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Archive for the ‘BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics’ Category


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

 

A mutated gene called RAS gives rise to a signalling protein Ral which is involved in tumour growth in the bladder. Many researchers tried and failed to target and stop this wayward gene. Signalling proteins such as Ral usually shift between active and inactive states.

 

So, researchers next tried to stop Ral to get into active state. In inacvtive state Ral exposes a pocket which gets closed when active. After five years, the researchers found a small molecule dubbed BQU57 that can wedge itself into the pocket to prevent Ral from closing and becoming active. Now, BQU57 has been licensed for further development.

 

Researchers have a growing genetic data on bladder cancer, some of which threaten to overturn the supposed causes of bladder cancer. Genetics has also allowed bladder cancer to be reclassified from two categories into five distinct subtypes, each with different characteristics and weak spots. All these advances bode well for drug development and for improved diagnosis and prognosis.

 

Among the groups studying the genetics of bladder cancer are two large international teams: Uromol (named for urology and molecular biology), which is based at Aarhus University Hospital in Denmark, and The Cancer Genome Atlas (TCGA), based at institutions in Texas and Boston. Each team tackled a different type of cancer, based on the traditional classification of whether or not a tumour has grown into the muscle wall of the bladder. Uromol worked on the more common, earlier form, non-muscle-invasive bladder cancer, whereas TCGA is looking at muscle-invasive bladder cancer, which has a lower survival rate.

 

The Uromol team sought to identify people whose non-invasive tumours might return after treatment, becoming invasive or even metastatic. Bladder cancer has a high risk of recurrence, so people whose non-invasive cancer has been treated need to be monitored for many years, undergoing cystoscopy every few months. They looked for predictive genetic footprints in the transcriptome of the cancer, which contains all of a cell’s RNA and can tell researchers which genes are turned on or off.

 

They found three subgroups with distinct basal and luminal features, as proposed by other groups, each with different clinical outcomes in early-stage bladder cancer. These features sort bladder cancer into genetic categories that can help predict whether the cancer will return. The researchers also identified mutations that are linked to tumour progression. Mutations in the so-called APOBEC genes, which code for enzymes that modify RNA or DNA molecules. This effect could lead to cancer and cause it to be aggressive.

 

The second major research group, TCGA, led by the National Cancer Institute and the National Human Genome Research Institute, that involves thousands of researchers across USA. The project has already mapped genomic changes in 33 cancer types, including breast, skin and lung cancers. The TCGA researchers, who study muscle-invasive bladder cancer, have looked at tumours that were already identified as fast-growing and invasive.

 

The work by Uromol, TCGA and other labs has provided a clearer view of the genetic landscape of early- and late-stage bladder cancer. There are five subtypes for the muscle-invasive form: luminal, luminal–papillary, luminal–infiltrated, basal–squamous, and neuronal, each of which is genetically distinct and might require different therapeutic approaches.

 

Bladder cancer has the third-highest mutation rate of any cancer, behind only lung cancer and melanoma. The TCGA team has confirmed Uromol research showing that most bladder-cancer mutations occur in the APOBEC genes. It is not yet clear why APOBEC mutations are so common in bladder cancer, but studies of the mutations have yielded one startling implication. The APOBEC enzyme causes mutations early during the development of bladder cancer, and independent of cigarette smoke or other known exposures.

 

The TCGA researchers found a subset of bladder-cancer patients, those with the greatest number of APOBEC mutations, had an extremely high five-year survival rate of about 75%. Other patients with fewer APOBEC mutations fared less well which is pretty surprising.

 

This detailed knowledge of bladder-cancer genetics may help to pinpoint the specific vulnerabilities of cancer cells in different people. Over the past decade, Broad Institute researchers have identified more than 760 genes that cancer needs to grow and survive. Their genetic map might take another ten years to finish, but it will list every genetic vulnerability that can be exploited. The goal of cancer precision medicine is to take the patient’s tumour and decode the genetics, so the clinician can make a decision based on that information.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/29117162

 

https://www.ncbi.nlm.nih.gov/pubmed/27321955

 

https://www.ncbi.nlm.nih.gov/pubmed/28583312

 

https://www.ncbi.nlm.nih.gov/pubmed/24476821

 

https://www.ncbi.nlm.nih.gov/pubmed/28988769

 

https://www.ncbi.nlm.nih.gov/pubmed/28753430

 

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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

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October 2002
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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.”

