e-Proceedings 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 Boston
https://bio-itworld.pathable.co/meetings/virtual/3T3SuWw9J2Bceei9s
Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN
Tweets & Retweets by @pharma_BI and @AVIVA1950 at #BioIT20, 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston
Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN
October 6, 2020
PLENARY KEYNOTE –
10:15 am ET – NIH’s Strategic Vision for Data Science
-
Susan Gregurick
NIH
Associate Director for Data Science
- Connected Data Ecosystem – Project is FAIR
- Data shareable
- NIH – agenda on data: diverse sets of data: Images of MRI, cells, of organs, of communities,
- Share images and link it to tables
- METADATA 34PB enable search – moving Data to clouds for Large-Scalable Analysis
- Sequence Read Archive (SRA) – DNA seq.
- COVID-19 from around the World SRA in Cloud Partnerships enabled
- Open Science – enhance SW tools for making research cloud-ready
- NIH has 12 Centers: Genomics, Neuro-imaging
- SCH – Smart & Connected Health
- IT, Sensor system hardware, effective usability, medical interpretation, Transformative data Science
- Cancer, Alzheimer’s, Genomics, Medical Imaging, Brain circuits,
- Coding it Forward: Students come to NIH Virtually from home to join CIVIL DIGITAL FELLOWSHIP
- COVID-19: repositories of data for researches:
- Treatment for Interventions
- Long term Sequelae
- Clinical platforms: BigData Catalyst, Allow US, ADSO, National COVID Cohort
- Across platforms: workflow after RAS August Deploy: Passport for researchers to access data faster, Privacy-Preserving Tokens, Interoperability across clinical COVID data bases
- Metadata super rich to link to other new data sources is a challenging issue to solve across studies
Scott Parker
Sinequa Corp
Director of Product Marketing
- Disconnect between R&D & IT
- Intelligence search Applications for sensitive information: Sinequa is a leader
- shares one index cost for document go down & productivity increases
Rebecca Baker
NIH OD
Dir HEAL Initiative
- END ADDICTION Project – NIH HEAL Initiative: 20 NIH collaborating on Studies
- National Overdose Deaths overdose opioid drugs – synthetic Fentanyl
- Heroin, Cocaine, Methamphetamine
- During COVID Overdose increased during the pandemic
- Increase in drug use overall and 67% of Fentanyl
- Chronic Pain: Daily severe pain: can’t go to work – 25 Million
- $500 Million/year Sustained Research Investment 25+ HEAL Research Programs
- HEAL Initiative: Pain management, Translating research, New presention, enhance outcomes for affected newborns, novel medications options Pre-clinical translational research in Pain management
- Improving treatments for opioid misuse & addiction
- Opioid disorder people do not receive treatment: justice community, collaborative, ER, pregnant mothers
- Medication-based treatment – do not stay long enough to achieve long-term recovery
- People experience Pain differently: Muscular, neurological, : Biomarkers, endpoints, signatures, test non-addictive treatments for specific pains
- Pain control balance of risks of long-term opioid therapy
- HEAL Research – infant born after exposure to opioids in utero affect brain growth, born with withdrawal syndromes
- Diversity of Data under HEAL Initiative –>> Harmonize the data
- Common Data Elements in HEAL Clinical Research in Pain Management
- CORE CDE & Supplemental CDE
- Making HEAL Data FAIR: Findable, Accessible, Interpretable, Reusable
- LINK HEAL data with communities studies, predict behaviours
- Data sharing made available to the public
- HEAL Data Lifecycle
- effect of change due to change in dosage used – if dat is not collected – then we are not able to explore the relationships
- Use the data to advance research beyond the current understanding of the problem
- #NIHhealthInitiative
Ari Berman
BioTeam Inc
Chief Executive Officer
- Distributed Questions from the Audience to the speakers
10:00 AM – 11:25 AM EDT on Tuesday, October 6
How to Hold on to Your Knowledge in an Agile World
Etzard Stolte
Roche Pharma
Global Head
October 7, 2020
