Translational informatics is a vibrant and multifaceted field that uses advanced analytical tools in conjunction with varied datatypes to bridge the gap between benchtop research and clinical applications, enhancing the journey from data to discovery to clinical utility.

Selected Focus Areas in Translational Informatics

• Real-World Evidence & Real-World Data: Focus on the collection and analysis of data from real-world settings (outside of controlled trials) to support clinical practice and decision-making.
• Precision Medicine: Tailoring medical treatment to the individual characteristics of each patient based on molecular, cellular, and clinical profiles.
• Bioinformatics: Emphasize the methods for storing, retrieving, organizing, and analyzing biological data, especially "omic data" (e.g., genomic, metabolomic and proteomic).
• Clinical Informatics: Apply information and knowledge-based technologies in healthcare settings, including electronic health records (EHR) management, clinical decision support systems, and health information exchanges.
• Phenomics: Systematic assessment of phenotypes on a large scale, integrating phenotype data with genomic and other data to better understand disease mechanisms.
• Medical Imaging Informatics: Apply advanced computing and statistical techniques to imaging data to improve diagnosis, treatment, and monitoring.

Selected Tools and Methods

• Artificial Intelligence/Machine Learning: Utilized across various research areas for tasks such as predictive modeling, image analysis, genomics, and personalized medicine.
• Natural Language Processing (NLP): Applied to extract meaningful information from unstructured data sources like clinical notes and scientific publications.
• Large Language Models: Advanced AI models that process, generate, and understand human language, capable of assisting in tasks such as summarizing medical documents, generating patient information leaflets, and even aiding in the development of new therapies by analyzing large volumes of scientific literature.
• Digital Health Technologies: Encompasses the development and use of mobile apps, wearable devices, telehealth, and other technologies to monitor, assess, and manage health conditions.
• Cloud Computing: Utilizes remote servers hosted on the internet to store, manage, and process data, facilitating scalability and collaboration.
• Visualization Tools: Advanced graphical representations of data to facilitate understanding and insight into complex datasets.
• Federated Learning: Enabling multiple data sources and systems to be used to address problems while maintaining individual source and system provenance and privacy.
• Data Quality Assessment: Evaluating the contextual quality of data from different sources but exhibiting similar labels to assess adequacy of annotation to reduce noise in analysis.

Community Goals

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