATK4 – GREATER Accuracy and Performance – Tensorflow

Michael McManus, Principal Solution Architect, Sales Marketing Group, Intel Corporation

9:00 Plenary Keynote Presentation: Height and Intelligence: Exploring the Complexity and Controversy of Heredity

Carl Zimmer

Award-winning Science Writer; Columnist, New York Times; Author of She Has Her Mother’s Laugh (coming May 2018)

Heredity has long been one of the foundations of society–but also the justification for some of the worst crimes in history. It has only been in the past century that scientists have begun to work out some of its molecular details. But the mysteries and controversies over heredity have proved remarkably durable. In my talk, I’ll explore the history and current research into heredity of two traits–height and intelligence. They may seem at first to be polar opposites, but it turns out they actually share some remarkable similarities.

Carl Zimmer is the author of 13 books about science. His newest book is She Has Her Mother’s Laugh: The Power, Perversions, and Potential of Heredity. His column, “Matter,” appears each week in the New York Times. Zimmer’s writing has earned a number of awards, including the 2016 Stephen Jay Gould Prize, awarded by the Society for the Study of Evolution to recognize individuals whose sustained efforts have advanced public understanding of evolutionary science. In 2017, he won an Online Journalism Award for his series of articles in which he explored his genome. A professor adjunct at Yale University, Zimmer is a familiar voice on programs such as Radiolab. He lives in Connecticut with his wife Grace and their children, Charlotte and Veronica. He is, to his knowledge, the only writer after whom a species of tapeworm has been named.

  • Galton,
  • Herditary Genius: Laws and Consequences, 1869
  • Twins: Hereditary & the Environment: Monozygotic twins vs Dizygotic twins, and adopted twins into different families
  • Laron Syndrome – very low stature
  • Growth Hormone – AIP gene
  • Joel Hirschhorn, Broad Institute
  • 3290 – 2018 700,00 people
  • Jonathan Pitcher – Polygenic to Omnigenic
  • Average Women’s Height (cm): Canada 157.6 CM [163.9 in 2018 vs Barbas 152.1 {169.2 in 2018]
  • Eugenics, Henry Goddard (advocated Sterilization) studied feable minded childrenVineland Training School, NJ
  • Binet – Intelligence Test related to Age
  • Feable mindedness related to hereditery: Emma Wolverton – Case study in Inheritance Kallikak Family
  • Das Erbe, 1935
  • Ian Deary, Univ of Edinburgh, 90s Correlation between IQ and mental processes
  • Sniekers, et al, Natural Genetics 49.7 – 52 Genes tied to Human Intelligence NYT, 05/22/2017
  • Up to 30 top SNPs contributed the largest to IQ Scores
  • Ancestry.com – Million of Gemone Profiles
  • DNALand.com
  • Lower Iodine – lower IQ, added Iodine to water then crops pregnant women increase IQ by 16 points.
  • Height – complex: DIet weight more protein, healthier pregnancy

 

 

 

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

9:45 Book Signing

She Has Her Mother’s Laugh
Carl Zimmer, Award-winning Science Writer and Columnist, New York Times

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Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

 

Reporter: Aviva Lev-Ari, PhD, RN

 

BOSTON – May 10, 2018 Vyasa Analytics, a provider of deep learning software and analytics for life sciences and healthcare organizations, today announces three pre-built deep learning analytics modules for its Cortex software at Bio-IT World Conference & Expo. Cortex enables the secure, scalable application of deep learning-based artificial intelligence (AI) analytics to enterprise data, identifying patterns, relationships and concepts across disparate data sources.

 

The new Neural Concept Recognition, Image Analytics and ChemVector analytics modules in Cortex enable life sciences organizations to quickly and easily apply deep learning analytics to large data streams of text, images and chemical structures. Like all deep learning analytical modules in Cortex’s library, these new modules allow users to ask complex questions of their data and use the answers to gain critical insights.

 

“Life sciences and healthcare organizations are using AI tools to advance research and development and deliver better patient care. Deep learning algorithms provide a set of powerful approaches that help us apply analytics more effectively and comprehensively across large scale data sources,” said Dr. Christopher Bouton, founder and CEO of Vyasa. “The idea of AI has been around for decades, but we are now experiencing a perfect storm of GPU-based computing power, deep learning algorithm advances and highly scalable data sources that enables paradigm-shifting machine learning and analytics capabilities.”

 

Vyasa will be demoing three deep learning analytics modules for Cortex at Bio-IT World 2018 in Boston from May 15 to 17, including:

 

  • Neural Concept Recognition. This module can be trained on text concepts (e.g. drugs, diseases, pathways, conditions, side effects, genes) in structured and unstructured data. Users can ask Cortex complex questions across large scale data sets, and discover unexpected relationships between concept types. Concept recognition analytics is applicable to a wide range of use cases from competitive intelligence, to drug repurposing and EHR analytics.