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SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Reporter: Aviva Lev-Ari, PhD, RN

Genes Affirm: High BMI Carries Weighty Heart, Diabetes Risk – Mendelian randomization study adds to ‘burgeoning evidence’

by Crystal Phend, Senior Associate Editor, MedPage Today, July 05, 2017

 

The “genetically instrumented” measure of high BMI exposure — calculated based on 93 single-nucleotide polymorphisms associated with BMI in prior genome-wide association studies — was associated with the following risks (odds ratios given per standard deviation higher BMI):

  • Hypertension (OR 1.64, 95% CI 1.48-1.83)
  • Coronary heart disease (CHD; OR 1.35, 95% CI 1.09-1.69)
  • Type 2 diabetes (OR 2.53, 95% CI 2.04-3.13)
  • Systolic blood pressure (β 1.65 mm Hg, 95% CI 0.78-2.52 mm Hg)
  • Diastolic blood pressure (β 1.37 mm Hg, 95% CI 0.88-1.85 mm Hg)

However, there were no associations with stroke, Donald Lyall, PhD, of the University of Glasgow, and colleagues reported online in JAMA Cardiology.

The associations independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history were found in baseline data from 119,859 participants in the population-based U.K. Biobank who had complete medical, sociodemographic, and genetic data.

“The main advantage of an MR approach is that certain types of study bias can be minimized,” the team noted. “Because DNA is stable and randomly inherited, which helps to mitigate errors from reverse causality and confounding, genetic variation can be used as a proxy for lifetime BMI to overcome limitations such as reverse causality and confounding, a process that hampers observational analyses of obesity and its consequences.”

 

Other related articles published in this Open Access Online Scientific Journal include the following:

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    Genomics Orientations for Personalized Medicine (Frontiers in Genomics Research Book 1)

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    Regenerative and Translational Medicine: The Therapeutic Promise for Cardiovascular Diseases

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    Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation: The Art of Scientific & Medical Curation

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Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

 

This article presents Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single molecule DNA sequencing

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN

2017bioit-bit-mini-logo

 

 bioinformatics

http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=140955

  #BioIT17

TUESDAY, MAY 23

7:00 am Workshop Registration and Morning Coffee

8:0011:30 Recommended Morning Pre-Conference Workshops*

(W4) Data Visualization to Accelerate Biological Discovery

12:304:00 pm Recommended Afternoon Pre-Conference Workshops*

(W13) Proteogenomics: Integration of Genomics and Proteomics Data

* Separate registration required.

2:006:00 Main Conference Registration Open

4:00 PLENARY KEYNOTE SESSION

Click here for detailed information

5:007:00 Welcome Reception in the Exhibit Hall with Poster Viewing

WEDNESDAY, MAY 24

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION

Click here for detailed information

9:50 Coffee Break in the Exhibit Hall with Poster Viewing

APPLICATIONS & SOLUTIONS FOR DATA SHARING AND DECISION MAKING

10:50 Chairperson’s Remarks

Kevin Merlo, BioSafety Development Engineer, Dassault Systemes

11:00 Innovative Data Integration Applicable for Therapeutic Protein Development 2.0

Wolfgang Paul, Group Leader and Senior Scientist, Large Molecule Research, Roche

Therapeutic proteins are registered including sequence, structural and functional data and information. Millions of data points are captured during the development of Roche’s innovative therapeutic proteins in data warehouse used by DAMAS (data acquisition, management and analyses system). Fast access and visualization of relevant process and analytical data drive scientific discussion and decision making. Analyzing the stored big data is key towards process development of therapeutic proteins 2.0.

11:30 Informatics – A Silver Bullet for Pharmaceutical Sciences?

William Loging, Ph.D., Associate Professor of Genomics & Head, Production Bioinformatics, Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai

The Pharmaceutical Sciences field is in constant search for the next big innovative push that will increase the success rate of drug programs. The fields of computational chemistry, structural bioinformatics – just to name a few – have changed the way drug researchers look for and identify novel drug candidates. Utilizing more than 15 years of Pharmaceutical experience, and using real world examples of high provide drug projects, this talk will provide practical steps for the merger of informatics and the strategic approaches needed for drug discovery success.

