List of participants (tentative list, including organizers):
Nadav Ahituv (UCSF), Patrick Aloy (IRB Barcelona), Gil Ast (Tel-Aviv University), Sourav Bandyopadhyay (UCSF), Anastasia Baryshnikova (Calico), Judith Berman (Tel-Aviv University), Pascal Braun (Institute of Network Biology, Helmholtz Zentrum München), Steven Brenner (UC Berkeley & UCSF), Rani Elkon (Tel-Aviv University), Chung Chao Hon (RIKEN), Nicholas Ingolia (UC Berkeley), Nilah Ioannidis (UC Berkeley), Manolis Kellis (Massachusetts Institute of Technology), Martin Kircher (Berlin Institute of Health), David Knowles (New York Genome Center), Daphne Koller (Stanford University), Martin Kupiec (Tel-Aviv University), Florian Markowetz (University of Cambridge), William Stafford Noble (University of Washington), Shyam Prabhakar (Genome Institute), Natasa Przulj (Barcelona Supercomputing Center), Teresa Przytycka (NCBI, NIH), Ron Shamir (Tel Aviv University), Roded Sharan (Tel-Aviv University), Mona Singh (Princeton University), Yun S. Song (UC Berkeley), Michael Springer (Harvard University), Aaron Streets (UC Berkeley), Peter Sudmant (UC Berkeley), Olga Troyanskaya (Princeton University), Erich Wanker (MDC Berlin), Zhiping Weng (University of Massachusetts), Xiaoxia Wu (University of Texas at Austin), Jimmie Ye (UCSF)
Numerous genome-wide datasets of biological samples, biochemical assays, genomic variation and phenotypic information are rapidly becoming available. These include, among others, genomics (DNA), transcriptomics (RNA), proteomics, metabolomics, ribosome profiling, and chromatin state (epigenomic) profiles such as DNA methylation, protein-DNA binding, chromatin accessibility and genomic contacts. The pertaining datasets carry enormous potential for better understanding of cellular and disease processes and thus facilitate the exploration of basic science questions, as well as drive translational applications. Basic biological questions include the association of genomic variants with functional effects, the identification of physical and functional interactions among molecules, and the inference of molecular mechanisms that underlie a response of interest. Translational challenges include the identification of disease biomarkers, the stratification of patients based on their molecular profiles, the prediction of disease state and outcome, and the inference of potential drug targets and treatments for diseases of interest.
However, these fundamental questions raise many technological, computational and statistical challenges—from data measurement to data integration and machine learning, some of which include:
- Improved technologies for measuring at higher resolution and with higher accuracy genome-wide data.
- Enhanced algorithms for integrating diverse data across different samples, tissues, cells and conditions.
- Improved modeling approaches for large scale and diverse data with coupled learning strategies that allow accurate inference of functional interactions.
These challenges call for major advances in biotechnology, algorithmics and statistics. They also call for combined experimental-computational approaches to maximize the gain from the developments in these domains. At the workshop, we will discuss recent advances in all three domains, how they apply to the questions at hand, and how they can be combined to drive us forward in this scientific quest.
Further details about this workshop will be posted in due course.
If you are interested in joining this workshop, please see the Participate page.
Registration is required to attend this workshop. Space may be limited, and you are advised to register early. The link to the registration form will appear on this page approximately 10 weeks before the workshop. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.
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