AI/ML

 

 

Medicine is data driven. Gone are the days of the chancing alchemist and in with the data. Relationships far too complexed for our minds to decipher are becoming unravelled thanks to AI and heaps of Big Data. Learn how companies are leveraging new opportunities here to extract meaning and correlate increasingly complexed systems biology and drug discovery platforms.
 

THIS YEAR’S WORLD LEADING AI/ML SPEAKERS

 

 

 

AI & ML, Tuesday 2 November 2021

10:40

Networking Break

Rafael Rosengarten
AI & ML
11:35

Chair's Remarks

Brandon Allgood
AI & ML
11:40

Using ML and augmented RWD for Novel target & biomarker generation  

Valo applies machine learning techniques to longitudinal patient data to identify and characterize patient sub-populations within focal disease areas using our Opal Computational Platform, a human-centric data-driven platform that aims to accelerate drug development. The Opal platform is being built to allow us to discover novel disease subtypes and biomarker associations with the objective to causally link target and pathway mechanisms to physiological biomarkers of patient fit and response to disease-relevant outcomes, improving clinical trial design and target ID through reverse translation. In this session, I will review how we use Opal to verify or disprove potential protein target drivers, and how Opal integrates data on molecular mechanisms to further strengthen the subgroup-biomarker-target hypothesis.
Edmund Champness
AI & ML
12:00

Bringing AI into day-to-day drug discovery: A case study optimising kinase profiling programmes with deep-learning platform Cerella

  • Reduce experimental costs by upfront being able to prioritise experiments that will providing the most information for decision making
  • Provide uncertainty estimates highly correlated with prediction accuracy on a per compound and experiment level
  • Provide accurate prediction outperforming other AI and QSAR methods
Philippe Menu
AI & ML
12:20

Advancing data-drive medicine: unlocking the potential of multimodal digital health data sets

Adrien Rousset
AI & ML
12:40

Debunking the Myths: Clinical Risk Prediction using Machine Learning

  • An overview of data dimensions and potential tradeoffs when developing innovative models
13:20

Networking Lunch Break

Daniel Jamieson
AI & ML
15:00

Empowering drug discovery with causal data

Bjorn Schuller
AI & ML
15:20

Ambient Personalised Health Intelligence: T-minus 3

  • AI for mHealth and IoT for Health
  • Examples from neurodevelopment to neurodegenerative, and psychological disorders to COVID-19
  • 3 Steps towards getting it „out there“
Ryan Fukushima
AI & ML
15:40

Data Centric AI in Clinical Research

Mathieu Galtier
AI & ML
16:00

Enabling access to health data while protecting privacy with Federated Learning

16:20

Networking Break

Panel discussion
AI & ML
17:00

AI for smarter portfolio management: Moving beyond serendipity in early R&D

Pharmaceutical research is inherently risky and failed or discontinued projects are a common part of the R&D process. However, a key point of failure is that competitive information and biological validation necessary to decide on target selection and clinical development is neither timely nor holistically available. With the volume of information increasing exponentially – from biomedical literature, to labels, to clinical trials, to conference readouts, to news–tackling this problem is a massive challenge.Roche is partnering with Causaly to build a unified and systematic approach to drive better project and portfolio level decisions. Machine-reading millions of documents with human precision and presenting opportunities, threats and other signals to decision makers is the center point of the collaboration. In this session, Causaly and Roche will discuss a user-centric application of AI for smarter portfolio management and value creation in early R&D.
18:00

Neri & Sons Band & Networking Drinks Reception

last published: 25/Oct/21 15:50

AI & ML, Wednesday 3 November 2021

10:20

Networking Break + Speed Networking

Rafael Rosengarten
AI & ML
11:20

A Million to One: Bridging the Biomarker Chasm from Discovery to the Clinic

Bill Fox
AI & ML
12:00

Next Generation AI Comes to Healthcare and Life Sciences

Abhimanyu Verma
AI & ML
12:20

Unlocking Enterprise Value from AI @ scale

13:00

Networking Lunch Break

Philip Hastings
AI & ML
14:40

Empower pharma decision-making with NLP from molecule to market

Pharma companies need good data to support decision making, from discovery to medical, development to commercialization. With 80% of all data in unstructured formats, organizations need innovative technologies to untap the potential value of these data streams. Deploying natural language processing (NLP) at scale, for example embedding NLP into your machine learning workflows, is key to achieving a step change in data access and data insights. This talk will provide an overview of pharma use cases from a range of application areas, highlighting where significant benefit can be found, using sources such as preprints for latest research or GEO for target landscapes.
Gloria Macia
AI & ML
15:00

