Computational Biology
Computational biology leverages computational tools and algorithms to analyze and interpret biological data, aiming to understand complex biological systems and processes. Current research heavily emphasizes the application of machine learning, particularly deep learning models like transformers and diffusion models, along with evolutionary algorithms and graph neural networks, to predict protein function, analyze gene expression, and solve other bioinformatics challenges. These advancements are significantly improving the speed, accuracy, and scalability of biological research, impacting areas such as drug discovery, personalized medicine, and disease understanding.
Papers
Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf, Steff Taelman, Maxime Van Haeverbeke, Wim Van Criekinge, Bernard De Baets
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin, Md. Alamin Talukder, AKM Azad, Sunil Aryal, Bikash Kumar Paul, Wahia Tasnim, Muhammad Ali Abdulllah Almoyad, Mohammad Ali Moni