The Chicagoland COVID-19 Commons: A Regional Data Commons Powering Research to Support Public Health Efforts
-
Matthew Trunnell
VP & Chief Data Officer
9:00 AM – 9:20 AM EDT on Wednesday, October 7
- Seattle & COVID – samples from Seattle Flu Study
- Public Health Practice vs Research – Data from Human Subjects: Avoid delute the control
- Chicagoland COVID-19 Data Commons – in Chicago
- Neighborhood level in Chicago
- common data model
- power efforts Predictive modeling : Case rate Total confirmed cases, Death cases
- Legal agreement of the Consortium
- https://chicagoland.pandemic
- Commons – resources held in commons non-for profit
- Data Commons: cloud based SW platforms that are co-located data, computing infrastructure and applications
- Level 1: Basic, Level 2: Repeatable, Level 3: Governance Level4: Interoperability Level 5: Sustainable
- COVID-19 Data Common: Public health authorities collects data – nor available to Research community
- Research community need access to Public health authorities
- Regional COVID-19 Data Commons: Reasons: Public health decision is LOCAL but specific to the Region
- Fund raising in the communities
- Data 1: Clinical Data for Health care Summary of incidence – Signals of ethnic dependencies and co-morbidities
- Safe harbor: removal of 18 identifiers
- Expert Determination
- Data 2: Public Data: Environmental,
- Data 3: Resident-Reported Data on iPhones: multiple languages supported early reports of people feeling unwell
CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data
Richard Head
Washington Univ
Prof & Dir Genome Technology Access Ctr
9:20 AM – 9:40 AM EDT on Wednesday, October 7
- Formating, data scrubbing,
- Replace data fabric with simplified version
- create “Memory Model” Machine learning does classification of patterns
- dimensions are the variables
- “Hyper-dimensional – ingestions of abstracts and articles
- Example; IL^: Aggregate Memories to create a NORMALIZED Aggregate Memory
- Relationships explored
- Complex Knowledge Patterns Generated by the PCMM: Compared Utilization
- Augmented AI System: Combination PCMM with AI
- Literature mining CompBio
- Evidence of Utility: PCMM – Accepted or Published Research Leveraging PCMM Applications
- Example 1: Cell Metabolism CompBio – A person formulate hypothesis
- Example 2: Analysis of RNA-Seq a rare mutational subtype of GBM
- Hypothesis –>> BioExplorer –>> Multiple relations revealed
- Example 3: Animal Models to Human Disease: CompBio – Crohn’s Assertion Engine
Summary – Augmented AI Platform for Biological DIscovery
- PCMM – Memory modle – hyperdimensional
- AAI Infrastructure
- Knowledge map libraries
- In development Medical Discoveries
PercayAI Team – commercial Development
Kingdom Capital
Precision Cancer Medicine
-
Jeffrey Rosenfeld
Rutgers Univ
Asst Prof
9:40 AM – 10:00 AM EDT on Wednesday, October 7
- Cancer Classification: Shift from Anatomy/History of Molecular Etiology
- Chronic Myeloenous Leukemia – Gleevec
- Type cancer seq:
- Hereditary cancer sequencing – BRCA
- Tumor cancer sequencing
- Panel Sizes – 500-1000x – the bigger the panel – more computational time more data need be investigated
- Hotspot Panels,
- Gene Panels,
- Exomes
- Cell free DNA Testing – Liquid biopsy
- Apoptosis
- Necrosis
- FoundationONE
- Patient Results: ALL mutations found, Mutation Burden,
- Gene EGFR – no mutation
- For every Mutation what Therapy is recommended for approved drugs
- Clinical Trials for the mutations
- VARIANTS of unknown significance
- WORKFLOW: many MDs send sample get 38pps report
- Genomic Classification and Prognosis in AML: Mutations subset and therapies available
- Paradigm Shift in Classification
- 2013 – Lung Adenocarcinoma <<<- –
- 2011 – another cancer
mTOR System: A Database for Systems-Level Biomarker Discovery in Cancer
-
Iman Tavassoly – CANCELLED
C2i Genomics
Physician Scientist
10:20 AM – 10:40 AM EDT on Wednesday, October 7
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FAIR and the (Tr)end of Data Lakes
-
Kees Van Bochove
The Hyve
Founder & Owner
10:20 AM – 10:40 AM EDT on Wednesday, October 7
Normalizing Regulatory Data Using Natural Language Processing (NLP)
-
Qais Hatim, Dr.