Patient Data and Digital Transformation

Guillaume Azarias
AI & ML
15:20

Challenges and opportunities arising from the use of patient-reported outcomes for machine learning

Panel discussion
AI & ML
15:40

Data integration and AI mining of pooled drug-CRISPR screens identifies novel targets and resistance to cancer treatments

  • AstraZeneca-CRUK Functional Genomics Centre’s vision is to accelerate the development of new treatments for people with cancer
  • The FGC generates pooled drug-CRISPR screen data for uncovering novel targets to battle resistance to cancer therapies
  • This talks focuses on our FAIR data strategy, integration of drug-CRISPR data with other large scale pre/clinical data sets and AI mining approaches to triage the pooled drug-CRISPR hits with a clear impact on oncology new target strategy
16:00

Networking Break

Kornel Marko
AI & ML
16:40

Time for what really matters - How an integrated ML/NLP platform can help to streamline your processes.

Reduce your effort by 75% with tailor-made classification of literatureUse case examples: Systematic Literature Review , Adverse Event Monitoring, Competitive Intelligence
Valeria De Luca
AI & ML
17:00

Using AI at Novartis

18:00

Drinks Reception

last published: 25/Oct/21 15:50

AI & ML, Thursday 4 November 2021

Aleksander Mihajlović
AI & ML
12:10

Living with the black box syndrome: The growing challenge of regulating AI‑driven R&D in biotech

This talk and panel discussion will consider the challenges of regulating AI-driven biomarker signature generation and diagnostic test development. Contemporary topics to be considered will include;·How are AI-trends beginning to change clinical genomic diagnostic R&D?·What are the "black box" difficulties behind accepting AI-derived results?·What is the potential impact these difficulties may have on correctness when using AI-enabled tools in the clinical setting?·Can model transparency and explainability solve the trust and regulatory issues hindering the wide spread acceptance and adoption of AI in clinical genomic diagnostics?
Srikanth Ramakrishnan
AI & ML
12:40

ML Ops Delivery for Pharmaceutical R&D -Transforming Processes for Global Medical Safety

Marion Schwaerzler
AI & ML
14:00

Honing into Humans’ Individual Needs: How Natural Language Processing Helps us to Analyze Text Data More Human-Like

  • After years of standardization of human and patient input for analytics, Natural Language Processing (NLP) provides us the opportunity to capture individual and non-standardizable insights from spoken and written text into all kinds of interactions on a large scale.
  • NLP enables us to analyze the input more human-like and on a larger amount of data than ever before with the aim of putting the individuals' needs into the center of attention of Artificial Intelligence.
  • This talk will be about the journey of a successful NLP team at Bayer, how we got there and what it needs to build digital products enhanced by NLP successfully. We will also touch upon selected examples of digital products enhanced by NLP in the pharma world.
Wankyu Kim
AI & ML
14:30

KMAP - Genome-scale Understanding of Drug MoA and Discovery

  • Drug-induced Transcriptome provides rich information on Drug MoA
  • KMAP- Globally Unique Reference Database for ~3000 approved Drugs' Transcriptome
  • KMAP is applicable for MoA, Drug Repositioning, Indication Expansion, and more.
last published: 25/Oct/21 15:50

GET INVOLVED AT BIODATA WORLD CONGRESS

 

 

TO SPONSOR


Alistair Wilmot
alistair.wilmot@terrapinn.com
0207 092 1174

 

 

TO SPEAK


Edward Glanville

edward.glanville@terrapinn.com
0207 092 1042

 

 

MARKETING OPPORTUNITIES


Onika Akhtar
onika.akhtar@terrapinn.com
0207 092 1034