 

  • Life Sciences R&D Specialized Image Analytics. Deep learning enables novel, powerful forms of image analytics, capable of being trained to detect patterns and objects in large scale image data sources. With just a few clicks in Cortex, the user can connect large streams of image data and apply analytics to those sources. Vyasa has finely-tuned this analysis for life sciences images, and it is ideal for cell assay screening, drug manufacturing and post-market screening for counterfeit packaging and tablets.
  • ChemVector de novo Compound Design. This proprietary Cortex module applies deep learning to chemical structures. Users can drag and drop one or more SDF files containing SMILES strings into Cortex, and Cortex can identify and generate novel compounds that optimize critical variables such as log-p, molecular weight and synthetic viability. ChemVector can be used with a range of other chemistry-specific analytical modules also available in Cortex.

 

 

Dr. Bouton, Vyasa’s founder and CEO, received his BA in Neuroscience (Magna Cum Laude) from Amherst College in 1996 and his Ph.D. in Molecular Neurobiology from Johns Hopkins University in 2001. Previously Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.

 

Visit Vyasa and demo Cortex at booth #632, and watch the explainer video at www.vyasa.com.

About Vyasa Analytics

Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations. Cortex is Vyasa’s secure, highly scalable software platform for collaborative knowledge discovery and data analytics. Using Vyasa’s proprietary Neural Concept Recognition technology, Cortex identifies trends and patterns across disparate data sources, empowering project teams to gain insights and drive better decision making. Learn more at www.vyasa.com.

 

 

Angela Zmyslinski
Account Executive
azmyslinski@matternow.com
Office – 401-330-2800

     

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From: Angela Zmyslinski <azmyslinski@matternow.com>

Date: Thursday, May 10, 2018 at 2:39 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: Demo deep learning software for life sciences at Bio-IT World 2018

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Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

Linguamatics Introduces Breakthrough Scientific Search Solution

iScite provides end-users with direct access to powerful AI-driven insights from text

Boston — May 9, 2018 — Linguamatics, the leading NLP-based text analytics provider for biomedical applications, today announced the launch of Linguamatics iScite, a breakthrough innovation in scientific search that puts the precision and power of Linguamatics artificial intelligence (AI) technology directly into the hands of scientists, researchers and other knowledge workers. iScite offers a modern, easy-to-use scientific search engine that provides intuitive access to AI-powered searches across key biomedical data sources and delivers insightful answers to search questions.

iScite is designed as a next-generation search experience that empowers non-technical users to conduct their own NLP-based scientific searches to extract data insights. Rather than rely on time- and/or resource-crunched technical experts to create and perform searches, iScite enables users to quickly and independently find precise answers to their high-value questions.

“Traditional search methods are often time-consuming, expensive and ineffective, and the results are imprecise and difficult to sift through,” said Jane Reed, head of life science strategy for Linguamatics. “With iScite, users can take advantage of the power of NLP without the traditional complexities. Our patent-pending Answer-Routing Engine interprets users’ search terms and guides them to the best possible answers to their questions. Searches are seamless across multiple content sources, and users are quickly pointed to the exact content relevant to their search without having to laboriously read through every word of the source documents.”

iScite uses Linguamatics’ award-winning technology stack to handle the nuances of language and the variety of ways people express the same information, ensuring searches are comprehensive and accurate. Using advanced NLP relationship and pattern matching, iScite rapidly guides users directly to the relevant insights extracted from cloud-hosted scientific content. Results are presented in a structured, semantically-meaningful way, with options for dynamic filtering and faceting, and multiple collaboration features to allow easier sharing of insights with co-workers and key stakeholders. Behind the scenes Linguamatics uses a powerful blend of NLP and machine learning-based methods to achieve the best precision and recall.

“By empowering end-user scientists and clinicians with an easy-to-use search engine, we are speeding their access to the right knowledge for decision-making to advance the discovery, development and delivery of therapeutics,” said Linguamatics Executive Chairman John Brimacombe. “iScite has the potential to revolutionize the search process for the biomedical industry by providing everyone with rapid access to the knowledge they need, while freeing data scientists and informaticians to focus on the most challenging, in-depth search projects. iScite is a breakthrough in scientific research, filling an industry demand for a self-service alternative that delivers deep insights in a single search.”

Linguamatics will demonstrate iScite at Bio-IT World 2018 in Boston May 15-17. Visit us at booth #549, or go to our website, http://www.linguamatics.com/iscite, for more information.

 

About Linguamatics
Linguamatics
 transforms unstructured big data into big insights to advance human health and wellbeing. A world leader in deploying innovative text analytics for high-value knowledge discovery and decision support, Linguamatics’ solutions are used by top commercial, academic and government organizations, including 18 of the top 20 global pharmaceutical companies, the US Food and Drug Administration (FDA) and leading US healthcare organizations.

