Machine Learning in Bioinformatics

The paper uses MitoPredictor to predict and analyze the mitochondrial proteome of the ctenophore Mnemiopsis leidyi.

The paper introduces Domain-PFP, a method for protein function prediction using function-aware domain embeddings, which outperforms existing methods and large-scale protein language models in GO prediction tasks.

This paper introduces a small-scale protein language model that achieves comparable performance to larger models with significantly reduced computational requirements.

This paper investigates using a fractal-based image representation and deep learning for predicting protein binding sites but concludes the approach is ineffective.

CoCoNat predicts coiled-coil boundaries, register annotations, and oligomerization states using deep learning, outperforming existing methods on multiple metrics.

This paper develops a hybrid model combining motif-based and machine learning approaches to predict exosomal proteins with high accuracy, achieving AUROC of 0.85 on an independent dataset.

This study evaluates in silico tools for predicting molecular functions of orphan proteins, finding most methods are descriptive rather than predictive.

This paper introduces TurNuP, a model predicting enzyme turnover numbers for natural reactions, outperforming existing methods and generalizing across enzyme similarity.

The paper creates a large, high-quality database of bacterial transmembrane beta barrels using co-evolution data and presents a new algorithm with high accuracy for identifying these proteins.

DNAGPT pre-trains a modified generative pre-trained transformer (GPT) architecture on over 200 billion mammalian base pairs to jointly process sequence and numerical data across diverse DNA analysis tasks.

SPAT annotates proteins based on their likelihood to be at the cell surface using existing annotations, showing good performance on leukemia and public datasets.

This paper introduces CoupleNet, a model that integrates protein sequence and structure to improve representation learning, achieving better performance in fold classification and function prediction.

A 100-billion-parameter protein language model trained with masked and autoregressive objectives together, topping prior results on 15 of 18 benchmarks and powering a fast structure predictor.

T4SEpp predicts bacterial type IV secreted effectors using protein language models and integrates multiple modules, achieving high sensitivity and specificity.

This paper benchmarks deep learning-based embeddings for predicting peroxisomal protein-protein interactions using sequence data and integrates AlphaFold2-Multimer predictions for improved accuracy.

This paper fine-tunes protein embeddings to improve interpretable function prediction, showing that fine-tuned models outperform pre-trained ones in both known and novel function classification.

This paper evaluates how different performance metrics and generalization definitions affect the assessment of protein regression models.

This paper demonstrates that large language models can predict protein-protein interactions with high accuracy using synthetic data and fine-tuned models.

This paper introduces genomicBERT, a lightweight genome analysis model that improves efficiency and performance through specialized DNA vocabulary and tokenization, validated across diverse case studies.

NRPreTo predicts Nuclear Receptor proteins and their subfamilies using enhanced features, achieving high performance and identifying 59 novel human NRs.

ULDNA improves DNA-binding site prediction using a LSTM-attention network with pre-trained language models, achieving higher accuracy than existing methods on 1287 proteins.

TSignal is a transformer model that predicts signal peptides and their cleavage sites, showing improved accuracy across various organisms and peptide types.

This paper introduces TransFun, a method that combines protein sequences and structures using transformers and equivariant graph neural networks to improve protein function prediction.

NTpred improves the identification of tyrosine nitration sites in protein sequences using advanced feature extraction and ensemble classifiers, achieving better performance than existing methods on benchmark datasets.

The paper develops a machine learning model to identify phage depolymerases with 90% accuracy using amino acid features.

CysPresso predicts recombinant expression of cysteine-dense peptides using deep learning protein representations, finding AlphaFold2 features most effective for this task.

AnnoPRO improves protein function annotation by using image-like representations and multimodal deep learning, showing better performance for rare protein families.

The paper develops a machine learning model to classify bacterial nanowire proteins using feature engineering, achieving high accuracy with XGBoost.

The paper evaluates how language models, ensemble learning, and sampling methods affect protein fitness prediction using binding affinity and thermal stability datasets.

AcrNET predicts anti-CRISPR proteins using a deep learning model, achieving significant performance improvements and class-level predictions.