Machine learning (ML) is a rapidly evolving subfield of artificial intelligence that focuses on developing algorithms and models that allow computers to learn from data without explicit programming. The field has progressed significantly since its origins in the 1950s, with major advancements in neural networks, deep learning, and applications across diverse domains. ML encompasses several types of learning approaches, including supervised learning for tasks like classification and regression, unsupervised learning for discovering patterns in unlabeled data, and reinforcement learning for decision-making through trial and error. Key algorithms and techniques in ML include decision trees, support vector machines, neural networks, and ensemble methods. In the realm of computational biology, ML has found numerous applications, from analyzing genomic and proteomic data to drug discovery and development. It plays a crucial role in tasks such as gene expression analysis, protein structure prediction, metabolic pathway modeling, and integrative multi-omics approaches. Computational biology itself is an interdisciplinary field that combines biology, computer science, mathematics, and statistics to analyze and interpret biological data. It relies heavily on bioinformatics tools and databases, and employs various computational techniques like sequence alignment and phylogenetic analysis. As the field continues to advance, it faces important ethical and regulatory considerations, particularly regarding data privacy and the implications of genomic research and personalized medicine.

This is a curated list of computational biology datasets that have been pre-processed for machine learning. This list is a work in progress, please submit a pull request for any dataset you would like to advertise!