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Bioinformatics Tool Review: Genome Variant Analysis Tools

Curator: Stephen J. Williams, Ph.D.

Updated 11/15/2018

The following post will be an ongoing curation of reviews of gene variant bioinformatic software.

 

The Ensembl Variant Effect Predictor.

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F.

Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.

Author information

1

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.

2

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

3

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.

Abstract

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

 

Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015).  However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here).  To this effect major sequencing projects have been initiated worldwide including:

A more thorough curation of sequencing projects can be seen in the following post:

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

 

And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein.  Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.

To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.

Summary of Features

  • Annotation: VEP can annotate two broad categories of genomic variants
    • Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
    • Larger structural variants > 50 nucleotides
  • Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
  • Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
  • Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
  • Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation.  Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
  • Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants.  Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation.  Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
  • Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats.  Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
  • Choice of user interface
    • Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
    • VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub.  The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
    • VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.

 

 

Watch Video on VES Instructional Webinar: https://youtu.be/7Fs7MHfXjWk

Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP

 

 

Availability of data and materials

The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.

 

References

Large-scale discovery of novel genetic causes of developmental disorders.

Deciphering Developmental Disorders Study.

Nature2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962

Updated 11/15/2018

 

Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools

Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:

  • For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
  • As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
  • For rare diseases, pathogenic allele frequencies are generally low

Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.

In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene.  These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007).  It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).

 

References

Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.

Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.

Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.

Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.

 

Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants.

Andersen LL1Terczyńska-Dyla E1Mørk N2Scavenius C1Enghild JJ1Höning K3Hornung V3,4Christiansen M5,6Mogensen TH2,6Hartmann R7.

 

Abstract

We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.

Methodology

  1. Identification of rare variants
  2. Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
  3. Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
  4. In-silico predictions: Variants classified by the in silico variant pathogenicity prediction programs: SIFT, Mutation Assessor, FATHMM, PROVEAN, SNAP2, PolyPhen2, PhD-SNP, SNP&GO, FATHMM-MKL, MutationTaster2, PredictSNP, Condel, MetaSNP, and CADD. Each program returned prediction scores measuring likelihood of a variant either being ‘deleterious’ or ‘neutral’. Prediction accuracy measured as

ACC = (true positive+true negative)/(true positive+true negative+false positive+false negative)

 

  1. Validation of prediction software/tools

In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function.  Western blot was performed to confirm the expression of the constructs.

 

Results

Table 2 Summary of the
bioinformatic predictions
HSE variants IFNLR1 variants Overall ACC
TN TP FN FP Total ACC TN TP FN FP Total ACC
Uniform cutoff
SIFT 4 1 0 4 9 0.56 8 1 0 7 16 0.56 0.56
Mutation assessor 6 1 0 2 9 0.78 9 1 0 6 16 0.63 0.68
FATHMM 7 1 0 1 9 0.89 0.89
PROVEAN 8 1 0 0 9 1.00 11 1 0 4 16 0.75 0.84
SNAP2 5 1 0 3 9 0.67 8 0 1 7 16 0.50 0.56
PolyPhen2 6 1 0 2 9 0.78 12 1 0 3 16 0.81 0.80
PhD-SNP 7 1 0 1 9 0.89 11 1 0 4 16 0.75 0.80
SNPs&GO 8 1 0 0 9 1.00 14 1 0 1 16 0.94 0.96
FATHMM MKL 4 1 0 4 9 0.56 13 0 1 2 16 0.81 0.72
MutationTaster2 4 0 1 4 9 0.44 14 0 1 1 16 0.88 0.72
PredictSNP 6 1 0 2 9 0.78 11 1 0 4 16 0.75 0.76
Condel 6 1 0 2 9 0.78 0.78
Meta-SNP 8 1 0 0 9 1.00 11 1 0 4 16 0.75 0.84
CADD 2 1 0 6 9 0.33 8 0 1 7 16 0.50 0.44
MSC 95% cutoff
SIFT 5 1 0 3 9 0.67 8 1 0 8 16 0.50 0.56
PolyPhen2 6 1 0 2 9 0.78 13 1 0 3 16 0.81 0.80
CADD 4 1 0 4 9 0.56 7 0 1 9 16 0.44 0.48

 

Note: TN: true negative, TP: true positive, FN: false negative, FP: false positive, ACC: accuracy

Functional testing (data obtained from reporter construct experiments) were considered as the correct outcome.

Three prediction tools (PROVEAN, SNP&GO, and MetaSNP correctly predicted the effect of all nine variants tested.

 

Other articles related to Genomics and Bioinformatics on this online Open Access Journal Include:

Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

 

Large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes

 

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

 

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

 

 

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5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

Please follow on Twitter using the following #hash tags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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Live Conference Coverage Medcity Converge 2018 Philadelphia: Clinical Trials and Mega Health Mergers

Reporter: Stephen J. Williams, PhD

1:30 – 2:15 PM Clinical Trials 2.0

The randomized, controlled clinical trial is the gold standard, but it may be time for a new model. How can patient networks and new technology be leveraged to boost clinical trial recruitment and manage clinical trials more efficiently?

