Machine Learning in Bioinformatics

To improve the predictions of binding residues with DNA, RNA, carbohydrate, and peptide via multiple-task deep neural networks

The paper introduces MTDsite, a multi-task deep learning method for predicting protein-binding residues across DNA, RNA, peptide, and carbohydrate, achieving higher accuracy than existing methods.

Annotating Gene Ontology terms for protein sequences with the Transformer model

The paper introduces a Transformer-based model for annotating Gene Ontology terms in protein sequences, showing improved accuracy over CNN-based methods and incorporating additional features like BLAST motifs and structural data.

SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

SAMbinder predicts SAM binding residues in proteins from amino acid sequences using machine learning, achieving MCC 0.61 and AUROC 0.89 on a realistic dataset.

A hybrid model for predicting pattern recognition receptors using evolutionary information

This study develops a hybrid model combining BLAST and machine learning to predict pattern recognition receptors, achieving high accuracy with an MCC of 0.82 and AUROC of 0.95.

End-to-end multitask learning, from protein language to protein features without alignments

This paper introduces an end-to-end multitask learning approach using language models to predict protein structure and function without evolutionary alignments, achieving faster predictions with moderate accuracy.

TranCEP: Predicting transmembrane transport proteins using composition, evolutionary, and positional information

TranCEP predicts transmembrane transport protein substrates by integrating amino acid composition, evolutionary information, and positional data, outperforming existing methods.

DeepKinZero: Zero-Shot Learning for Predicting Kinase-Phosphosite Associations Involving Understudied Kinases

DeepKinZero predicts kinase-phosphosite associations for understudied kinases using zero-shot learning, improving accuracy compared to existing methods.

UDSMProt: Universal Deep Sequence Models for Protein Classification

UDSMProt uses self-supervised language modeling to create a universal protein sequence model that outperforms task-specific methods in enzyme class prediction, gene ontology prediction, and remote homology detection.

DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins

This paper introduces two deep learning methods, DeepDBP-ANN and DeepDBP-CNN, to identify DNA-binding proteins from protein sequences, achieving state-of-the-art accuracy on benchmark datasets.

Evolutionary context-integrated deep sequence modeling for protein engineering

This paper introduces ECNet, a deep learning model that uses evolutionary context to predict protein variant fitness, outperforming existing methods on multiple datasets.

G-domain prediction across the diversity of G protein families

The paper introduces SMA, an algorithm to predict G domains in G proteins by matching G-box patterns and inter-box spacer lengths, improving precision for family-specific motifs.

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies

The paper develops machine learning models to predict antibody biophysical properties using sequence data, finding that charge and hydrophobicity are key features.

Modeling the Language of Life - Deep Learning Protein Sequences

This paper introduces SeqVec, a method to represent protein sequences as continuous vectors using deep learning, achieving better prediction results than methods relying on evolutionary information.

FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N6-methyladenine sites

FastFeatGen improves DNA N6-methyladenine site prediction by combining parallel feature extraction and machine learning, achieving high accuracy on rice and mouse genomes.

Spectrum of protein localization in proteomes captures evolutionary relation between species

The paper compares predicted protein localization spectra across ten eukaryotic species to infer evolutionary relationships.

Prediction of liquid-liquid phase separation proteins using machine learning

The paper develops a machine learning tool, PSPredictor, to predict phase separation proteins using sequence information, achieving high accuracy and outperforming existing methods.

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

The paper develops an artificial neural network model to predict breast cancer-related proteins, achieving high accuracy and identifying top-ranked proteins in three categories.

PerMemDB: a database for eukaryotic peripheral membrane proteins

PerMemDB is a database of eukaryotic peripheral membrane proteins, containing 241,173 entries from 1,216 organisms, annotated with interaction data and domain predictions using MBPpred.

SKIPHOS: non-kinase specific phosphorylation site prediction with random forests and amino acid skip-gram embeddings

This paper introduces SKIPHOS, a non-kinase specific phosphorylation site predictor using random forests and amino acid skip-gram embeddings, showing better performance than existing methods for three phosphorylation residues.

Expectation pooling: An effective and interpretable pooling method for predicting DNA-protein binding

This paper introduces expectation pooling, an interpretable CNN pooling method that improves DNA-protein binding prediction by combining probabilistic ideas with deep learning.

Detecting Novel Sequence Signals in Targeting Peptides Using Deep Learning

The paper introduces TargetP 2.0, a deep learning method to identify N-terminal sorting signals in proteins, revealing insights into the importance of the second residue in targeting peptides.

Supervised learning of protein thermal stability using sequence mining and distribution statistics of network centrality

The paper improves protein thermal stability classification using sequence mining and network centrality statistics, achieving 96% accuracy.

Predicting protein subcellular location using learned distributed representations from a protein-protein network

This study introduces node2loc, a deep learning method that predicts protein subcellular locations using protein-protein network representations, achieving an MCC of 0.812.

DeepGOPlus: Improved protein function prediction from sequence

DeepGOPlus improves protein function prediction from sequence alone, achieving faster and more accurate results on biological processes and cellular components.

Evaluating Protein Transfer Learning with TAPE

Across five protein tasks with evolution-aware splits, self-supervised pretraining helped nearly every model on every task, yet alignment-based features still beat learned embeddings on secondary structure and contact prediction.

An Accurate Bioinformatics Tool For Anti-Cancer Peptide Generation Through Deep Learning Omics

The paper introduces an anti-cancer peptide prediction tool, ACS, achieving 95% accuracy, outperforming existing methods.

PureseqTM: Efficient and accurate prediction of transmembrane topology from amino acid sequence only

PureseqTM predicts transmembrane topology from amino acid sequences, outperforming existing methods and correcting UniProt annotations in the human proteome.

SolXplain: An Explainable Sequence-Based Protein Solubility Predictor

SolXplain is a sequence-based protein solubility predictor that achieves high accuracy and provides feature-based explanations for its predictions.

Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima

The paper develops machine learning models to predict microorganism growth temperatures and enzyme catalytic optima using proteome-wide amino acid composition data.

Lasagna: Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN

Lasagna is an end-to-end framework for protein-protein interaction prediction using primary sequences, outperforming existing methods in binary prediction and showing promise in interaction type and binding affinity tasks.