Translational Informatics (TI) Community

Description & Goals

The Translational Informatics community is comprised of scientists who develop and apply novel data-driven methodology to biomedical data in order to advance the basic science and practice of clinical pharmacology to ultimately enhance patient outcomes. The field of informatics is highly diverse ranging from bioinformatics (development and application of methods to analyze large scale biomedical and omics data, and conduct knowledge discovery) to clinical informatics (the application of information technology to manage Healthcare systems, improve the quality of Healthcare, and deliver novel healthcare services). Informatics methods are developed and applied to facilitate translational research.

Community Goals

To provide scientific expertise and resources regarding the discovery, development, regulation and utilization of informatics methodology to advance the basic science and practice of clinical pharmacology.
To identify and bridge existing gaps in clinical pharmacology and translational medicine by developing and implementing innovative scientific programs from emerging areas of informatics.
To identify educational opportunities and provide guidance on the application of informatics tools to advance science and practice of clinical pharmacology.
To engage community members through collaboration, volunteer opportunities, and outreach to grow a diverse informatics community with a patient-focus.

Community Connection

Join the LinkedIn Group.

Past Webinars

The webinars below can be accessed in the Members Only Webinar Library: Access Library

Activity Identification and Verification Framework
How to Streamline Assessment of Unmet Clinical Research Need and Preliminary Evidence of Applicable Digital Health Technology-Based Solution
Introduction of Deep Learning in Drug Discovery and Development Part 6 – Model Interpretability
Big Data in Immunology and Clinical Research: Sharing, Dissemination, and Repurposing
Introduction of Deep Learning in Drug Discovery and Development Part 5 – Time Series Analysis
Taking A Second Look: A Type 2 Diabetes Subtype Responsive to Intensive Glycemia Treatment in the ACCORD Trial
Introduction of Deep Learning in Drug Discovery and Development Part 4 – Natural Language Processing (NLP)
Introduction of Deep Learning in Drug Discovery and Development, Part 3 – Computer Vision with Convolutional Neural Networks (CNNs)
Challenges in Dealing with Real-World Data
Advancing High-Fidelity, Personalized Pharmacogenomics Education Through the Test2Learn Platform
Drug Re-purposing Opportunities Using Shared Gene Expression Molecular Signatures - Case Studies in Neurodegenerative Diseases and Infections
Merging AI and Pharmacometrics Approaches to Elucidate Genes Linked to Disease Progression of Diabetic Patients on Metformin

Leadership

Brian Cicali, PhD

Community Chair

Patrick Hanafin, PhD

Community Vice Chair

Jagdeep Podichetty, PhD

Community Past Chair

Steering Committee members

Chandrali Bhattacharya, PhD
Philip Empey, PharmD, PhD
Xiajing (Jean) Gong, PhD
Michael Liebman, PhD
Gina Patel, PhD
Jagdeep Podichetty, PhD
Sony Tuteja, PharmD