.- Feddaw: Dual Adaptive Weighted Federated Learning for Non-IID Medical Data.
.- LoopNetica: predicting chromatin loops using convolutional neural networks and attention mechanisms.
.- Probabilistic and Machine Learning Models for the Protein Scaffold Gap Filling Problem.
.- Patient Anticancer Drug Response Prediction based on Single-Cell Deconvolution.
.- A Data Set of Paired Structural Segments between Protein Data Bank and AlphaFold DB for Medium-Resolution Cryo-EM Density Maps: A Gap in Overall Structural Quality.
.- PmmNDD: Predicting the Pathogenicity of Missense Mutations in Neurodegenerative Diseases via Ensemble Learning.
.- Improved Inapproximability Gap and Approximation Algorithm for Scaffold Filling to Maximize Increased Duo-preservations.
.- Residual Spatio-Temporal Attention based Prototypical Network for Rare Arrhythmia Classification.
.- SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for comprehensive quantitative metaproteomics.
.- PrSMBooster:Improving the Accuracy of Top-down Proteoform Characterization using Deep Learning Rescoring Models.
.- FCMEDriver: identifing cancer driver gene by combining mutual exclusivity of embedded features and optimized mutation frequency score.