FDA CDER
Visiting Assoc
David Milward
Linguamatics
Senior Director, NLP Technology
10:40 AM – 11:00 AM EDT on Wednesday, October 7
- ML focus on Disease
- NLP – different words have same meanings, different expression same meaning, grammer & Meaning
- Normalizes output
- Disease
- Genes
- Dates
- Mutations
- Transform Unstructured into structured
- Identifying Gaps in adverse events Labelling: Pain and Opioids
- Improve drug safety
- ChemAxon
Supplemental Approval Letters
Coding for Adverse events: “derived values of possible interest”
- Use of Prominent Terminologies used at the FDA: UNII – Translation into ANSI tesaurus standard
- Matching to the Variation found within Real Text: synonyms
- Using ML for Normalization in Disease Context
- Deep Learning PRE-TRAINING APPROACH for annotated date = supervised learning
- A set of rules to handle overlapping entities
- normalized the amp extracted from concepts
- BERN and Terminologies: BioBERN, PubMed Central, PubMed Articles
- NER – Named Entity Recognition
- Evaluation of the Approach
Conclusions
NLP, ML, Hybrid methods, Terminology +ML methods
Building an Artificial Intelligence-Based Vaccine Discovery System: Applications in Infectious Diseases & Personalized Neoantigen-Related Immunotherapy for Treatment of Cancers
-
Kamal Rawal
Amity Univ
Assoc Prof
10:40 AM – 11:00 AM EDT on Wednesday, October 7
- Classification of proteins
- Data Collection
- Feature Selection – Most important from 1447 features
- Deep learning Model: Vaxi-DL: Layers, compilation
- Overfitting Model strategy
- Balancing Imbalanced
- Hyper parameter tuning: Internal parameter of the model
- Stratified K-Fold Training and Validation
- Ensembling Approach: many weak classifier to create a STRONG Classifier
- ROC Curve: Ensemble by Consensus
- Before and after calibration
- Benchmarking the system: Vaxi-DL Ensemble by Average vs by Consensus
- SYSTEM developed: Type protein – find results
- Rare disease CHARGE Syndrome was used for validation
- Application to COVID-19 – Methodology
- Application on Cancer: Which peptide can be used as antigen for prediction of immunogenic peptides
Using GPU Computing to Evaluate Variant Calling Strategies
-
George Vacek
NVIDIA Corp
Sequencing Strategic Development
-
Eriks Sasha Paegle
Dell EMC
Senior Business Development Manager
11:15 AM – 11:30 AM EDT on Wednesday, October 7
- Navidia: 100 Genomes Cohort generated at NY Genome Center NHGRI
- Navidia Parabricks mentioned AZURE
- Dell EMC: Test environment: Dell Technology Cloud Storage for Multi-Cloud: resources across GCU, AWS, Azure in Northern Virginia regions
- Multi-Cloud ease of use: without Multi-cloud vs with Faction multi-clouds
- Ease of use
- Deep Averaging Network (DAN)
- NVIDIA CLARA PARABRICK TOOLKIT: Short & Long read, Deep learning, Data Analytics, ML
- Reference applications – host of customized applications, 3rd Party App, Libraries
- GPU (Genomics PUs) – Drop in tools for Somatic Pipelines : Clara Parabricks v3.5
- Partnership of NVIDIA and Petagene announced at BioIT20 – NGS Data compretion
- Petagene technology allows lossless compression reduce storage costs
- Project with Sanger Institute – Optimizing Muto-graph Identification
- completed run in 24 hours instead 31 days
- Parabricks is a joint project Dell/EMC and NVIDIA
PLENARY KEYNOTE: Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward
12:30 PM – 1:55 PM EDT on Wednesday, October 7
-
Allison Proffitt
BioIT World & Diagnostics World
Editorial Dir
Seth Cooper
Northeastern Univ
Asst Prof
- Foldit – Scientific discovery using video games in the domain of protein structures and folding
- Combine Human with machine
- Score based on competition among players for higher score and collaboration in groups
- Problem: Chemistry give input.
