Useful Resources  

Useful Resources

We are looking to make the community aware of useful data science resources. If you know of any resources, please let us know. Contact us: info@dknet.org

Training material and Online Courses

• datacamp.com: An online learning platform that offers courses in data science, AI, and programming skills.

Webinar Resources

• dkNET Bioinformatics Webinar Series: Webinars presented by the dkNET New Investigator Pilot Program Awardees

• dkNET Webinar Series; Recordings: Webinars hosted by dkNET. The webinar topics include: useful resources for the researchers in NIDDK relevant disease fields, Rigor and Reproducibility, FAIR data, protocols.io, bioinformatics tools…etc. 

• Training@Bridge2AI: Bridge2AI training resources focuses on fostering collaboration across disciplines, enhancing data use ethics, and equipping learners with the skills to navigate and apply AI/ML in real-world biomedical contexts.

Talks, for AI, and related

• UCI ISG Seminar series: Talks hosted by The Information Systems Group (ISG) at UC Irvine  

• UCI Seminar Series in AI & Machine Learning: Talks hosted by Center for Machine Learning and Intelligent System at UC Irvine

Conferences in data science and related

• NIDDK AI Workshop “Artificial Intelligence in Precision Medicine for Diabetes and Other Chronic Diseases: Challenges and Opportunities in Developing Predictive Models and Biomarkers for Disease Initiation, Progression, Prevention, and Subtypes” : This workshop aims to bring together biomedical researchers and AI/ML experts, to discuss the critical challenges, crosscutting gaps, and opportunities for and actionable items in leveraging AI/ML and other recent data science advances in precision medicine.

• KDD (KDD Conference on Knowledge Discovery and Data Mining): The annual ACM SIGKDD Conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share ideas, research results and experiences.  

• ISMB by International Society for Computational Biology: The Intelligent Systems for Molecular Biology (ISMB) conference is vital for its role in uniting computer science, biology, and bioinformatics. It accelerates discovery by leveraging intelligent systems to analyze complex molecular data, enabling precision medicine and revolutionizing drug discovery. ISMB addresses big data challenges, educates researchers, and fosters interdisciplinary collaboration, making it a critical hub for advancing life science research and applications.

• PSB (Pacific Symposium on Biocomputing): An international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. 

• ICSB (the International Conference on Systems Biology): ICSB brings together the  systems biology community from different countries and facilitate interaction, sharing ideas and incubating collaboration with the best practitioners of systems biology. 

• Other AI Conferences: SIGMOD, VLDB, ICDE, EDBT, CIKM, NeurIPS, ICML, ICLR, AAAI, IJCAI

Other

• NIH Office of Data Science: Training programs, data sharing best practices, and updates on relevant policies and developments within the NIH data ecosystem.

• Bridge2AI: The Bridge2AI Consortium seeks to bridge the biomedical and behavioral research communities with the rapidly growing community of experts developing AI/ML models by producing flagship datasets that adhere to the FAIR principles (Findable, Accessible, Interoperable, Reproducible) and critically integrate ethical considerations in preparing data for computation.

• AIM-AHEAD: AIM-AHEAD program has established mutually beneficial, coordinated, and trusted partnerships to enhance the participation and representation of researchers and communities currently underrepresented in the development of artificial intelligence and machine learning (AI/ML) models and to improve the capabilities of this emerging technology, beginning with electronic health records (EHR) and extending to other diverse data to address health disparities and inequities.