Linguamatics Media contact:
Michelle Ronan Noteboom, Sr. Account Director
Amendola Communications
+ 1 512.426.2870
mnoteboom@acmarketingpr.com

 

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From: Chad Van Alstin <cvanalstin@acmarketingpr.com>

Date: Thursday, May 10, 2018 at 11:30 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: Big News from NLP-Leader Linguamatics at Bio IT World – Can I arrange a meeting?

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Synopsis Track 7: NGS in Real Time @pharma_BI 2018 CHI’s BioIT World conference & Expo, May 15 – 17, 2018, Boston, MA – Seaport World Trade Center

http://www.bio-itworldexpo.com/

LPBI Group will cover Track 7: NGS in Real Time

@pharma_BI

@AVIVA1950

Aviva Lev-Ari, PhD, RN will be in attendance

 

 

 

 

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2018 Plenary Keynote Speakers

Mark BoguskiMark Boguski, MD, PhD
Executive Vice President and Chief Medical Officer, Liberty BioSecurity
tanya cashTanya Cashorali
Founder, TCB Analytics 
John ReyndersJohn Reynders, PhD
Vice President, Data Sciences, Genomics, and Bioinformatics, Alexion Pharmaceuticals, Inc.

 

Jerald SchindlerJerald Schindler, DrPH
Vice President, Biostatistics, Merck Research Laboratories (Retired)
Yu LihuaLihua Yu, PhD
Chief Data Science Officer, H3 Biomedicine

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TUESDAY, MAY 15

7:00 am Workshop Registration Open and Morning Coffee

 

8:00 – 11:30 Recommended Morning Pre-Conference Workshops*

W4. Introduction to Scalable and Reproducible RNA-Seq Data Processing, Analysis, and Result Reporting Using AWS, R, knitr, and LaTex

12:30 – 4:00 pm Recommended Afternoon Pre-Conference Workshops*

W13. Leveraging Cloud Technologies to Enable Large-Scale Integration of Human Genome and Clinical Outcomes Data

* Separate registration required.

2:00 – 6:30 Main Conference Registration Open

4:00 PLENARY KEYNOTE SESSION

Click here for detailed information

5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

WEDNESDAY, MAY 16

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION

Click here for detailed information

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

LARGE-SCALE RNA-SEQ AND GENE EXPRESSION VARIABILITY

10:50 Chairperson’s Remarks

11:00 KEYNOTE PRESENTATION: RNA-Seq X: Look Back and Look Ahead

Shanrong Zhao, PhD, Director, Computational Biology and Bioinformatics, Pfizer, Inc.

Since Dr. Mortazavi published his groundbreaking research entitled “Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq” in Nature Methods in 2008, RNA-seq has evolved rapidly and revolutionized biological research, drug development and clinical diagnostics. 2018 is the 10-year anniversary of RNA-seq, and it’s the right time to look back and look forward.

11:30 LCA: A Robust and Scalable Algorithm to Reveal Subtle Diversity in Large-Scale Single-Cell RNA Sequencing Data

Xiang Chen, PhD, Assistant Member, Department of Computational Biology, St. Jude Children’s Research Hospital

We developed Latent Cellular Analysis (LCA), a machine learning based single-cell RNA sequencing (scRNA-seq) analytical pipeline that combines similarity measurement by latent cellular states and a graph based clustering algorithm featuring dual-space model search for both the optimal number of subpopulations and the informative cellular states distinguishing them. LCA has proved to be robust, accurate and powerful by comparison to multiple state-of-the-art computational methods on large-scale real and simulated scRNA-seq data.

12:00 pm Presentation to be Announced

 

12:15 RSEQREP: An Open-Source Cloud-Enabled Framework for Reproducible RNA-Seq Data Processing, Analysis & Result Reporting

Johannes Goll, Director, Bioinformatics, The Emmes Corporation

RSEQREP (RNA-Seq Reports) is a new open-source cloud-enabled framework that allows researchers to execute start-to-end RNA-Seq analysis to characterize transcriptomics changes in human cells following treatment. It outputs dynamically generated reports using R and LaTeX. We provide results for a published RNA-Seq study to characterize transcriptomics changes following influenza vaccination.

12:30 Session Break

WuXi_Nextcode_notagline12:40 Luncheon Presentation I: Querying of 100k Genomes Using Google Cloud

Hákon Gudbjartsson, PhD, Chief Informatics Officer, WuXi NextCODE

Hákon Gudbjartsson will demonstrate the power of the GOR database in real time. GORdb is used to organize, mine and share massive genome datasets, providing a global architecture for the largest precision medicine efforts worldwide. It’s designed to enable fast, computationally-efficient use of sequence data, and allows for the query and application of data in the context of reference sets.