- Puzzle available for one week on the Internet, games ongoing,
- Solution analysis – continually IMPROVE the structure of Protein folding
- Foldit Tutorials offered online
- Player accomplishments: Articles by scientists ,
- development of algorithms discovery
- Electron Density fitting
- Enzyme re-design
- de novo Protein Design – named authors on a paper – scientific process
- Future Work: Coronovirus Spike protein
- Small molecule design
- narrative
- virtual reality – 3D protein structure for manipulation
- htp://Fold.it/Educator Mode
- htp://Fold.it/standalone
- http://fold.it/
- seth.cooper@gmail.com
Lee Lancashire, CIO
Cohen Veterans Bioscience – not for profit – advancing Brain health
- Biotyping and stratification
- Biomarkers
- Omics data
- All meet in the Common – Brain Commons: Clinician, Geneticist, Scientist, Bioinformatician, R Studio, Python, Jupyterhub
- Multidimensional Biomarkers in Multiple Sclerosis
Pietro Michelucci
Human Computation Institute
Director
- Why machine can’t tackle AI on their own and AI can’t do Precision Medicine on their own
- young people more than others N of 1 – Precision Mediicne
- Scandinavians and Russians are immune
- AI & Precision Medicine: can’t solve the complexity of messy data vs big data
- Messy data: heterogeneous multidimensional, to many combinations to explore, select which combination to explore vs let the machine generate all the combination and do analysis on all and discover PATTERN
- Causal vs spurious
- Logical reasoning, right brain abstract and short cuts – Human brain does routinely
- Human do better on context: Not all info is in pixels such as context
- #ADS – SBIR suspected the hypothesis to be tested
- improving crowd wisdom methods: 20 input by different people PLUS machine
- combine crowd answers with machine faster and improved accuracy
- Machine has no intuition – machine bias of Human and of machine is similar
- Wisdom of Crowd: Bootstrapping hybrid Intelligence: CIVIUM
- bit.ly/civiumintro
Jerome Waldispuehl
McGill Univ
Assoc Prof
- visualization of nucleotide – tools for
- http://phylo.cs.mcgill.ca
- GAME: Phylo DNA Puzzles: Goal 202, Score, Top Score
- Whole-genome multiple
- Phylo: 350,000 participants, 1MM solutions Improve 40 to 95% computer alignments
- education & science outreach – reach out to the Public
- Borderlands Science + game designers: 1MM participants 50MM solutions
- Joint initiative with a major science project
- Improvement of 16S rRNA
- MMOS company in Science games
Towards AI-Guided Cell Profiling of Drugs with Automated High-Content Imaging
Ola Spjuth
Uppsala Univ
Professor
2:10 PM – 2:30 PM EDT on Wednesday, October 7
- Accelerate drug discovering using AI automation in collaboration with AstraZeneca
- Closed-loop (autonomous) experimentation
- collect the best data at the minimal cost
- Active learning: query active learning model
- Exploitation [best predictions from given data] vs Exploration
- Automation in Life Science: micro-plate, stack of micro-plates
- Robot scientist: come out with hypothesis and conduct research
- high-throughput biology: Robots vs Disease
- Cell painting: Imaging with multiplexed dyes: genetic or chemical perturbations
- classify images into biological mechanisms
- combinations of toxicants
- A discovery engine: Toxicity, Efficacy, mechanisms combinations
- Automating our cell-based lab: fixed setup
- Open source lab automation suite: Github https://github.com/pharmbio/imagedb
- Dealing with large scale data [TensorFlow]
- STACKn.com – AI modeling Life cycle
- HASTE: Hierarchical analysis of Spacial and Temporal
- https://pharmb.io
Advanced Imaging and AI Technologies Providing New Image and Data Analysis Challenges and Opportunities
Richard Goodwin
AstraZeneca
Dir & Head of Imaging & AI
2:30 PM – 2:50 PM EDT on Wednesday, October 7
- Small molecule – not suitable for complex diseases
- focus on quality vs quantity
- compound for commercial value
- right safety
- Imaging supports R&D: Molecular, medical, big data and AI
- convergence of ML for decision making
- Spatial imaging: morphology
- Multiplex imaging like MRI
- Multimodal analysis: tissue data and invivo holistic understanding of drug delivery
- spacial transcriptomics proteomics: imaging platforms in R&D
- AZ invest in imaging technologies already impacting projects: AI-empowered imaging delivering subcellular resolution
- Mass Spec Imaging (MSI) – ex-vivo imaging techniques- spatial distribution of molecular
- cartography of cancer: Drug metabolite distribution – NEW understanding of disease and drug distribution in tissue
- DATA: digitization, integration, analysis, exploration
- Digital pathology and beyond – AI Image Analysis – AI outperform pathololigst and radiologists
- Data volume and dimensionality challenge and opportunity
- Data volume and dimensionality: complete image
- AZ Oncology – disease is understood for drug discovery using Imaging technology
PANEL: Framework and Approach to Unlock the Potential of Quantum Computing in Drug Discovery
-
Brian Martin
AbbVie Inc
Research Fellow & Head
Philipp Harbach
Merck KGaA
Head of In Silico Research in Germany
- chemistry and manufacturing with QC – end user in Pharmaceutical
- VC at Merck ask expert in Merck to guide investment of Merck in QC
- 50 people across Merck [three areas at Merck [Pharmaceutics, Animal Health, Diagnostics]
Celia Merzbacher
SRI Intl
Assoc Dir Quantum Economic Dev Consortium (QEDC)
- Methodology from Pistoia to be used in QC
- QC R&D developed in parallel
- Simulation of all the components is possible
John Wise
Pistoia Alliance Inc (2007)
We are a global, not-for-profit members’ organization working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration.