12:00 pm Big Data-Driven Bioinformatics

Frank Lee, Ph.D., Healthcare Life Sciences Industry Leader, Software Defined Infrastructure, IBM Systems, IBM

IBM will discuss the IBM Reference Architecture for Genomics, its new features, and case studies: hybrid cloud with integrated workload and data management for high performance genomics analytics; container technologies for migrating and sharing application and data; and application portal and metadata engine for global access to and searching of distributed resources. A demo of a hybrid cloud-based bioinformatics solution will follow.

12:30 Session Break

12:40 Luncheon Presentation I to be Announced

1:10 Luncheon Presentation II to be Announced

1:40 Session Break

STANDARDS FOR CHEMICAL STRUCTURES

1:50 Chairperson’s Remarks

1:55 PANEL DISCUSSION: Linking and Finding Information Using the IUPAC InChI Standard for Chemical Structures

Steve Heller, Ph.D., Project Director, InChI Trust; Scientific Information Consultant (Moderator)

Evan Bolton, Ph.D., Lead Scientist, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), and National Institutes of Health (NIH)

Keith T. Taylor, BSc, Ph.D., MRSC, Principal, Ladera Consultancy

Tyler Peryea, Informatics Scientist, National Center for Advancing Translational Sciences (NCATS)

Lawrence Callahan, Ph.D., Chemist, Substance Registration System, Office of Critical Path Programs, Food and Drug Administration (FDA)

This session will highlight on-going efforts to strengthen and expand the non-proprietary IUPAC International Chemical Identifier (InChI) standard for chemical structures and its hashed-form, the InChIKey. Information standards are critical to enable effective communication of scientific content. Funding to maintain InChI comes from most major publishers and database providers as well as governmental agencies (NIH, FDA and NIST). The InChI is an open-source, widely adopted standard found in most chemical information containing databases, including those from Chemical Abstracts, Reaxys, ChEMBL, OpenPHACTS, PubChem, DrugBank, PDB, Sigma-Aldrich, and many others, such as internal Pharma corporate databases. InChI is an addition to a database, not a replacement. With the implementation of the ISO identification of medicinal products (IDMP) and the related ISO 11238 standards, adding and having an InChI will allow for an easier, effective, and more complete search for information on a particular drug.

2:55 Sponsored Presentation (Opportunity Available)

3:10 Integrated Informatics for Biologics Discovery

Robert Brown, Ph.D., Vice President, Product Marketing, Dotmatics

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

MACHINE LEARNING TECHNIQUES AND APPLICATIONS TO PERFORM BIG DATA ANALYTICS ON –OMICS DATA

4:00 Building Disease Networks Using Text Mining and Machine Learning Techniques

Kamal Rawal, Ph.D., Assistant Professor, Biotech and Bioinformatics, Jaypee Institute of Information Technology

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. Using text mining & deep curation strategies combined with omics data, we have explained the therapeutics and side effects of several drugs (i.e., orlistat) at network level.

4:20 Big Data and Systems Biology: From Genome to Phenome (and Everything in Between)

Dan Jacobson, Ph.D., Computational Biologist, Oak Ridge National Laboratory

4:40 Novel Feature Selection Strategies for Enhanced Predictive Modeling and Deep Learning in the Biosciences

Tom Chittenden, Ph.D., D.Phil., Lecturer and Senior Biostatistics and Mathematical Biology Consultant, Harvard Medical School

We have built a robust AI approach that precisely assesses pathogenicity for all genomic missense variants. Coupled with our advanced deepCODE mathematical statistics feature selection strategy for constructing deep learning models, we are able to quantitatively integrate a priori pathway-based biological knowledge with multiple types of high-throughput omics data.

5:00 Network Analysis for Drug Discovery: Benchmarking Results and Best Practices Reported by CBDD Consortium

Marina Bessarabova, Ph.D., Senior Director, Discovery and Translational Science, Life Sciences Professional Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters)

A large number of advanced approaches to network analysis of -omics data were developed by academia groups in the past 15 years. Adoption of these approaches in drug development requires thorough review of the published approaches, implementation of methods identified as potentially applicable to drug development and benchmarking of the methods with an aim to establish best practices for application of the methods to diseases and mechanism of action understanding, target identification, drug repositioning, patient stratification, biomarker discovery, and drug combination effect prediction. CBDD (Computational Biology Methods for Drug Discovery) is a precompetitive consortium between Novartis, Pfizer, Sanofi, Janssen, Regeneron, UCB, Roche, Takeda, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Merck and Clarivate Analytics (formally Thomson Reuters) focused on adoption of network analysis approaches in drug development: literature review, method implementation and benchmarking. Benchmarking results and best practices for application of network analysis in drug development established by members of the program will be shared during the presentation.