1:10 Luncheon Presentation II (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:40 Session Break

OPTIMIZING GENE BASES WITH CODON USAGE

1:50 Chairperson’s Remarks

Leonard Lipovich, PhD, Associate Professor with Tenure, Center for Molecular Medicine and Genetics, Wayne State University

1:55 Analysis of Codon Optimized Therapeutic Proteins Using Ribosome Profiling

Chava Kimchi-Sarfaty, PhD, Research Chemist, Principal Investigator, OTAT Acting Deputy Associate Director for Research, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, FDA | CBER | OTAT

Codon optimization is a genetic engineering technique used to improve the yield of recombinant therapeutic proteins. Despite being used ubiquitously to increase protein expression, codon optimization requires widespread substitution of synonymous codons across the native expression sequence. This degree of genetic manipulation can carry consequences, including altered conformation of the recombinant product. These unforeseen modifications can have impacts on protein function and health outcomes, and are of high regulatory importance. To study these techniques, we have used ribosome profiling, a technique used to characterize the translation pattern of the ribosome across the mRNA transcript. In this technique, actively translating ribosomes are cross‐linked to mRNA and is followed by nuclease digestion of mRNA not protected by a ribosome, generating short mRNA fragments (called “ribosome footprints”). These fragments are sequenced and aligned to generate a differential coverage map across portions of the transcript. This technique provides insight into the relative translation efficiency in a given area of the transcript. We have analyzed the ribosome profiling data for relationships to codon usage. By identifying regions of differential ribosome profiling patterns between wild type and codon optimized transcripts, we aim to create a method of selecting regions to leave unmodified, allowing recombinant proteins to benefit from increased expression while maintaining the integrity and safety of the protein product. Codon optimization as a technique relies heavily on accurate codon usage statistics of the organism in question, to identify rare codons to be replaced with common codons for an increase in translation efficiency. However, previous databases containing this information were either outdated or limited in scope. To address this gap in knowledge, we constructed a new database containing codon usage tables for all the species in GenBank and RefSeq. We designed a program in Python to download, parse, and organize all the sequence data available in these two repositories, and in Javascript designed an accessible web portal available to the public to query the new database. The new HIVE‐CUTs database contains substantially more organisms and coding sequence data and is a dramatic improvement upon prior databases. This tool will aid in the effective implementation of codon optimization techniques and other areas of recombinant protein design.

  1. FDA approved 2011-2017 – Therapeutic Proteins by Drug Class: i.e., Oncology , Hematology
  2. Effort on Gene therapy is primarily in Cancer Disease
  3. Factor 9 and Hemophillia B
  4. Codon Optimization to increase Protein expression TECHNIQUES:
  5. Transcription
  6. mRNA
  7. Codon Usage Database – Tables open to the public -9606 (Homo Sapient)
  8. Evaluation of Codon Optimized Factor 9 – Altered Protein Structure Binding Affinity, protein folding
  9. Riboson profile: Protected Profile 20-22 or 24-27 nucleotides [PCR Sequence] Sites: A, P, E
  10. Analysis of Ribosome Profiling Data: Correlations F9 and ACTB and GAPDH – each against Codon Optimizer
  11. In Silico: translation kinetics based solely on calculated codon usage frequency
  12. CONCLUSION:
  • Ribosomal profiling data do not correlate with codon Optimization
  • Genetically engineered therapeutics – benefit from Codon Optimization

 

 

2:25 Multidimensional Global Proteogenomics Identifies Persistent Ribosomal In-Frame Mis-Translation of Stop Codons as Amino Acids in Multiple Open Reading Frames from a Human Breast Cancer Long Non-Coding RNA

Leonard Lipovich, PhD, Associate Professor with Tenure, Center for Molecular Medicine and Genetics, Wayne State University

Two-thirds of the ~60,000 human genes (www.gencodegenes.org) do not encode known proteins, and aside from long non-coding RNA (lncRNA) genes with recently characterized functions, the possibility that these poorly understood genes’ transcripts serve as de-facto unconventional messenger RNAs has not been formally excluded. Our group was the first to use direct evidence from protein mass spectrometry, preceding efforts that employed indirect evidence from ribosome profiling, to demonstrate that specific lncRNAs are recurrently and nonrandomly translated in human cells (Bánfai et al 2012, Genome Research 22:1646-1657). In our current study, we integrated RNAseq, ribosome profiling, and mass spectrometry to globally assess lncRNA translation in human estrogen receptor alpha positive MCF7 breast cancer cells. We identified 27 peptides, mapping to multiple sense-strand open reading frames (ORFs) of the lncRNA gene MMP24-AS1, united by a novel and highly unconventional property: the existence of these peptides can only be explained by stop-to-nonstop in-frame replacements of specific UAG and UGA (but not UAA) stop codons by amino acids. This result, validated by the absence of any genomic mutations, polymorphisms, and RNA editing events in genomic and cDNA targeted resequencing, represents an unprecedented apparent gene-specific violation of the Genetic Code in human breast cancer cells, and hints at a new mechanism enhancing the combinatorial complexity of the cancer proteome.
[Note 1: This work has been funded in its entirety by the NIH Director’s New Innovator Award 1DP2-CA196375 to LL.]
[Note 2: This project encompasses collaborations. A full listing of co-authors will be shown during the talk.]