Consultant
- How Pharmaceutical Industry can benefit from quantum computing
- 9 of 10 big Pharma are members of the Pistoia Alliance
- IP created on specifications
Zahid Tharia
Pistoia Alliance Inc
Consultant
- Barriers to adoption of quantum computing (QC) in Pharma is training of staff and skills in the IT aspects of QC
3:10 PM – 4:00 PM EDT on Wednesday, October 7
October 8, 2020
Trends from the Trenches
Kevin Davies, PhD
CRISPR Journal
Exec VP & Exec Editor
Timothy Cutts
Wellcome Sanger Institute
Head
- Collaborations with scientists in subSahara
- pay for data analysis – ownership issues
- in UK 6 Labs for the entire countries: all send the data to Wellcome Sanger Institute for analysis
- Metadata is the problem – coordination of each of the 6 labs to send the metadata created problems
-
Cindy Crowninshield
Cambridge Healthtech Institute
Executive Event Director
Vivien Bonazzi
Deloitte Consulting LLP
Managing Dir & Chief Biomedical Data Scientist
- How organizations use bioscience data
- Data Ecosystem: Hardware and software: Cloud and other options
- Operationalize the two trends:
- Platforms: End to end solutions resulting in SILOS, systems are native: data ingestions
- Data Commons: Open arch, open source – integration and interdependence issues
- Biomedical Agencies in NIH various Organizations in the Private sector: Sharing data must be more effective
- IT, Data Science, Management – COVID – reduced barriers
- Leadership: Different voices from different people
- Data strategies & Governance not the whole but small pieces , incentives to share data
Chris Dagdigian
BioTeam Inc
Sr Dir
- 10th Anniversary to Trends from the Trenches
- IT infrastructure changes
- Research IT:
- Genomics & BioInformatics
- Image-based data acquisition and analysis: CryoEM, 3D microscopy, fMRI image analysis
- ML and AI – GPU FPGAs, neural processors: Drive in organizations: bottom up
- Chemistry & Molecular Dynamics
- Storage and exploitation of data for insights
- 2020 Hype vs Reality
- Scientific Data: managing and understanding, data movement, federated/access
- Big Data: data storage, management & governance standards vs human curated data
- IT needs guidance and decisions from Science Team
- Culture change for joint management by Science & IT: data fidelity, attribution, allocation top down
- NERSC File System quotas & Purging overviewSilos & So
- Petabytes of open access data, collaborative research resources: Data rich environments
- Data Lakes: Gen3 Data Commons
- Data hygiene:metadata is Science side vs IT
- Biased Data: Model & Data Bias
- Failed Predictions:
- Compilers matter again – not True
- CPU benchmarking is back – WRONG
- AMD vs Inter arm64 vs both
- Policy driven auto-tiering storage – wrong, USER self-service for tiering, movement and archive decision. Let researchers tier/move/archive based on Project, Experiment or Group
- Single storage namespace – Wrong: Data intensive science: scientists must do some IT jobs themselves
Kjiersten Fagnan
Lawrence Berkeley Natl Lab
CIO
- Genome Project of DOE
- Data management with other agencies
- COVID: Collaborations, breaking down barriers, small labs and big labs ALL generate data and sharing
- that collaboration is needed regardless of COVID – not happen
- If twoo big one lab can’t handle it all
- Funding and training does not support the Collaborations because next round of funding depend on individual publications – which requires silos
- Data cleaning and data management:Standards are annoying and painful – not needed for publishing the results as soon as possible – just that someone else will be able to use it
- Facebook have hundred of curators – the curation of scientific data requires same hunsrands od curators that are SCIENTISTS and Data scientists
Matthew Trunnell
Pandemic Response Commons, Seattle
VP & Chief Data Officer
- Data commons for intra- and inter-mural data sharing
- ML is needed for Data commons
- Progress in FAIRness, NIH efforts driven by Susan Gregory across NIH all centers
- Large amount of B-to-B Data sharing UBER sharing with a jurisdiction they operate
- SNOWFLAKES – new cloud technology
- COVID – plays an accelerator
