Computational biology has played an important role in the field of biomedical research. Due to the scarcity of datasets and the lack of domain-specific expertise, the exploration of AI, in particular with the machine learning and deep learning techniques, in computational biology was mostly limited to single data types. Multimodal learning, as a recently powerful and promising learning scheme, aims to fuse information obtained from different modalities for enhancing data understanding and perception, realizing cross-domain applications, and accomplishing complex tasks. Existing deep learning approaches in multimodal analysis have already been applied in multiple fields, including robotics, autonomous vehicles and medical research. In recent years, with the advancement of biotechnology, more and more data have been collected and made publicly available, we believe the future is to model and understand these multi-source data, including but not limited to sequencing, geometric information, 3D structure and cryo-Electron tomography. The goal of this workshop is to encourage the researchers from AI and bioinformatics fields to bring together the state-of-the-art frameworks and solutions focus on integrating data from different modalities to explore more possibilities.

Authors are invited to submit original, full-length research papers that are not previously published, accepted to be published, or being considered for publication in any other forum. Full-length papers should satisfy the standard requirements of top-tier international research conferences.Manuscripts should be submitted to ACM-BCB 2024 OpenReview site in PDF format, using double column ACM SIG conference format, see https://www.acm.org/publications/proceedings-template. Full papers cannot exceed 8 pages, including an appendix, plus unlimited references (paper content is limited to 8 pages, that means that if you have an appendix, then it should be included within that page limit. It is also ok if you do not have an appendix and instead 8 pages of content). The review of manuscripts will be double-blind, and submissions not properly anonymized will be desk-rejected without review.Papers that include text generated from a large-scale language model (LLM), such as ChatGPT, are prohibited unless this produced text is presented as a part of the paper’s experimental analysis. AI tools may be used to edit and polish authors’ work, such as using LLMs for light editing of their text (e.g., automate grammar checks, word autocorrect, and other editing of author-written text), but text “produced entirely” by generative/AI models is not allowed.

We welcome submission on tasks related to the intersection of multimodal analysis and biomedical research, including but not limited to:

• Protein-ligand Binding Affinity Prediction
• Spatial Transcriptomics Data Analysis
• Protein-Protein Interaction
• Protein Design and Generation
• Protein Localization, Function or Binding Site Prediction
• Biomedical Image Processing
• The application of Biological Language Model in Computational Biology