Infusing Structure & Knowledge into Biomedical AI Algorithms | Marinka Zitnik (Harvard) YINS Seminar

Welcome to AIP.
- The main focus of this channel is to publicize and promote existing SoTA AI research works presented in top conferences, removing barrier for people to access the cutting-edge AI research works.
- All videos are either taken from the public internet or the Creative Common licensed, which can be accessed via the link provided in the description.
- To avoid conflict of interest with the ongoing conferences, all videos are published at least 1 week after the main events. A takedown can be requested if it infringes your right via email.
- If you would like your presentation to be published on AIP, feel free to drop us an email.
- AI conferences covered include: NeurIPS (NIPS), AAAI, ICLR, ICML, ACL, NAACL, EMNLP, IJCAI

If you would like to support the channel, please join the membership:

Subscribe to the channel:

Paypal ⇢
Patreon ⇢
Donate any cryptocurrency on BEP20 (BTC, ETH, USDT, BNB, Doge, Shiba): 0x0712795299bf00eee99f13b4cda0e19dc656bf2c
BTC ⇢ 1BwE1gufcE5t1Xh4w3wQhGgcJuCTb7AGj3
ETH ⇢ 0x0712795299bf00eee99f13b4cda0e19dc656bf2c
Doge ⇢ DL57g3Qym7XJkRUz5VTU97nvV3XuvvKqMX

The video was published under the license of Creative Commons, where reposted is allowed.

The video is reposted for educational purposes and encourages involvement in the field of AI research.
Yale Institute for Network Science
Subscribe to YINS now!
YINS Seminar: "Infusing Structure & Knowledge into Biomedical AI Algorithms"

Speaker: Marinka Zitnik
Assistant Professor of Biomedical Informatics, Harvard Medical School
Zitnik Lab:

Talk summary: Grand challenges in biology and medicine often lack annotated examples and require generalization to entirely new scenarios not seen during training. However, standard supervised learning is incredibly limited in scenarios, such as designing novel medicines, modeling emerging pathogens, and treating rare diseases. In this talk, I present our efforts to overcome these obstacles by infusing structure and knowledge into learning algorithms. First, I outline our subgraph neural networks that can disentangle distinct aspects of subgraph topology. I then present a general-purpose approach for few-shot learning on graphs. At the core is the notion of local subgraphs that transfer knowledge from one task to another, even when only a handful of labeled examples are available. This principle is theoretically justified as we show the evidence for predictions can be found in subgraphs surrounding the targets. I conclude with applications in drug development and precision medicine where the algorithmic predictions were validated in human cells and led to the discovery of a new class of drugs.

Speaker bio: Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik leads the Machine learning for Medicine and Science Lab, focusing on methods and applications for networked systems that require infusing structure and domain knowledge. This research won best paper and research awards from the International Society for Computational Biology, International Conference of Machine Learning, Bayer Early Excellence in Science Award, Amazon Faculty Research Award, Rising Star Award in EECS, and Next Generation Recognition in Biomedicine, being the only young scientist who received such recognition in both EECS and Biomedicine.

About the Yale Institute for Network Science (YINS): We produce and disseminate knowledge related to network science, in all its forms and applications. Network phenomena are now studied in many disciplines, including engineering, computer science, sociology, economics, political science, biology, physics, medicine, public health, and management. Hence, the study of networks is dramatically transforming scientific fields traversing engineering and the social and natural sciences. One of the major goals for YINS is to expose researchers to the phenomena, measurements, methodologies, and challenges of diverse disciplines. With this goal in mind, we proudly present the YINS Seminar Series, intended to promote the development and application of network science. Speakers include faculty from throughout Yale who are interested in networks, as well as distinguished guest lecturers who are scientists and innovators in the field. Visit us online at
Be the first to comment