- Cancer vs COVID – transfer knowledge from COVID to Cancer
9:00 AM – 10:40 AM EDT on Thursday, October 8
Q&A
- Project vs enterprise – Sequencing for internal research vs for clients’ data
- Tension in governmental agencies – no robust solutions: IT, Science, Management
- different Use cases need different infrastructure: HW & SW: Storage and data exploration
- Data Lakes: rule base, enterprising – training is an issue in organizations
- Management, Scientists, IT in enterprises – terra byte of storage, budgets issues, conversation on the limits that IT can ofer putting more burden on the Scientists for triage and quotas – business and scientific value
- New capabilities in organizations: hands on in data management tactical of data management not IT bur data engineering
- Citizen Science: privacy vs plants and microbes – no privacy issues
- Incentives need be changed for Data Citations in addition to Papers
- Curation Citations as Authorship citation
- Data sharing in Cancer: GEN3 – NCI Data Commons, Data Governance and Data Permission (Access) – NCI does work in data commons – much data outside this space
- EBI – in UK Sanger Institute has the infrastructure in one place
- Migrating Project based Data structure: that involves scientist decisions that should not be a quota (storage is full) in the IT space
- Human to Human communications vs tools for data migration
- Which Organizations get the data curation and annotation well: Subject matter from day 1 – hard to teach vs data engineering skills; TEAM as a solving is critical in Biomedical space no incentives
- BBC – Meta tagging system is outstanding
- NCAST TRANSLATOR – across organizations
- Changing incentives – MORE organizations will do that task better
- Common metadata across domains with predict uses of data in the Future – collaboration of CS to create in the science organization tagging like in BBC
TRACK 16: OPEN ACCESS AND COLLABORATIONSTRACK 15: CANCER INFORMATICSTRACK 13: GENOME INFORMATICS
Session Wrap-Up Panel Discussion
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Chris Anderson
Clinical OMICs
Editor in Chief
Ian Fore
NIH NCI
Sr Biomedical Informatics Program Mgr
- NCI – Cancer Data Commons – concierge services to organization on data services
Ravi Madduri – CVD large cohort
Univ of Chicago
Scientist
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Lara Mangravite
Sage Bionetworks
President
-
Kees Van Bochove
The Hyve
Founder & Owner
11:10 AM – 11:30 AM EDT on Thursday, October 8
BREAKOUT: Driving Scientific Discovery with Data / Digitization
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Timothy Gardner
Riffyn Inc
CEO
11:35 AM – 12:00 PM EDT on Thursday, October 8
PLENARY KEYNOTE – 12:00 PM – 1:25 PM EDT on Thursday, October 8
Robert Green
Brigham & Womens Hospital
Co-founder of Genome Medicine
Prof & Dir G2P Research
- Combining data to rapidly analyze COVID-19 Patients –
- identify BIOMARKERS for vulnerability
- Preventive Genomics – Angelina Jolly’s musectomy as a preventive clinical condition
- Patients access to own genomics data
- Population screening – to predict risks
- Genetic Testing to Consumer: Preventive Genomics: conflated genotyping/sequencing and labs/care providers
- Genetic Testing to Consumer: COST & Benefits – UNCLEAR
- diagnosis of unsuspected genetic disease
- stratification for surveillance
- which pieces of the puzzle need to be brought to bear in patient care
- Categories and Reporting criteria: Gene-Disease validity vs Variant Pathogenicity –>> Clinic
- MedSeq Project: 10MM randomized study – all genome info shared with Patient, other arm only selective genome data shared with patient: 100 patients 20% carry monogenic condition: Polygenic risk scores:
- CAD – high Cholesterol biomarker, A-FIb, DM2, 52% Women 48% Men
- No high risk error by PCP discussing and disclosing the results of the sequence
- Filtering the results: Indication -based testing vs Screening
- BabySeq Project: INFANTS sequencing to prevent disease: 11% carry a mutation in a monogenic gene for a monogenic condition -like abnormal narrowed aorta
- MDR – Monogenic Disease Risk
- MilSeq Project: US Air Force – Military active duty
- 5,8,10 – are all Polygenic studies
