Bin Yu

This study shows that plantaricin JK from Lactiplantibacillus plantarum inhibits EHEC O104:H4 and modulates gut microbiota in vitro, with engineered strains enhancing efficacy.

This paper introduces DQHTFI, a framework for drug-target interaction classification and binding affinity regression, achieving competitive results on benchmark datasets.

PG-MLD transfers dynamic 3D physical knowledge to SMILES-based molecular models, improving property prediction without requiring 3D conformer generation.

WattmaMod detects multiple RNA modifications from nanopore sequencing data using deep learning, achieving high accuracy and extending to low-resource modifications.

DNN-DTIs predicts drug-target interactions using XGBoost feature selection and deep neural networks, achieving high accuracy on multiple datasets.

This paper presents a model for predicting fertility-related proteins using multi-information fusion and LightGBM, achieving high accuracy in cross-validation and independent testing.

This paper introduces a deep forest-based method for predicting protein-protein interactions, achieving high accuracy in yeast and Helicobacter pylori and improving drug discovery.

The paper introduces MpsLDA-ProSVM, a model for predicting multi-label protein subcellular localization using feature refinement and a novel classifier, achieving high accuracy across multiple organisms.

StackPDB predicts DNA-binding proteins using XGB-RFE feature optimization and a stacked ensemble classifier, achieving high accuracy and MCC on benchmark datasets.

This paper introduces a PPI prediction method using gradient tree boosting and L1-regularized logistic regression, achieving high accuracy on multiple organisms.

The paper introduces DMLDA-LocLIFT, a method for predicting multi-label protein subcellular localization using dimensionality reduction and a classifier, achieving high accuracy on bacterial and plant datasets.

StackRAM is a cross-species method for identifying m6A sites using machine learning, achieving high accuracy in yeast, human, and Arabidopsis.

This paper introduces signed iRF (s-iRF), a method to refine interaction search in biological systems, identifying stable and predictive feature interactions for enhancer activity and spatial gene expression.

The paper introduces DeepTune, a framework to interpret neural network models of V4 neurons, revealing diverse shape/texture tuning and suppressive responses in primate visual cortex.