Machine Learning in Bioinformatics

The paper presents serverless deep learning models for predicting peptide properties, achieving results comparable to state-of-the-art methods without requiring cloud infrastructure.

The paper compares sequence-based and structure-based methods for predicting protein function, finding that structure-based approaches can offer better accuracy when using high-quality protein structures.

NetGO 3.0 integrates a protein language model with NetGO 2.0 to improve functional annotations by leveraging unannotated proteins.

SPROF-GO predicts protein functions from sequences using a pretrained language model and label diffusion, outperforming existing methods by over 10% in AUPR.

DRPbind predicts DNA, RNA, and protein-binding residues in disordered protein sequences using SVM and features from evolutionary, physicochemical, and structural data.

This study introduces two models, PromoR and PromoS, for promoter recognition and classification in E. coli, achieving accuracies of 0.887 and 0.781 respectively.

This paper evaluates prompt tuning as a cost-efficient alternative to fine-tuning protein language models for sequence generation, finding it more efficient in resource usage but less effective than fine-tuning in sequence quality.

ProteInfer uses deep convolutional neural networks to predict protein functions from amino acid sequences, offering efficient and accurate predictions with a lightweight software interface.

MsPBRsP predicts protein binding residues using a language model without MSA tools, achieving higher accuracy and faster performance than existing methods.

This paper introduces a method to improve protein representation learning by retrieving and integrating similar sequences without alignment, achieving faster and more accurate predictions than traditional methods.

HMMSTRTM predicts transmembrane protein topology using a Hidden Markov Model, achieving high accuracy in residue prediction.

GO Bench provides a benchmarking hub for evaluating machine learning models in protein function annotation using Gene Ontology terms.

The paper evaluates pre-trained language models for antibody tasks, introduces an evaluation benchmark, and investigates their performance and benefits in real-world applications.

The paper introduces KeAP, a knowledge-enhanced auto-encoder for protein representation learning that outperforms existing methods on multiple downstream tasks.

DeepBIO is a deep-learning platform that automates biological sequence analysis, offering 42 algorithms and visualization tools for functional prediction and annotation.

This paper develops a fast method for predicting multi-label subcellular localization of mRNA at genome scale, achieving high AUROC scores using hybrid techniques.

Protein-specific tuning of masking, depth and positional embeddings, rather than more parameters, yields an encoder-decoder model that tops structure and function benchmarks at a fraction of the compute.

ISPRED-SEQ predicts protein-protein interaction sites from sequences using deep neural networks and outperforms existing methods.

This study compares ensemble and non-ensemble machine learning methods for predicting ubiquitination sites, finding ensemble methods generally outperform non-ensemble ones.

MetaLLM predicts metal binding sites in proteins using a transformer-based model trained on protein sequences, achieving over 90% precision for common metal ions.

Hist2Vec is a kernel-based embedding method for biological sequence classification that outperforms existing methods with >76% and >83% accuracy on DNA and protein datasets, respectively.

The paper introduces CATHe, a method using protein language model embeddings to detect remote homologues for CATH superfamilies, achieving high accuracy and validating predictions with AlphaFold structures.

This paper introduces ProteinNPT, a non-parametric transformer for protein property prediction and design, showing improved performance in label-scarce and multi-task settings.

The paper addresses imbalanced multilabel classification for protein subcellular localization using oversampling techniques to improve minority class prediction performance.

PROSTATA predicts protein stability changes from point mutations using a transformer-based model, outperforming existing neural network approaches.

WUREN is a multimodal model that fuses sequence, graph, and structural features to predict protein interaction interfaces and antigen epitopes, achieving state-of-the-art performance.

ProtLM.SCL is a protein language model that improves subcellular localization prediction for Gram-negative bacteria proteins, outperforming PSORTb in accuracy and precision.

The paper shows that codon-based language models outperform amino acid sequence models in protein engineering tasks like species recognition and melting point estimation.

This paper introduces nucleotide augmentation (NTA) to improve machine learning models for protein engineering by generating biologically meaningful sequence data, achieving performance gains even with limited training data.

The paper analyzes how a pre-trained BERT model processes nucleotide sequences, identifies k-mer embeddings as key, and proposes an accelerated pre-training method.