- Polygenic Risk Scores – High risk
- Classification need to be repeated every few years (2 years – re-sequence) due to changes in health and to efficiencies in new discovery in curated data which is improving as on-going
- Risk benefit – UTILITY – Partners Biobank Return of Genomic Results
- No interest on knowing by the Public NCCN criteria on chart review 20%
- Brigham Preventive Genomics via telemedicine – First in the country
- APC mutation after colonoscopy – obstruction diagnosed
- @robertgreen
Juergen Klenk
Deloitte Consulting LLP
Principal
- Bradykinin hypothesis for COVID-19
- liberate the data: People , Data Risk
Natalija Jovanovic
Sanofi
Chief Digital Officer
- AI in Pharma
- Vaccine preventable diseases – produce 1Billion vaccines a year
- reduction of incidence: Pertusis – 92% eradication
- manage risk profile
- Science mechanism translatable to machines
- high automated ingestible data for AI
- Digital is about people: Good data Good algorithms Good GUI
Vivien Bonazzi
Deloitte Consulting LLP
Managing Dir & Chief Biomedical Data Scientist
12:00 PM – 1:25 PM EDT on Thursday, October 8
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1. PLENARY KEYNOTE PRESENTATION |
10:15 am ET – NIH’s Strategic Vision for Data Science Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health |
2. WORKSHOPS |
11:55 am ET – W1: Data Management for Biologics: Registration and Beyond Monica Wang, PhD, Principal Technology Lead, Scientific Informatics, Takeda Sebastian Schlicker, Head, Biologics Business Operations, Genedata AG 11:55 am ET – W2: A Crash Course in AI: 0-60 in Three Peter V. Henstock, PhD, Machine Learning & AI Lead, Software Engineering & Statistics & Visualization, Pfizer Inc. 11:55 am ET – W3: Data Science Driving Better Informed Decisions Meghan Raman, Director, R&D Data Lake & Analytics, Bristol Myers Squibb Co. Nigel Greene, PhD, Director & Head Data Science & Artificial Intelligence, Drug Safety & Metabolism, AstraZeneca Pharmaceuticals 2:15 pm ET – W4: Digital Biomarkers and Wearables in Pharma R&D and Clinical Trials Danielle Bradnan, MS, Research Associate, Digital Health and Wellness, Lux Research Graham Jones, PhD, Director, Innovation, Technical Research and Development, Novartis Ariel Dowling, PhD, Director of Digital Strategy, Data Sciences Institute, Research and Development, Takeda Pharmaceuticals Brian Martin, Head of AI, R&D Information Research, Senior Principal Data Scientist, AbbVie 2:15 pm ET – W6: Dealing with Instrument Data at Scale: Challenges and Solutions Rachana Ananthakrishnan, Executive Director, Globus, University of Chicago Michael A. Cianfrocco, PhD, Assistant Professor, Department of Biological Chemistry and Research Assistant Professor, Life Sciences Institute, University of Michigan Brigitte E. Raumann, Product Manager, Globus, University of Chicago |
3. Connect with peers from across the industry during these dedicated networking times. |
9:25 am ET – Virtual Exhibit Hall Open Looking to meet fellow attendees and have meaningful conversations – just as you would at an in- person event? This is the perfect way to achieve just that. Get to know your fellow attendees by joining this interactive speed networking event. To participate, each attendee will be paired at random with another fellow attendee and given a chance to interact for 7 minutes in a private zoom room. Once the 7 minutes are up, you will move on to meet with another selected attendee. Maximize your networking at the meeting and join in. Take a minute to revitalize and join our friends from VOS Fitness for a stretch break. The professional trainer from VOS will bring you through some easy moves that will help with screen fatigue and ease your muscles after a long day of sitting at the computer. All moves can be done right at your desk and is appropriate for all fitness levels. |
4. Game On! |
Earn points by completing the activities listed on our Game tab. Some activities will only award points once, but others will award you every time you do it – so the more involved you are in the virtual event, the more points you will earn! You can start earning points one week before the event – so get ready to start sending meeting invitations, exploring our virtual expo and planning your schedule. Attendees in the top 5% of points earned when the game closes at the end of the conference will be eligible to win a gift card worth $200 USD! |