Moderator: John Reites, Chief Product Officer, Thread @johnreites
Speakers:
Andrew Chapman M.D., Chief of Cancer Services , Sidney Kimmel Cancer Center, Thomas Jefferson University Hospital
Michelle Longmire, M.D., Founder, Medable @LongmireMD
Sameek Roychowdhury MD, PhD, Medical Oncologist and Researcher, Ohio State University Comprehensive Cancer Center @OSUCCC_James

 

Michele: Medable is creating a digital surrogate biomarker for short term end result for cardiology clinical trials as well as creating a virtual site clinical trial design (independent of geography)

Sameek:  OSU is developing RNASeq tests for oncogenic fusions that are actionable

John: ability to use various technologies to conduct telehealth and tele-trials.  So why are we talking about Clinical Trials 2.0?

Andrew: We are not meeting many patients needs.  The provider also have a workload that prevents from the efficient running of a clinical trial.

Michele:  Personalized medicine: what is the framework how we conduct clinical trials in this new paradigm?

Sameek: How do we find those rare patients outside of a health network?  A fragmented health system is hurting patient recruitment efforts.

Wout: The Christmas Tree paradigm: collecting data points based on previous studies may lead to unnecessary criteria for patient recruitment

Sameek:  OSU has a cancer network (Orion) that has 95% success rate of recruitment.  Over Orion network sequencing performed at $10,000 per patient, cost reimbursed through network.  Network helps pharma companies find patients and patients to find drugs

Wout: reaching out to different stakeholders

John: what he sees in 2.0 is use of tech.  They took 12 clinic business but they integrated these sites and was able to benefit patient experience… this helped in recruitment into trials.  Now after a patient is recruited, how 2.0 model works?

Sameek:  since we work with pharma companies, what if we bring in patients from all over the US.  how do we continue to take care of them?

Andrew: utilizing a technology is critically important for tele-health to work and for tele-clinical trials to work

Michele:  the utilization of tele-health by patients is rather low.

Wout:  We are looking for insights into the data.  So we are concentrated on collecting the data and not decision trees.

John: What is a barrier to driving Clinical Trial 2.0?

Andrew: The complexity is a barrier to the patient.  Need to show the simplicity of this.  Need to match trials within a system.

Saleem: Data sharing incentives might not be there or the value not recognized by all players.  And it is hard to figure out how to share the data in the most efficient way.

Wout: Key issue when think locally and act globally but healthcare is the inverse of this as there are so many stakeholders but that adoption by all stakeholders take time

Michele: accessibility of healthcare data by patients is revolutionary.  The medical training in US does not train doctors in communicating a value of a trial

John: we are in a value-driven economy.  You have to give alot to get something in this economy. Final comments?

Saleem: we need fundamental research on the validity of clinical trials 2.0.

Wout:  Use tools to mine manually but don’t do everything manually, not underlying tasks

Andrew: Show value to patient

2:20-3:00 PM CONVERGEnce on Steroids: Why Comcast and Independence Blue Cross?

This year has seen a great deal of convergence in health care.  One of the most innovative collaborations announced was that of Cable and Media giant Comcast Corporation and health plan Independence Blue Cross.  This fireside chat will explore what the joint venture is all about, the backstory of how this unlikely partnership came to be, and what it might mean for our industry.

sponsored by Independence Blue Cross @IBX 

Moderator: Tom Olenzak, Managing Director Strategic Innovation Portfolio, Independence Blue Cross @IBX
Speakers:
Marc Siry, VP, Strategic Development, Comcast
Michael Vennera, SVP, Chief Information Officer, Independence Blue Cross

Comcast and Independence Blue Cross Blue Shield are teaming together to form an independent health firm to bring various players in healthcare onto a platform to give people a clear path to manage their healthcare.  Its not just about a payer and information system but an ecosystem within Philadelphia and over the nation.

Michael:  About 2015 at a health innovation conference they came together to produce a demo on how they envision the future of healthcare.

Marc: When we think of a customer we think of the household. So we thought about aggregating services to people in health.  How do people interact with their healthcare system?

What are the risks for bringing this vision to reality?

Michael: Key to experience is how to connect consumer to caregiver.

How do we aggregate the data, and present it in a way to consumer where it is actionable?

How do we help the patient to know where to go next?

Marc: Concept of ubiquity, not just the app, nor asking the provider to ask patient to download the app and use it but use our platform to expand it over all forms of media. They did a study with an insurer with metabolic syndrome and people’s viewing habits.  So when you can combine the expertise of IBX and the scale of a Comcast platform you can provide great amount of usable data.

Michael: Analytics will be a prime importance of the venture.

Tom:  We look at lots of companies that try to pitch technologies but they dont understand healthcare is a human problem not a tech problem.  What have you learned?

Marc: Adoption rate of new tech by doctors is very low as they are very busy.  Understanding the clinicians workflow is important and how to not disrupt their workflow was humbling for us.

Michael:  The speed at which big tech companies can integrate and innovate new technologies is very rapid, something we did not understand.  We want to get this off the ground locally but want to take this solution national and globally.

