Machine Learning in Bioinformatics

GP-HTNLoc: A Graph Prototype Head-Tail Network-based Model for Multi-label Subcellular Localization Prediction of ncRNAs

GP-HTNLoc is a multi-label subcellular localization model for ncRNAs that addresses class imbalance and outperforms existing methods.

Peptriever: A Bi-Encoder approach for large-scale protein-peptide binding search

Peptriever uses a bi-encoder transformer model to efficiently rank proteins interacting with peptides, achieving high AUC-ROC and AUC-PR scores.

ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks

ChatNT is a conversational agent that understands biological sequences and achieves state-of-the-art results on genomics tasks while being accessible to non-coders.

Alignment-free sequence composition based features can distinguish paralogous proteins

The paper uses sequence composition features to classify paralogous proteins and identifies key amino acids driving classification.

Exploiting protein language models for the precise classification of ion channels and ion transporters

This paper introduces TooT-PLM-ionCT, a framework using six protein language models to classify ion channels and transporters from membrane proteins, achieving strong performance in distinguishing these classes.

Genome-scale annotation of protein binding sites via language model and geometric deep learning

GPSite predicts protein binding sites across multiple ligands using a multi-task network, outperforming existing methods and enabling genome-scale annotations.

Towards a Dataset for State of the Art Protein Toxin Classification

The paper develops a toxin classifier, BioLMTox, trained on a comprehensive dataset to improve in-silico toxin classification accuracy and recall.

Nphos: Database and Predictor of Protein N-phosphorylation

The paper constructs a database of N-phosphorylation sites and develops a predictor achieving high accuracy for human N-phosphorylation types.

Learning the language of proteins and predicting the impact of mutations

This paper introduces a protein language model that predicts mutation impacts and lineage classification in viral genomes, offering faster and more accurate functional predictions than traditional methods.

PROTGOAT : Improved automated protein function predictions using Protein Language Models

PROTGOAT improves protein function prediction using an ensemble of pre-trained protein language models, achieving high performance in CAFA 5 benchmark.

ViRNN: A Deep Learning Model for Viral Host Prediction

ViRNN is a deep learning model that predicts viral hosts from genomic sequences, achieving 90.55% accuracy and outperforming existing methods.

Effect of Tokenization on Transformers for Biological Sequences

This study evaluates how different tokenization methods affect transformer models' performance on various biological sequence tasks, showing improved accuracy and efficiency with optimized tokenizers.

TraitProtNet: Deciphering the Genome for Trait Prediction with Interpretable Deep Learning

TraitProtNet uses deep learning to predict traits in fungi from genome data, highlighting protein importance over expression levels through interpretability methods.

Scoring alignments by embedding vector similarity

The paper introduces a new amino acid similarity scoring method, E-score, based on contextual embeddings from deep learning, showing improved alignment quality compared to BLOSUM matrices.

DeepSS2GO: protein function prediction from secondary structure

DeepSS2GO predicts protein functions using secondary structure features, outperforming existing methods and achieving five times faster prediction.

Comprehensive prediction and analysis of human protein essentiality based on a pre-trained protein large language model

The paper introduces PIC, a deep learning model for predicting human protein essentiality, outperforming existing methods and quantifying essentiality across humans, cell lines, and mice orthologues.

ProInterVal: Validation of Protein-Protein Interfaces through Learned Interface Representations

This paper introduces a method to validate protein-protein interfaces using learned representations, achieving 0.91 accuracy on a benchmark dataset.

TemStaPro: protein thermostability prediction using sequence representations from protein language models

TemStaPro predicts protein thermostability using sequence embeddings from protein language models, showing good agreement with experimental data.

Sensitive remote homology search by local alignment of small positional embeddings from protein language models

The paper demonstrates that positional embeddings from protein language models can improve sensitivity in homology detection without sacrificing search speed.

Protein Language Models Expose Viral Mimicryand Immune Escape

The paper uses protein language models to distinguish human and viral proteins, identifying immune escape mechanisms through error analysis.

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki

CatPred predicts in vitro enzyme kinetic parameters using deep learning, achieving competitive performance with uncertainty quantification on diverse datasets.

Sequence-based Protein-Protein Interaction Prediction Using Multi-kernel Deep Convolutional Neural Networks with ProteinLanguage Model

This study introduces xCAPT5, a hybrid model combining CAPT5 and XGBoost for PPI prediction, outperforming existing methods on multiple tasks.

GATLncLoc+C&S: Prediction of LncRNA subcellular localization based on corrective graph attention network

The paper introduces GATLncLoc+C&S, a model for predicting LncRNA subcellular localization using a corrective graph attention network, achieving high accuracy on benchmark datasets.

Transformer-Based Deep Learning Model with Latent Space Regularization for CRISPR-Cas Protein Sequence Classification

This paper develops two deep learning models for classifying CRISPR-Cas proteins, achieving high accuracy in distinguishing CAS from non-CAS and sub-classifying CAS9 and CAS12.

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

The paper develops a computational method to predict protein-protein interactions using dipeptide compositions as key features, showing strong correlation across organisms.

Exploring Genomic Large Language Models: Bridging the Gap between Natural Language and Gene Sequences

The paper introduces GenomicLLM, a large language model trained on genomic and natural language data, capable of performing various analysis tasks with comparable performance to existing tools.

EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow

This paper extends EvoAug, an evolution-inspired data augmentation method for genomic deep learning, to TensorFlow, demonstrating comparable performance to the original PyTorch-based version.

Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning of Protein Fitness Landscape

This paper introduces ConFit, a method to train protein language models with limited experimental data for fitness prediction, showing improved accuracy and efficiency in low-N learning scenarios.

mtx-COBRA: Subcellular localization prediction for bacterial proteins

mtx-COBRA improves subcellular localization prediction for bacterial proteins, outperforming PSORTb in accuracy for proteins with unknown localization.

Prediction of protein-carbohydrate binding sites from protein primary sequence

This paper introduces StackCBEmbed, a machine learning model that predicts protein-carbohydrate binding interactions from primary sequence, achieving better performance than existing methods.