Machine Learning in Bioinformatics

The paper develops a method to predict if two proteins catalyze the same reaction by integrating sequence and structural features, outperforming existing approaches.

Fast-Part partitions biological sequence data efficiently while maintaining homology separation and stratification ratios.

DeepLocPro is a subcellular location predictor for prokaryotic proteins, outperforming PSORTb 3.0 on benchmark metrics using experimentally verified data.

FAPM improves protein functional annotation by combining protein sequence and language models, outperforming sequence-only methods on benchmarks and phage proteins with few homologs.

The paper evaluates localization prediction algorithms across evolutionary distances and finds performance drops significantly, especially for plant mitochondria.

The paper introduces ISM, a sequence-only model that improves upon ESM2 by incorporating structural information, achieving better performance on mutation stability and structure prediction tasks.

PLM-interact improves PPI prediction by jointly encoding protein pairs, achieving 16-28% AUPR gains across multiple species.

InterLabelGO+ improves protein function prediction by combining deep learning and alignment-based methods with a novel loss function and dynamic weighting, achieving strong performance in the CAFA5 challenge.

FastProtein is an open-source proteomics analysis tool that automates in silico protein property prediction and annotation, providing a user-friendly interface for both small and large-scale studies.

This paper introduces a hybrid machine learning model for protein classification using amino acids, achieving 95% accuracy and outperforming existing methods by 15%.

DeepEST predicts bacterial protein functions using a multimodal deep learning framework combining gene expression, localization, and structure data, outperforming existing methods on a 25-species benchmark.

This paper introduces LC-PLM, a protein language model based on BiMamba-S architecture, showing improved performance on protein tasks compared to Transformer-based models.

This paper introduces two tools, HAM and HAC, to preprocess protein sequences by masking homologous regions and resolving annotation conflicts, improving machine learning model integrity.

The paper introduces three geometric learning models for predicting protein stability changes upon mutations, achieving improved performance over existing methods.

SpatialPPI 2.0 predicts protein-protein interactions using graph neural networks and inter-residue analysis, outperforming existing methods and working with AlphaFold-predicted structures.

This paper introduces a dual-language foundation model that integrates protein and coding sequences, showing improved performance in discriminative tasks and generating coding sequences with higher protein yield.

This study introduces PARA, a pre-trained model for antibody sequences that outperforms existing models on various tasks by incorporating antibody-specific features.

This paper develops a CNN-based framework to predict plastic-degrading enzymes from environmental sequences, achieving high accuracy and F1 score.

BBATProt predicts protein and peptide functions using explainable deep learning, outperforming existing models in hydrolytic catalysis, peptide activity, and post-translational modification site prediction.

This paper adapts DNA and protein bio-language models for mRNA-focused tasks through various finetuning techniques, showing that medium-scale models perform best for cross-modal knowledge transfer.

This study introduces a sequence-based deep learning method for predicting DNA-binding residues, outperforming existing models and demonstrating interpretability through contrastive learning and graph neural networks.

The paper introduces a deep learning model that improves PTM prediction by incorporating known PTM sites into the training process, resulting in better performance than existing models.

The paper introduces ProtAC, a framework for cleaning large protein datasets using multimodal learning, achieving improved function prediction and a cleaned UniRef50 dataset.

ProteinAligner improves protein function and property prediction by integrating sequence, structure, and literature text modalities in a multi-modal pretraining framework.

This paper introduces ProPEC, a perplexity-based ensemble model for zero-shot prediction of viral protein fitness, showing improved performance over individual models.

The paper develops pLM-DBPs, a tool for predicting DNA-binding proteins in plants using protein language model embeddings, achieving 83.8% MCC on an independent test set.

BioLLMNet improves RNA-interaction prediction by combining multi-modal language model features using a gating mechanism, achieving state-of-the-art results in RNA-protein, RNA-small molecule, and RNA-RNA interactions.

This paper introduces PROEDIT, a method to remove virus-related knowledge from pre-trained protein models to improve safety in mutation effect predictions without affecting performance on non-virus proteins.

The paper develops RNN and MLP models to predict O-GlcNAcylation sites in mammalian proteins, achieving significantly better performance metrics than previous models.

Pair-EGRET predicts protein-protein interaction sites using graph attention networks and protein language models, outperforming existing methods on experimental data.