Marc:  We are not in competition with local startups but we are looking to work with them to build scale and operability so startups need to show how they can scale up.  This joint venture is designed to look at these ideas.  However this will take a while before we open up the ecosystem until we can see how they would add value. There are also challenges with small companies working with large organizations.

 

Please follow on Twitter using the following #hashtags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

 

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Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address

Reporter: Stephen J. Williams, PhD

8:30 AM -9:15

Practical Applications of AI in Cancer

We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.

Moderator: Ayan Bhattacharya, Advanced Analytics Specialist Leader, Deloitte Consulting LLP
Speakers:
Wout Brusselaers, CEO and Co-Founder, Deep 6 AI @woutbrusselaers ‏
Tufia Haddad, M.D., Chair of Breast Medical Oncology and Department of Oncology Chair of IT, Mayo Clinic
Carla Leibowitz, Head of Corporate Development, Arterys @carlaleibowitz
John Quackenbush, Ph.D., Professor and Director of the Center for Cancer Computational Biology, Dana-Farber Cancer Institute

Ayan: working at IBM and Thompon Rueters with structured datasets and having gone through his own cancer battle, he is now working in healthcare AI which has an unstructured dataset(s)

Carla: collecting medical images over the world, mainly tumor and calculating tumor volumetrics

Tufia: drug resistant breast cancer clinician but interested in AI and healthcareIT at Mayo

John: taking large scale datasets but a machine learning skeptic

moderator: how has imaging evolved?

Carla: ten times images but not ten times radiologists so stressed field needs help with image analysis; they have seen measuring lung tumor volumetrics as a therapeutic diagnostic has worked

moderator: how has AI affected patient recruitment?

Tufia: majority of patients are receiving great care but AI can offer profiles and determine which patients can benefit from tertiary care;

John: 1980 paper on no free lunch theorem; great enthusiasm about optimization algortihisms fell short in application; can extract great information from e.g. images

moderator: how is AI for healthcare delivery working at mayo?

Tufia: for every hour with patient two hours of data mining. for care delivery hope to use the systems to leverage the cognitive systems to do the data mining

John: problem with irreproducible research which makes a poor dataset:  also these care packages are based on population data not personalized datasets; challenges to AI is moving correlation to causation

Carla: algorithisms from on healthcare network is not good enough, Google tried and it failed

John: curation very important; good annotation is needed; needed to go in and develop, with curators, a systematic way to curate medial records; need standardization and reproducibility; applications in radiometrics can be different based on different data collection machines; developed a machine learning model site where investigators can compare models on a hub; also need to communicate with patients on healthcare information and quality information

Ayan: Australia and Canada has done the most concerning AI and lifescience, healthcare space; AI in most cases is cognitive learning: really two types of companies 1) the Microsofts, Googles, and 2) the startups that may be more pure AI

 

Final Notes: We are at a point where collecting massive amounts of healthcare related data is simple, rapid, and shareable.  However challenges exist in quality of datasets, proper curation and annotation, need for collaboration across all healthcare stakeholders including patients, and dissemination of useful and accurate information

 

9:15 AM–9:45 AM

Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System

The Promise and Hype of Immunotherapy

Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.

Speaker:
Joshua Brody, M.D., Assistant Professor, Mount Sinai School of Medicine @joshuabrodyMD

Director Lymphoma therapy at Mt. Sinai

  • lymphoma a cancer with high PD-L1 expression
  • hodgkin’s lymphoma best responder to PD1 therapy (nivolumab) but hepatic adverse effects
  • CAR-T (chimeric BCR and TCR); a long process which includes apheresis, selection CD3/CD28 cells; viral transfection of the chimeric; purification
  • complete remissions of B cell lymphomas (NCI trial) and long term remissions past 18 months
  • side effects like cytokine release (has been controlled); encephalopathy (he uses a hand writing test to see progression of adverse effect)

Vaccines

  •  teaching the immune cells as PD1 inhibition exhausting T cells so a vaccine boost could be an adjuvant to PD1 or checkpoint therapy
  • using Flt3L primed in-situ vaccine (using a Toll like receptor agonist can recruit the dendritic cells to the tumor and then activation of T cell response);  therefore vaccine does not need to be produced ex vivo; months after the vaccine the tumor still in remission
  • versus rituximab, which can target many healthy B cells this in-situ vaccine strategy is very specific for the tumorigenic B cells
  • HoWEVER they did see resistant tumor cells which did not overexpress PD-L1 but they did discover a novel checkpoint (cannot be disclosed at this point)

 

 

 

 

 

 

 

 

 

Please follow on Twitter using the following #hashtags and @pharma_BI

#MCConverge

#AI

#cancertreatment

#immunotherapy

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

Read Full Post »


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

     

SOURCE

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

 

SOURCE

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 Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

Curator: Aviva Lev-Ari, PhD, RN

 

Synopsis Day 1: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/23/synopsis-day-1-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

 

Synopsis Day 2: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/24/https-pharmaceuticalintelligence-wordpress-com-p47489previewtruesynopsis-day-2-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachus/

 

Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/25/synopsis-day-3-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

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