e-Proceedings 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston

e-Proceedings 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 Boston


 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

  • Susan Gregurick


    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:
  1. Treatment for Interventions
  2. Long term Sequelae
  3. Clinical platforms: BigData Catalyst, Allow US, ADSO, National COVID Cohort
  4. Across platforms: workflow after RAS August Deploy: Passport for researchers to access data faster, Privacy-Preserving Tokens, Interoperability across clinical COVID data bases
  5. 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


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
  1. Neighborhood level in Chicago
  2. common data model
  3. power efforts Predictive modeling : Case rate Total confirmed cases, Death cases
  4. Legal agreement of the Consortium
  5. 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
  1. Safe harbor: removal of 18 identifiers
  2. 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
  1. Hypothesis –>> BioExplorer –>> Multiple relations revealed
  2. 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


  1. Hereditary cancer sequencing – BRCA
  2. Tumor cancer sequencing
  • Panel Sizes – 500-1000x – the bigger the panel – more computational time more data need be investigated
  1. Hotspot Panels,
  2. Gene Panels,
  3. Exomes
  • Cell free DNA Testing – Liquid biopsy
  1. Apoptosis
  2. 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
  1. 2013 – Lung Adenocarcinoma <<<- –
  2. 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
Add to Calendar

mTOR system is a database I have designed for exploring biomarkers and systems-level data related to mTOR pathway in cancer. This database consists of different layers of molecular markers and quantitative parameters assigned to them through a current mathematical model. This database is an example of merging systems-level data with mathematical models for precision oncology.

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.


    Visiting Assoc

David Milward


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
  1. Disease
  2. Genes
  3. Dates
  4. 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


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


  • 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


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


Dir & Head of Imaging & AI

2:30 PM – 2:50 PM EDT on Wednesday, October 7

AstraZeneca is empowering its scientists to see the complexity of a disease in unprecedented detail to enable effective development and selection of new medicines. This is enabled though the use of an extensive range of cutting-edge imaging technologies that support studies into the efficacy and safety of drugs through the R&D pipeline. This presentation will introduce the range of novel in vivo and ex vivo imaging technologies employed, describe the data challenges associated with scaling up the use of molecular imaging technologies, and address the new data integration and mining challenges. Novel computational methods are required for large cohort imaging studies that involve tissue based multi-omics analysis, which integrate spatial relationships in unprecedented detail.

  • 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.


  • 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


  • 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

In 2019, major life sciences companies mobilized to form a pre-competitive, collaborative quantum computing working group (QuPharm) and delineate a framework and approach to accelerate realizing the potential of quantum acceleration in drug discovery. Learn from industry thought leaders on how to valuate and map problems into quantum algorithms, set up organizations to enable and scale quantum computing pilots and establish effective cross-industry, tech, and start-up collaborations.

Session Wrap-Up Panel Discussion

Etzard Stolte, PhD

Roche Pharma

Global Head

  • no official policy
  • 2020 it become important to be mentioned by management as a potential use in automation
  • continual updates needed – it is manual and a disillusion without a business case
  • Roche try to commodatized tools in AI as Classifiers, automation,

Samiul Hasan


Scientific Analytics and Visualization Director

  • AI is perceived as having potential to take off on its own
  • POC – demonstrate the vlaue
  • Proof of Concept – Semantic report – a story vs one off
  • demonstration of value is needed and is continuous



Bin Li

Millennium The Takeda Oncology Co

Dir Computational Biology & Translational Medicine

  • ML community at Takeda
  • Positive to have, how successful not much yet – not used much yet
  • some models are pretty good do not need improvement

Jens Hoefkens


Industry Principal Director

  • Future of AI as support to the Human intuition vs replacement of humans
  • automation like pathology classification
  • Machine and Human working together – not as maker of decisions in clinical settings
  • POC cycle prevent production conversion
  • where is the highest value for production and deploy with scale
  • AI Assisted to sift Genomics data
  • BERT term extraction from Google technology to make sense of data assist the user
  • ML
  • RPA – Robotic concept extraction – 80% accuracy needed by scientists

4:00 PM – 4:20 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


  • 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:
  1. Platforms: End to end solutions resulting in SILOS, systems are native: data ingestions
  2. 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:
  1. Genomics & BioInformatics
  2. Image-based data acquisition and analysis: CryoEM, 3D microscopy, fMRI image analysis
  3. ML and AI – GPU FPGAs, neural processors: Drive in organizations: bottom up
  4. Chemistry & Molecular Dynamics
  5. Storage and exploitation of data for insights
  6. 2020 Hype vs Reality
  7. Scientific Data: managing and understanding, data movement, federated/access
  8. Big Data: data storage, management & governance standards vs human curated data
  9. IT needs guidance and decisions from Science Team
  10. Culture change for joint management by Science & IT: data fidelity, attribution, allocation top down
  11. NERSC File System quotas & Purging overviewSilos & So
  12. Petabytes of open access data, collaborative research resources: Data rich environments
  13. Data Lakes: Gen3 Data Commons
  14. Data hygiene:metadata is Science side vs IT
  15. Biased Data: Model & Data Bias
  • Failed Predictions:
  1. Compilers matter again – not True
  2. CPU benchmarking is back – WRONG
  3. AMD vs Inter arm64 vs both
  4. 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
  5. Single storage namespace – Wrong: Data intensive science: scientists must do some IT jobs themselves

