Our lab aims to developing an automated computational pipeline for peptide drug design and discovery, which involves bioactive peptide generation, prediction, and functional analysis (such as peptide toxicity).
Our lab focuses on developing computational methods to accurately annotate epigenetic modifications in whole-genome scale, and study their functions in gene regulatory network.
Our lab also focuses on the development of generic machine learning algorithms and applied them for analyzing biological sequence data and imaging data. We particularly interest in designing new representation learning architectures to automatically learn and extract the functional patterns underlying different data.
[Nov-17-2022] Prof. Wei was named "Highly Cited Researcher" in Computer Science by Clarivate Analytics 2021!
[Oct-3-2022] Our work " iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations" has been accepted for publication on Genome Biology.
[Jun-24-2022] Prof. Wei was appointed as the Editorial Board Member of "Biology Methods and Protocols" (Oxford University Press).
[May-18-2022] Our work " Predicting protein-peptide binding residues via interpretable deep learning" has been accepted for publication on Bioinformatics.
[Apr-26-2022] Our collaborative work "Recent advances in the prediction of subcellular localization of proteins and related topics." has been accepted for publication on Frontiers in Bioinformatics.
[Apr-20-2022] Our work "scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods" has been accepted for publication on Nucleic Acids Research.
[Apr-14-2022] Prof. Wei was named "Highly Cited Chinese Researcher" by Elsevier 2021!
[Feb-20-2022] Our work "Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images " has been accepted for publication on Bioinformatics.
[Jan-15-2022] Prof. Wei was appointed as the Editorial Board Member of METHODS.
WeiLab 2021 by WEIlab