  • LncRNA
  • ENCODE –
  • WG 6-frame tRanslation + mass spectrometric data {mass specriptom] = empirical redefinition of the genomic sequencing field
  • MMP24 maps – Breast Cancer
  • Mechanism: MisTranslation – Translation Infidelity: why are only UAG and UGA, never UAA, reference-genome stop codons are affected

 

2:55 CO-PRESENTATION: Workflow Optimization for NGS Discovery – How to Drive BIX Insights

Jack DiGiovanna, PhD, General Manager, NGS Applications and Services, Seven Bridges Genomics (->2009) 250 CS

Isaac M. Neuhaus, PhD, Director, Computational Genomics, Bristol Myers Squibb

  • Predict immuno-oncoogy outcomes
  • Biomarkers
  • Microsatellite Instability (MSI) – short tanden repeats of 1 to 6 base-pairs: Detections of MSI mutations in somatic variants, Profiling
  • Whole Exome gene data
  • Companion diagnostics
  • Colorectal adenocarcinomas with MSI status
  • Validating predictions
  • Tumor Heterogeneity: Clones have different potentials to metastasize
  • Heterogeneity (purity) work flow & Validation – Variant Allele Frequency
  • MSI sensor score: Benchmarking MSIsensor vs Tumor Purity
  • Clinical MSI data

 

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

NGS DATA ANALYSIS, INTEGRATION, INTERPRETATION, AND VISUALIZATION

4:00 Variant Query Tool: Drag & Drop for a Scalable, Server-Less, Web UI to Querying Annotated Variants

William Van Etten, Senior Scientific Consultant, BioTeam

It’s a challenge to build an environment that provides real-time querying of reads and annotated variants for genomics research, requiring significant human and computational resources. Whether tens or thousands of genomes, the barrier to entry can be high for the biologists/geneticist, who might not also be computer scientist. BioTeam has developed a simple tool that leverages several AWS services (S3, Athena, Lambda, Cognito, IAM, CloudWatch) to enable a biologists/geneticist to drag & drop VCF and BAM files onto an S3 bucket, then point their web browser at this bucket, to provide a scalable, server-less, web UI to querying the reads and annotated variants within these files. We aim to demonstrate, explain, and promote what we’ve learned from this proof of concept software development in the hope that others might benefit from our experience.

http://vqt.bioteam.net

  • Amazon Athena API Introduced – Variant Query Tool – Server-less

 

 4:30 Building a GXP Validated Platform for NGS Analysis Pipelines

Anthony Rowe, PhD, Business Technology Leader, R&D IT, Janssen R&D LLC

An NGS applications approach the clinic the bioinformatics pipelines used to analyze the data have to be validated to demonstrate their correctness. This talk will present Janssen approach to deploying validated NGS applications with specific focus in microbiome metagnomics.

  • IBD: UlcerativeColitis  (UC), Crohn’s DIsease (CD)
  • $11Bil – $28Billion – Cost burden in Health Care systems
  • J&J and Vedanta announced a collaboration DNAnexus 
  • Microbiome based approach for IBD: Gut Dysbiosis, beneficial microbes symbionts, Pathogenesis
  • Jeanssen developing a new platform for DIsease therapy
  • How to analyze microbiome data as drug?
  • Stelara Biotherapeutics – 27 microbes
  • PK PD of antigen therapy
  • Biotherapeutic product like VE202 –
  • Can VE202 be detected in stool
  • Real Time NGS – The Clinical Novel NSG  for Clinical Trials – Emerging Science meets Regulated Science
  • Emerging Science: Novel NGS Informatics
  • Tool Box for Microbiome: Biomathematicians carried workflow to Clinical Trials
  • Computational workflow: Step 1: alienment 1,2,3,4
  • 7 samples 4 tools: Run Time cost result quality
  • Quality Control: for Clinical NGS Platform:Manufacturing, Software QA,
  • current system Overview: sequencing Vendors prtnerships
  • Establish scientific ladscape and a structured drug development process

Sapio Sciences

 

5:00 LIMS or ELN, Which Do You Need? [Electronic Lab Notebook]

Kevin Cramer, CEO, Sapio Sciences (->2007)

Both Biotech and Pharma need Laboratory Information Management (LIMS) and Electronic Lab Notebook (ELN) capabilities. Sapio has eliminated the barriers between these two product areas by leveraging its more than decade of unique experience offering both LIMS and ELN solutions and combining the key features of each solution into one, best of breed, product: Exemplar ELN Pro.