5:30 15th Anniversary Celebration in the Exhibit Hall with Poster Viewing and Best of Show Awards

THURSDAY, MAY 25

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM

8:05 Benjamin Franklin Awards and Laureate Presentation

8:35 Best Practices Awards Program

8:50 Plenary Keynote

Click here for detailed information

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

DATA COMPUTING AND BIOINFORMATICS IN AGRO CHEMICALS AND BIOTECHNOLOGY: CHALLENGES AND OPPORTUNITIES

10:30 Chairperson’s Remarks

Bino John, Ph.D., Computational Biology Group Leader, Dow AgroSciences LLC

10:40 How Biotech and Big Data Are Changing Agro Industry

Bino John, Ph.D., Computational Biology Group Leader, Dow AgroSciences LLC

More than 70% of the increase in food production in the next 50 years is expected to come from technological advances. Indeed, recent advances in genomics and phenomics are beginning to transform the Agro-industry, whereby creating new opportunities for informatics disciplines. While informatics needs in managing, analyzing, and visualizing big data share commonalties between Agro and the biomedical communities, Agro companies face unprecedented challenges in big biological data, generally larger than their peers in the biomedical community.

11:00 Offering Outcomes: How Digital Farming Data Is Enabling New Business Models

Tobias Menne, Global Head of Digital Farming, Bayer

11:20 Building the Next-Generation R&D IT Infrastructure for Small Molecule Discovery

Paimun Amini, Chemistry IT Lead, R&D IT, Monsanto Company

Barrett Foat, Ph.D., Data Science Team Lead, Agricultural Productivity Innovations, Monsanto

The Pharma boom in the 90s & 2000s led to the emergence of a rich ecosystem of software companies focused on delivering the IT needs for small molecule discovery. Today, cloud data storage, IoT, and the growth of predictive analytics present new opportunities for the evolution of the R&D pipeline. New technologies allow for integrated software and hardware solutions that optimize productivity while removing the risk of technical debt.

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

LOOKING BEYOND THE GENOME OF THE PATIENT: DATA, ANALYSIS AND TOOLS TO IMPROVE BETTER DISEASE UNDERSTANDING FOR CURRENT TREATMENTS AND DRUG DEVELOPMENT

1:55 Chairperson’s Remarks

Michael N. Liebman, Ph.D., Managing Director, IPQ Analytics, LLC and Strategic Medicine, Inc.

2:00 Distinguishing between Precision Medicine and Accurate Medicine: Application to Heart Failure Patients and Clinical Practice

Michael N. Liebman, Ph.D., IPQ Analytics, LLC and Strategic Medicine, Inc.

Increasingly, patient stratification based on genomic analysis is being considered in disease management. Critically, the need to understand real world medical practice and real world patient complexities extends far beyond the genome of the patient. We have shown examples of this complexity in heart disease and how this impacts development of clinical guidelines, trial design, and development of new patient management approaches.

2:30 CARPEDIEM – Comorbidity and Risk Profiles Evaluation in Diabetes and Heart Morbidities

Sabrina Molinaro, Psy.D., Ph.D., Head, Department of Epidemiology and Health Services, Institute of Clinical Physiology, National Research Council of Italy

Our project uniquely develops a patient record that includes clinical and individual factors (EHR-driven phenotyping) that will be validated through the comparison of existing standards for building new risk algorithms. An understanding of the current limitations and biases of risk profiling in heart disease and diabetes and how an extended, integrated database and automatic rule-based classification system can be used to improve patient management.

3:00 PANEL DISCUSSION: Precision Medicine vs. Accurate Medicine: The Need to Understand Real World Medicine and Real World Patients

Michael N. Liebman, Ph.D., IPQ Analytics, LLC and Strategic Medicine, Inc. (Moderator)

Charles Barr, M.D., MPH, Group Medical Director and Head, Evidence Science and Innovation, Genentech

Hal Wolf, Director, National Leader of Information and Digital Health Strategy, The Chartis Group

4:00 Conference Adjourns

SOURCE

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

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