The ICML Workshop on Computational Biology will highlight how machine learning approaches can be tailored to making discoveries with biological data. Practitioners at the intersection of computation, machine learning, and biology are in a unique position to frame problems in biomedicine, from drug discovery to vaccination risk scores, and the Workshop will showcase such recent research. Commodity lab techniques lead to the proliferation of large complex datasets, and require new methods to interpret these collections of high-dimensional biological data, such as genetic sequences, cellular features or protein structures, and imaging datasets. These data can be used to make new predictions towards clinical response, to uncover new biology, or to aid in drug discovery.
This workshop aims to bring together interdisciplinary machine learning researchers working at the intersection of machine learning and biology that includes areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community.
The workshop is a sequel to the WCB workshops we organized in the last five years at ICML, which had excellent line-ups of talks and were well-received by the community. Every year, we received 60+ submissions. After multiple rounds of rigorous reviewing, around 50 submissions were selected from which the best set of papers were chosen for Contributed talks and Spotlights and the rest were invited for Poster presentations. We have a steadfast and growing base of reviewers making up the Program Committee. For two of the previous editions, a special issue of Journal of Computational Biology has been released with extended versions of a selected set of accepted papers.