Kjiersten Fagnan

Lawrence Berkeley Natl Lab


  • 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

The “Trends from the Trenches” will celebrate its 10th Anniversary at Bio-IT! Since 2010, the “Trends from the Trenches” presentation, given by Chris Dagdigian, has been one of the most popular annual traditions on the Bio-IT Program. The intent of the talk is to deliver a candid (and occasionally blunt) assessment of the best, the worthwhile, and the most overhyped information technologies (IT) for life sciences. The presentation has helped scientists, leadership, and IT professionals understand the basic topics related to computing, storage, data transfer, networks, and cloud that are involved in supporting data-intensive science. In 2020, Chris will give the “Trends from the Trenches” presentation in its original “state-of-the-state address” followed by guest speakers giving podium talks on relevant topics. An interactive Q&A moderated discussion with the audience follows. Come prepared with your questions and commentary for this informative and lively session.


  • 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
  • Chris Anderson

    Clinical OMICs

    Editor in Chief

Ian Fore


Sr Biomedical Informatics Program Mgr

  • NCI – Cancer Data Commons – concierge services to organization on data services

Ravi Madduri – CVD large cohort

Univ of Chicago



  • Lara Mangravite

    Sage Bionetworks


  • 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

  • Timothy Gardner

    Riffyn Inc


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
  1. diagnosis of unsuspected genetic disease
  2. stratification for surveillance
  3. which pieces of the puzzle need to be brought to bear in patient care
  4. Categories and Reporting criteria: Gene-Disease validity vs Variant Pathogenicity –>> Clinic
  5. 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:
  6. CAD – high Cholesterol biomarker, A-FIb, DM2, 52% Women 48% Men
  7. No high risk error by PCP discussing and disclosing the results of the sequence
  8. Filtering the results: Indication -based testing vs Screening
  9. BabySeq Project: INFANTS sequencing to prevent disease: 11% carry a mutation in a monogenic gene for a monogenic condition -like abnormal narrowed aorta
  10. MDR – Monogenic Disease Risk
  11. MilSeq Project: US Air Force – Military active duty
  12. 5,8,10 – are all Polygenic studies
  13. Polygenic Risk Scores – High risk
  14. 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


  • Bradykinin hypothesis for COVID-19
  • liberate the data: People , Data Risk


Natalija Jovanovic


Chief Digital Officer

  • AI in Pharma
  • Vaccine preventable diseases – produce 1Billion vaccines a year
  1. reduction of incidence: Pertusis – 92% eradication
  • manage risk profile
  • Science mechanism translatable to machines
  1. high automated ingestible data for AI
  2. 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
Add to Calendar

12:00 Organizer’s Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute

12:05 Keynote Introduction

Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP

12:15 Toward Preventive Genomics: Lessons from MedSeq and BabySeq

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

12:40 AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur

1:05 LIVE Q&A: Session Wrap-Up Panel Discussion


Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP

Vivien R. Bonazzi, PhD, Managing Director & Chief Biomedical Data Scientist, Deloitte Consulting LLP

Below are included sessions that are NOT included above. I covered ONLY the above sessions.

Session Availability


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


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

2:15 pm ET – W5: AI-Celerating R&D: Foundational Approaches to How Emerging Technologies Can Create Value

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

1:00 pm ET – Speed Networking

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.

2:00 pm ET – Stretch Break

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.

  • Check out your recommended connections flagged as “Want to Meet” in the People Tab. These connections were chosen based on your similar roles, companies and conference program interests.
  • Take a moment to add relevant interest tags to your profile. Then search and connect with participants who have the same interests.
  • Engage with technology leaders in their booths and view relevant videos and demos.
  • Take part in live Q&A with speakers and participants following each educational session.
  • Create and join in ad hoc group discussions throughout the event.
  • Watch Our Quick Tutorial on how to Maximize Networking Opportunities: CII’s Virtual Event Platform – Networking

10:00 AM – 11:25 AM EDT on Tuesday, October 6
Add to Calendar


10:00 Welcome Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute

10:05 Keynote Introduction

Scott Parker, Director of Product Marketing, Marketing, Sinequa

10:15 PLENARY KEYNOTE PRESENTATION: 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

11:05 LIVE Q&A: Session Wrap-Up Panel Discussion


Ari E Berman, PhD, CEO, BioTeam Inc

Session Availability

Wednesday, October 7

9:00 AM EDT

    The Emergence of the AI-Augmented Drug Discoverer

    9:00 AM – 9:20 AM EDT

    Mark Davies


9:20 AM EDT

    Generative Chemistry and Generative Biology for AI-Powered Drug Discovery

    9:20 AM – 9:40 AM EDT

    Alex Zhavoronkov

    Insilico Medicine

9:40 AM EDT

    Talk Title to be Announced

    9:40 AM – 11:00 AM EDT

    Grace Wenjia You

    EMD Serono

11:00 AM EDT

    Coupling AI and Network Biology to Generate Insights for Disease Understanding and Target ID

    11:00 AM – 11:30 AM EDT
    Cortellis, A Clarivate Analytics Solution logo

    Alexander Ivliev


11:30 AM EDT

    Session Wrap-Up Panel Discussion

    11:30 AM – 11:50 AM EDT



OLD Material


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.




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

Sponsors &
Conference Tracks

Comments RSS

Leave a Reply

%d bloggers like this: