Machine Learning in Bioinformatics

This paper proposes using deep learning embeddings to transfer GO annotations based on protein sequence proximity rather than homology, achieving performance comparable to existing methods.

The paper introduces NetGPI, a new method for predicting GPI-anchored proteins using recurrent neural networks with attention mechanisms, achieving better performance than existing methods.

The paper demonstrates that using universal contextualized protein embeddings from SeqVec allows effective cross-species molecular function prediction, especially for understudied species.

This paper proposes a sequence-based method for predicting antibody paratopes by integrating local-global sequence information and partner antigen features, achieving better performance than existing methods.

This paper reviews the application of representation learning in biological sequence analysis, focusing on how embedding techniques transform sequences for functional and structural insights.

The paper introduces a lightweight deep learning method using protein language model embeddings to predict protein subcellular localization, outperforming state-of-the-art methods without using multiple sequence alignments.

PPIMem predicts transmembrane protein-protein complexes by identifying interaction motifs from 3D structures and searching them in databases, predicting 21,544 complexes across 39 species.

The paper develops an ensemble system combining random forest and neural network models to predict protein functions using sequence and taxonomic features, showing competitive performance in CAFA3 evaluations.

The paper introduces a kmerRP2vec feature representation method combined with CNN and RNN to predict miRNA subcellular locations, outperforming existing methods on a public dataset.

This study develops DeepCSO, a deep-learning tool for predicting Cysteine S-sulphenylation sites across species, showing improved performance over existing methods.

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.

The paper introduces transformer networks for sequence labeling in genomics, achieving state-of-the-art performance on tasks like transcription start site annotation in E. coli.

The paper introduces FunSite, a machine learning method for predicting functional sites in proteins using sequence, structure, and evolutionary data from CATH functional families, showing superior performance over existing methods.

This paper develops a machine learning method to predict the thermal dependence of xylanases from metagenomic data, identifying three novel xylanases from rumen microbiota.

This paper introduces TNFPred, a model that identifies tumor necrosis factors from cytokine sequences using hybrid features based on word embeddings, achieving high accuracy in classification.

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

This study introduces T4SE-XGB, an XGBoost-based model for predicting type IV secreted effectors from protein sequences, achieving high accuracy and interpretability.

The paper introduces DeepKhib, a deep-learning framework for predicting lysine 2-hydroxyisobutyrylation sites, showing improved performance over existing methods across multiple species.

FastSK improves the speed and scalability of gapped k-mer kernel computations for sequence analysis tasks, outperforming existing methods in both speed and accuracy.

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.

This paper introduces a deep neural network for predicting essential genes in microbes using only protein sequences, outperforming existing methods that rely on structural or topological features.

This work introduces FFPred-GAN, a GAN-based method to generate synthetic protein feature samples, improving function prediction accuracy across all three Gene Ontology domains.

DELPHI is a deep learning model that improves the accuracy of predicting protein-protein interaction sites by using an ensemble structure and novel features.

AE-LGBM predicts protein-protein interactions using an ensemble of autoencoder and LightGBM, achieving high accuracy across multiple organisms and PPI networks.

The paper shows that unsupervised protein sequence embeddings outperform traditional features in predicting molecular function, with a simple model achieving state-of-the-art results.

The paper predicts the receptorome for the human-infecting virome using a random-forest model and protein-protein interaction data, identifying 1380 human cell membrane proteins and 693 virus-specific receptors.

This study improves enzyme optimum temperature prediction by applying ensemble learning and resampling strategies to address data imbalance, reducing error on high Topt values and increasing overall R2.

The paper builds a database of nucleic acid binding proteins and predicts their functions using machine learning with 77.43% accuracy.

The paper introduces iPromoter-BnCNN, a branched CNN model for identifying and classifying six types of sigma promoters, showing superior performance on benchmark and independent test datasets.