  • Configurable data model for LIMS Platform
  • LIMS 1.0 Configure Data Model
  • LIMS 2.0 Workflow enginw for tracking complex processes
  • NGS – 2008 Celexa,
  • ELN – 2015 Exemplar support ad Hoc experimentation, clean sheet design
  1. create RQS for Samples
  2. Assign processes
  3. Track progres
  4. register Samples
  5. Register plates Aliquoting Define Storage
  6. Graphical Assignment
  7. register consumables
  • ELN: Spreadsheets, Office Integration, Drag & Drop experiment items, Curve fitting, R Stat, Charting/visualization
  • Author/witness/Reviewer/Approver Accept/reject attach instrument design
  • ELN, LIMS, ELN Pro[fessional]
  • ELN Pro: ELN plus LIMS properties
  • Exemplar ELN Pro: storage mgm

Sapio Sciences – Exemplar ELN Pro

  • Integrate collaboration,charting tools
  • global repository
  • Chemaxon Integration
  • Prebuilt NGS pipelines out of the box

 

 

5:15 Sponsored Presentation (Opportunity Available)

5:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing

7:00 – 10:00 Bio-IT World After Hours @Lawn on D

THURSDAY, MAY 17

7:30 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM

Click here for detailed information

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

 

APPLICATION OF NGS TO ONCOLOGY, IMMUNOLOGY, DIAGNOSTICS, AND THERAPEUTIC DEVELOPMENT

10:30 Chairperson’s Remarks, Bruce Press, EVP Seven Bridges Genomics

10:40 Instantiating a Single Point of Truth for Genomic Reference Data

David Herzig, Scientist, Research Informatics, Roche Pharmaceuticals

This talk will exemplify how expression and mutation data were made actionable by consolidating a scattered landscape of genomic reference data into a real SPoT.

  • Common System Landscapes
  1. Data Sources
  2. Silo Solutions
  3. API
  4. Consumers: Bioinformatics, Data Science, JBrowser
  • Single Source of Truth
  1. Moving away from Silo solutions
  2. Roche Data Commons

Physical HW

File SYstem & Workflow

Single Point of Truth SSOT

Integration Data Mart

UI

All data goes into Data Storage API as input and Output from the data storage

Evaluation

  • inhouse development
  • customization of open source
  • Ensembl
  1. Genomics Reference Data manu species
  2. Multi species DB [stable ID] MySQL DB is been used
  3. API & SW: REST, Tools, Web Code
  4. Modules – Variation, Funcgen, other features CORE – genome annotation
  5. SOLUTION – 150 hours = Homo-sapiens Variation is the most time consuming Ensebl REST API Endpoints
  6. Customization: NCBI: DOwnload, Unzip data – Import data – Ensembl PERL Script goes into SPoT (SSoT)
  7. Into CORE  – only species of interestLoading Log – UPDATE META TABLE (Roche Data from BioInformatics Dept)
  8. SSOT & Arvados – 2 updates a year, 5 versions are available in parallel: Portal Page, latest version: http://genomes.roce.com:3091 http://genomes.roche.com/latest 
  9. USE CASES: Comparative Genomics

 

11:10 A Network-Based Approach to Understanding Drug Toxicity

Yue Webster, PhD, Principal Research Scientist, Informatics Capabilities, Research IT, Eli Lilly and Company

Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome. Compared to gene-level analysis alone, the network approach identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.

  • Phase III Clinical Trials fail due to Drug Toxicity – TXG – MAP
  • Food preservative BHA
  • antibiotic TB patient Trecator – like Tunicamycin
  • blood thinner – Ticlid
  • n-dimensional problem space for Toxicity:Gene Expression COmplexity DYnamic COmplexity, pathophysiology complexity
  • TRANSLATION: from Animal to Human Clinical Trials – Failure of Clinical Trial equals to failure of the TRANSLATION
  • Modules in DNA and RNA
  • Protein structure
  • Reduce dimensionality of the information space
  • Changes of patterns – risk assessment of the confidence in translation
  • Gene vs System-level View – Tunicamycin – image TXG – MAP = unsupervised approach to convert a table of data into ONE image
  • How to build TXG – MAP: Genotype and Phenotype – for Predictions of Untargeted Effects, recomendations
  1. Data Input
  2. Training set – DrugMatrix
  3. Algorithm – 415 co-expression module
  4. Interpretation (Gene ontology)
  • use TXG – MAP for adaptive response: Measure changes in Biological processes usinf eigengene scores
  • TXG – MAP to compare Treatments: Apoptosis post treatment with Tunicamycin Red Induced expression Green supressed expression
  • Hypetrophy caused by antibiotic Tunicamycin
  • Building TXG – MAP – for sharing with Scientific Community across species to be used in Translation Research for preservation across cell lines, across species and for translation to Humans