5. Take part in 1-on-1 networking with an easy-to-navigate profile search and scheduling platform. |
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10:00 AM – 11:25 AM EDT on Tuesday, October 6
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Wednesday, October 7
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TRACK 7: AI FOR DRUG DISCOVERY
The Emergence of the AI-Augmented Drug Discoverer
PRESENTATIONON DEMANDRECORDEDSESSION PASSMark Davies
BenevolentAI
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TRACK 7: AI FOR DRUG DISCOVERY
Generative Chemistry and Generative Biology for AI-Powered Drug Discovery
PRESENTATIONON DEMANDRECORDEDSESSION PASSAlex Zhavoronkov
Insilico Medicine
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TRACK 7: AI FOR DRUG DISCOVERY
Talk Title to be Announced
PRESENTATIONON DEMANDRECORDEDSESSION PASSGrace Wenjia You
EMD Serono
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TRACK 7: AI FOR DRUG DISCOVERY
Coupling AI and Network Biology to Generate Insights for Disease Understanding and Target ID
PRESENTATIONON DEMANDRECORDEDSESSION PASSAlexander Ivliev
Clarivate
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TRACK 7: AI FOR DRUG DISCOVERY
Session Wrap-Up Panel Discussion
PANELON DEMANDLIVESESSION PASS
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OLD Material
http://www.giiconference.com/chi909998/
Welcome to Bio-IT World 2020
In the spirit of open collaboration, the world’s premier bio-IT conference will bring together the community to focus on how we are using technologies and analytic approaches to solve problems, accelerate science, and drive the future of precision medicine. With a focus on AI, data science and other “data-driven” technologies that are advancing biomedical research, drug discovery and healthcare, the Bio-IT World Conference & Expo ’20 will bring together more than 3,000 participants to the Seaport World Trade Center in Boston from October 6-8, 2020.
The participants will have the chance to meet and share research/ideas with leading life sciences, pharmaceutical, clinical, healthcare, informatics and technology experts.
BROCHURE
http://www.giiconference.com/chi909998/catalog.pdf?20200122
2020 CONFERENCE PROGRAMS VIEW
TRACK 1 Data Storage and Transport VIEW
TRACK 2 Data and Metadata Management VIEW
TRACK 3 Data Science and Analytics Technologies VIEW
TRACK 4 Software Applications and Services VIEW
TRACK 5 Data Security and Compliance VIEW
TRACK 6 Cloud Computing VIEW
TRACK 7 AI for Drug Discovery VIEW
TRACK 8 Emerging AI Technologies VIEW
TRACK 9 AI: Business Value Outcomes VIEW
TRACK 10 Data Visualization Tools VIEW
TRACK 11 Bioinformatics VIEW
TRACK 12 Pharmaceutical R&D Informatics VIEW
TRACK 13 Genome Informatics VIEW
TRACK 14 Clinical Research and Translational Informatics VIEW
TRACK 15 Cancer Informatics VIEW
TRACK 16 Open Access and Collaborations
2020 Plenary Keynote Speakers
Rebecca Baker, PhD
Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health
Vivien Bonazzi, PhD
Chief Biomedical Data Scientist, Managing Director, Deloitte
Tim Cutts, PhD
Head, Scientific Computing, Wellcome Trust Sanger Institute
Chris Dagdigian
Co-Founder and Senior Director, Infrastructure, BioTeam, Inc
Kevin Davies, PhD
Executive Editor, The CRISPR Journal, Mary Ann Liebert, Inc.
Kjiersten Fagnan, PhD
Chief Informatics Officer, Data Science and Informatics Leader, DOE Joint Genome Institute, Lawrence Berkeley National Laboratory
Robert Green, MD, MPH
Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School
Susan K. Gregurick, PhD
Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health
Natalija Jovanovic, PhD
Chief Digital Officer, Sanofi Pasteur
Pietro Michelucci, PhD
Director, Human Computation Institute
Matthew Trunnell
Vice President and Chief Data Officer, Fred Hutchinson Cancer Research Center
Professionals
Exhibitors
Presentations
Conference Tracks
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