Machine Learning in Bioinformatics

Accurate plant pathogen effector protein classification ab initio with deepredeff, an ensemble of convolutional neural networks

The paper develops deepredeff, a deep learning model ensemble for classifying plant pathogen effector proteins from sequence data, achieving higher accuracy and lower false positives than existing tools.

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction

The paper predicts human-virus protein-protein interactions using transfer learning with CNN and MLP, outperforming existing methods and applying it to SARS-CoV-2.

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks

This study introduces AttCNNPPISP, a model combining attention mechanisms and CNNs to predict protein-protein interaction sites, showing improved performance over existing methods.

Language models enable zero-shot prediction of the effects of mutations on protein function

A single pre-trained protein language model, ESM-1v, scores mutations zero-shot about as well as family-specific DeepSequence across 41 deep mutational scans, with no experimental labels or retraining.

A multi-task deep-learning system for predicting membrane associations and secondary structures of proteins

The paper introduces MASSP, a deep-learning system that simultaneously predicts secondary structure, transmembrane segments, and topology of proteins, outperforming existing methods in accuracy and applicability.

ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing

This paper trains and evaluates language models on protein sequences to predict structural and localization features, achieving state-of-the-art results without evolutionary information.

Deep learning model for the prediction and classification of protein toxins across all domains of life

This paper introduces a deep learning model, ConvTOX, that classifies protein toxins across all domains of life with high accuracy.

Sigma70Pred: A highly accurate method for predicting sigma70 promoter in prokaryotic genome

The paper introduces Sigma70Pred, a method for predicting sigma70 promoters in prokaryotic genomes, achieving high accuracy on training and independent datasets.

In-Pero: Exploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins

In-Pero predicts peroxisomal protein sub-localisation using deep learning embeddings, achieving over 0.9 classification accuracy.

Reverse-Complement Equivariant Networks for DNA Sequences

The paper introduces new RC-equivariant architectures for DNA sequence analysis, showing they outperform existing models.

Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins

This paper introduces evotuning protocols for Transformer-based variant effect prediction, showing improved performance by incorporating domain architecture information from homologous sequences.

Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs

The paper improves protein structure classification by combining GmdRQA and HVG with reduced computational cost compared to state-of-the-art methods.

Structure-Based Function Prediction using Graph Convolutional Networks

This paper introduces a Graph Convolutional Network for predicting protein function and identifying important residues, showing improved accuracy over sequence-based methods.

LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

The paper introduces LSTM-PHV, a sequence-based model predicting human-virus protein-protein interactions using LSTM and word2vec, achieving high accuracy on imbalanced data.

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction

The paper introduces a method combining CNNs and GCNs to predict paratope residues in antibodies by integrating sequence and spatial information.

A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome

The paper develops a CNN-based tool to identify 6mA sites in rice genomes and shows it generalizes to other plant genomes.

Clustering FunFams using sequence embeddings improves EC purity

The paper improves the purity of protein functional families by clustering them using sequence embeddings, doubling the number of pure clusters per family.

ABC-finder: A containerized web server for the identification and topology prediction of ABC proteins

ABC-finder identifies and predicts the topology of ABC proteins across organisms using a Docker-based web server.

SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences

SubFeat predicts functionalities of DNA, RNA, and protein sequences using an ensemble classifier, outperforming existing methods on four datasets.

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

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.

DeepDigest: prediction of protein proteolytic digestion with deep learning

DeepDigest predicts proteolytic cleavage sites for eight proteases using a deep learning model, outperforming traditional methods across multiple datasets.

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

The paper trains a deep language model on 250 million protein sequences to learn biological representations that capture structural and functional properties.

TALE: Transformer-based protein function Annotation with joint sequence-Label Embedding

TALE is a deep learning model for protein function annotation that uses joint sequence-label embeddings to improve generalizability to novel sequences, species, and functions.

Exploring an experiment-split method to estimate the generalization ability in new data: DeepKme as an example

The paper introduces the experiment-split method to address overfitting in cross-validation by splitting data based on experimental sources, using DeepKme as an example to demonstrate its effectiveness.

Prediction of protein-protein interactions based on elastic net and deep forest

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.

Multiple probabilistic models extract features from protein sequence data and resolve functional diversity of very different protein families

ProfileView uses probabilistic models to classify protein sequences and resolve functional diversity across different families, outperforming existing methods.

Improving Generalizability of Protein Sequence Models with Data Augmentations

The paper improves protein sequence model generalizability by applying data augmentations and contrastive learning, showing performance gains across multiple tasks.

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

AIRBP uses machine learning to accurately identify RNA-binding proteins from sequence data, outperforming existing methods across multiple species.

MSA Transformer

One protein language model trained across many families on whole multiple sequence alignments, with interleaved row and column attention, reportedly outperforms unsupervised structure-learning methods by a wide margin.

Structure-aware Protein Solubility Prediction From Sequence Through Graph Convolutional Network And Predicted Contact Map

The paper develops a structure-aware model using graph convolutional networks and predicted contact maps to predict protein solubility from sequence, showing improved performance over existing methods.