11:40 Michael Rusch , Dir Bioinformatics, St. Jude Cloud

  • 2017 Genomic Test is ORDERABLE >400 patients as of May 2018
  • 300 approved access requests globally – PCGP Data Sharing – 8 attempts to download have failed once
  • Solution: 2015 Cloud: Secure, sustainable, expandable
  • SW development Partner DNAnexus on Microsoft ADURE in 2017, St.Jude CLoud
  • 3000 pediatric cancer survivors – Optimize therapy to improve quality of life
  • Simple Data Access Procedure : data securely into private cloud
  • Gene fusions – Turnaround Time Challenge – Assay – 42 days – Leukemia RNA Seq workflow 15 days to get seq done
  • Cost $5-$10 per sample running 5-8 days seq, manual Review and Reporting 20 Minutes
  • most runs completed in 12 hours
  • Variant annotation, pathogenicity: Germ line Mutations and Pediatric Cancer NEJM, Journal Pediatric Oncology
  • Recan PIE – Pathogenicity Information Exchange (PIE) for SNV/Indel Classification
  • Results overview Variant page: Gene info Protein Paint, Gene ingo
  • damage prediction algorithm – ACMG classification Tool: Variant page
  • 100 Registered users
  • 425 jobs
  • 340,000 variants
  • VisualizationProtein paint, PCGP Mutation: SOmatic and Germ line Pathogenic and Likely Pathogenic Variants

https://stjude.cloud

 

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing

DATA MINING FOR DISEASE CLASSIFICATION – CITYVIEW I

1:55 Chairperson’s Remarks

John Methot, Director, Health Informatics Architecture, Dana-Farber Cancer Institute

2:00 Disease Classification in the Era of Data-Intensive Medicine

Kanix Wang, PhD, Research Professional, Booth School of Business, Institute for Genomics & Systems Biology, University of Chicago

We used insurance claims for over one-third of the U.S. population to create a subset of 128,989 families (481,657 unique individuals). Using these data, we estimated the heritability and familial environmental patterns of 149 diseases. We then computed the environmental and genetic disease classifications for a set of 29 complex diseases after inferring their pairwise genetic and environmental correlations.

2:30 Enviro-Geno-Pheno State Approach and State-Based Biomarkers for Differentiation, Prognosis, Subtypes, and Staging

Lei Xu, PhD, Director, Centre for Cognitive Machines and Computational Health; Zhiyuan Chair Professor, Department of Computer Science and Engineering, Shanghai Jiao Tong University

In the joint space of geno-measures, pheno-measures, and enviro-measures, one point represents a bio-system behavior and a subset of points that locate adjacently and share a common system status represents a ‘state’. The system is characterized by such states learned from samples. This enviro-geno-pheno state is considered a biomarker, indicating ‘health/normal’ versus ‘risk/abnormal’ together with its associated enviro-geno-pheno condition.

3:00 PANEL DISCUSSION: Can We Improve Breast Cancer Patient Outcomes through Artificial Intelligence?

Maya Said, ScD, President & CEO, Outcomes4me, Inc. (Moderator)

Panelists:
Regina Barzilay, PhD, MacArthur Fellow and Delta Electronics Professor, Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science; Member, Computer Science and Artificial Intelligence Laboratory, MIT

Kevin Hughes, MD, Co-Director, Avon Breast Evaluation Program, Massachusetts General Hospital; Associate Professor of Surgery, Harvard Medical School; Medical Director, Bermuda Cancer Genetics Risk Assessment Clinic

Newly diagnosed cancer patients attempting to understand their treatment options face the overwhelming task of filtering an information deluge, much of which is irrelevant, outdated and occasionally inaccurate. Additionally, matching their diagnosis to best-in-class treatments or potential clinical trials, while simultaneously learning to navigate an extremely complex healthcare system is daunting, even for the most highly trained physicians. We will explore various platforms aimed at improving patient outcomes by leveraging technology to help educate, track, and connect patients with personalized resources while simultaneously working to improve the care continuum and the development of new treatments. We will explore the nexus of healthcare networks and their IT systems, clinical decision-making and delivery, R&D, and patients, for whom we all create our innovation solutions. Attendees will be interested to understand how various groups are working to increase value across the entire system by bringing laboratory, clinical and pharmaceutical science, real-world evidence and patient-reported data together with technology and artificial intelligence to solve health challenges. These approaches offer the opportunity to generate deeper insights into how therapies perform in the real world and harness that understanding to improve efficiency, effectiveness, value, and ultimately, patient care.

  • Targeted Therapy in Breast Cancer more than another diseases

4:00 Conference Adjourns

SOURCE

http://www.bio-itworldexpo.com/next-gen-sequencing-informatics/

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International Award for Human Genome Project

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

The Thai royal family awarded its annual prizes in Bangkok, Thailand, in late January 2018 in recognition of advances in public health and medicine – through the Prince Mahidol Award Foundation under the Royal Patronage. This foundation was established in 1992 to honor the late Prince Mahidol of Songkla, the Royal Father of His Majesty King Bhumibol Adulyadej of Thailand and the Royal Grandfather of the present King. Prince Mahidol is celebrated worldwide as the father of modern medicine and public health in Thailand.

 

The Human Genome Project has been awarded the 2017 Prince Mahidol Award for revolutionary advances in the field of medicine. The Human Genome Project was completed in 2003. It was an international, collaborative research program aimed at the complete mapping and sequencing of the human genome. Its final goal was to provide researchers with fundamental information about the human genome and powerful tools for understanding the genetic factors in human disease, paving the way for new strategies for disease diagnosis, treatment and prevention.

 

The resulting human genome sequence has provided a foundation on which researchers and clinicians now tackle increasingly complex problems, transforming the study of human biology and disease. Particularly it is satisfying that it has given the researchers the ability to begin using genomics to improve approaches for diagnosing and treating human disease thereby beginning the era of genomic medicine.

 

National Human Genome Research Institute (NHGRI) is devoted to advancing health through genome research. The institute led National Institutes of Health’s (NIH’s) contribution to the Human Genome Project, which was successfully completed in 2003 ahead of schedule and under budget. NIH, is USA’s national medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases.

 

Building on the foundation laid by the sequencing of the human genome, NHGRI’s work now encompasses a broad range of research aimed at expanding understanding of human biology and improving human health. In addition, a critical part of NHGRI’s mission continues to be the study of the ethical, legal and social implications of genome research.

 

References:

 

https://www.nih.gov/news-events/news-releases/human-genome-project-awarded-thai-2017-prince-mahidol-award-field-medicine

 

http://www.mfa.go.th/main/en/news3/6886/83875-Announcement-of-the-Prince-Mahidol-Laureates-2017.html

 

http://www.thaiembassy.org/london/en/news/7519/83884-Announcement-of-the-Prince-Mahidol-Laureates-2017.html

 

http://englishnews.thaipbs.or.th/us-human-genome-project-influenza-researchers-win-prince-mahidol-award-2017/

 

http://genomesequencing.com/the-human-genome-project-is-awarded-the-thai-2017-prince-mahidol-award-for-the-field-of-medicine-national-institutes-of-health-press-release/

 

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Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers

Reporter: Aviva Lev-Ari, PhD, RN

 

Cracking the Genome

SOURCE
Paperback
, 352 pages
ISBN:

9780801871405
October 2002
$29.00
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Cracking the Genome

Inside the Race to Unlock Human DNA

In 1953, James Watson and Francis Crick unveiled the double helix structure of DNA. The discovery was a profound moment in the history of science, but solving the structure of the genetic material did not reveal what the human genome sequence actually was, or what it says about who we are. Cracking the code of life would take another half a century.

In 2001, two rival teams of scientists shared the acclaim for sequencing the human genome. Kevin Davies, founding editor of Nature Genetics, has relentlessly followed the story as it unfolded week by week since the dawn of the Human Genome Project in 1990. Here, in rich human and scientific detail, is the compelling story of one of the greatest scientific feats ever accomplished: the sequencing of the human genome.

In brilliant, accessible prose, Davies captures the drama of this momentous achievement, drawing on his own genetics expertise and on interviews with the key scientists. Davies details the fraught rivalry between the public consortium, chaperoned by Francis Collins, and Celera Genomics, directed by sequencer J. Craig Venter. And in this newly updated edition, Davies sheds light on the secrets of the sequence, highlighting the myriad ways in which genomics will impact human health for the generations to come.

Cracking the Genome is the definitive, balanced account of how the code that holds the answer to the origin of life, the evolution of humanity, and the future of medicine was finally broken.

Kevin Davies is the founding editor of Nature Genetics and is currently editor-in-chief of Bio•IT World. He graduated from Oxford University and holds a Ph.D. in genetics from the University of London.

“For an up-to-the-minute account of one of the most dramatic periods in present-day science, Cracking the Genome is an essential read.”

“A superb job… A tantalizing glimpse of the ethical perils and technological possibilities awaiting humanity.”

“A rollicking good tale about an enduring intellectual monument.”

“The race is over, and Davies was there, all along, providing the running commentary—and there, too, at the finish line. In Cracking the Genome, he hands out the prizes.”

“Davies has tracked one of the most important stories ever to unfold. Davies helps readers understand how the deciphering of our genetic code will revolutionize our lives while posing serious ethical dilemmas.”

“An impressive job of contextualizing the science within a political, economic, and social framework, creating a lively tale as accessible to non—specialists as it is to scientists.”

“Investors and others looking for a quick primer on the science and business of biotechnology will find this a useful guide.”

“In Davies’ prose, this story of molecular biology and the Human Genome Project is as compelling as any Arthurian legend. In a fast-moving approachable style, Davies captures the uncovering of biology’s Holy Grail, relying on his own expertise in genetics and interviews with key players such as Collins and Venter.”

SOURCE

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