Machine Learning in Bioinformatics

AmyloDeep: pLM-based ensemble model for predicting amyloid propensity from the amino acid sequence

AmyloDeep predicts amyloid propensity from amino acid sequences using an ensemble of deep learning models, achieving high accuracy and robustness.

GeoEPred: A Multimodal Structure-Aware Geometric Deep Learning Framework for Gram-Negative Bacterial Secreted Effector Prediction with Sequence Semantics

GeoEPred improves effector protein prediction by integrating sequence and structural information to better model functional motifs and spatial conformations.

Information Leakage in Enzyme Substrate Prediction

This study identifies information leakage in enzyme-substrate prediction models and datasets, showing performance drops when leakage is removed.

Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes

This paper introduces DEFT, a method that combines sequence- and structure-based approaches to improve EC number prediction accuracy and enable high-throughput enzyme repertoire annotation.

Mechanistic Interpretability of Fine-Tuned Protein Language Models for Nanobody Thermostability Prediction

This paper fine-tunes a protein language model on nanobody thermostability data and uses sparse autoencoders to uncover biophysical rules governing stability, identifying both known and novel stabilizing features.

BioGraphX-RNA: A Universal Physicochemical Graph Encoding for Interpretable RNA Subcellular Localization Prediction

BioGraphX-RNA predicts RNA subcellular localization using physicochemical graph encoding, showing competitive performance and interpretability across mRNA, miRNA, and lncRNA.

Developing SCL2205 : A Protein Sequence-based Spatial Modelling Dataset for the Protein Language Model Frontier

The paper develops a high-quality dataset for protein sub-cellular localisation prediction, showing performance improvements and highlighting data leakage issues in existing models.

ICFinder: ion channel identification and ion permeation residue prediction using protein language models

ICFinder uses protein language models to identify ion channels and predict permeation residues, outperforming existing methods and providing a public resource.

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

This paper develops interpretable biophysical neural networks to predict transcriptional activation domains from protein sequences, revealing how residue properties influence both activation strength and protein abundance.

CLASPP: A unified model for predicting post-translational modifications

CLASPP is a unified deep learning model that predicts multiple PTM types from protein sequences, addressing data imbalance and improving prediction performance across organisms.

CoMR: an integrative scoring pipeline for Comprehensive Mitochondrial proteome Reconstruction across eukaryotes

CoMR improves mitochondrial proteome reconstruction by integrating multiple evidence sources, achieving better performance than standalone predictors in both model and non-model eukaryotes.

Can Large Language Models ''Read'' Biological Sequences? A Systematic Evaluation of In-Context Learning for Antibody Characterization

This study evaluates how large language models perform in-class learning for antibody classification using biological sequences, finding that few-shot prompting with diverse demonstrations achieves competitive results.

Preferential CDR masking in paired antibody language models improves binding affinity prediction

This paper shows that focusing on complementarity-determining regions (CDRs) in antibody sequences improves binding affinity prediction using region-aware pretraining strategies.

Decoding protein language models: insights from embedding space analysis

This paper introduces a framework for comparing embedding spaces in molecular biology, demonstrating its utility in pre-filtering machine learning tasks and identifying differential patterns across embeddings.

ViralMap: Predicting Features in Viral Proteins from Primary Sequence

ViralMap predicts ten annotation classes for viral proteins from primary sequences, achieving high performance on seven classes and generalizing across viral strains and families.

Systematic contextual biases in SegmentNT potentially relevant to other nucleotide transformer models

The paper identifies contextual biases in the SegmentNT model, showing how input sequence length and nucleotide position affect predictions and suggests ways to mitigate these biases.

How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning

This paper introduces EnzPlacer, a contrastive learning method that predicts enzyme function from sequence by placing it in known function space, even when the precise function is unknown.

Optimizing Protein Tokenization: Reduced Amino Acid Alphabets for Efficient and Accurate Protein Language Models

This study explores using reduced amino acid alphabets with sub-word tokenization in protein language models, showing improved efficiency with minimal performance loss.

ACCURATE PREDICTION OF ASPARAGINE DEAMIDATION IN BIOLOGICS USING ADVANCED MACHINE LEARNING MODELS

This study develops machine learning models to predict asparagine deamidation in proteins, showing improved accuracy over existing methods and reducing development costs.

AbTune: Layer-wise selective fine-tuning of protein language models for antibodies

AbTune improves protein language model performance for antibody-related tasks through depth-selective fine-tuning, outperforming baselines in structure prediction and mutation effect prediction.

Probe-Based Identification of Metal-Binding Sites Using Deep Learning Representations

PRIME is a deep learning framework that predicts metal-binding sites in proteins by integrating evolutionary and structural signals, achieving high accuracy across various metal ions.

LAMBDA: A Prophage Detection Benchmark for Genomic Language Models

The paper introduces LAMBDA, a benchmark to evaluate genomic language models' ability to detect prophage sequences across four complexity levels.

seqLens: optimizing language models for genomic predictions

This study develops seqLens, a genomic language model family, and evaluates its performance on phenotypic predictions using diverse pretraining datasets and tokenization strategies.

A Convolutional Deep Learning Approach to identify DNA Sequences for Gene Prediction

The paper presents a convolutional neural network for gene prediction using human genome sequences, achieving state-of-the-art results on genes associated with genetic disorders.

GUANinE v1.1 Reveals Complementarity of Supervised and Genomic Language Models

GUANinE v1.1 evaluates supervised and unsupervised genomic models, showing each excels on specific tasks and identifying tradeoffs in model performance and resource use.

A PLM-Based Method for Predicting Protein Ion Channel Modulators for Drug Discovery and Safety Evaluation

This paper introduces IonNTXpred, a PLM-based method for predicting ion channel modulators, achieving high accuracy and supporting drug discovery and safety evaluation.

EssTFNet: Integration of Adaptive Time - Frequency and DNA Language Models for Interpretable Human Essential Gene Prediction

EssTFNet predicts human essential genes using a deep learning framework combining adaptive time-frequency analysis and DNA language models, achieving high accuracy and interpretability.

SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites

SPPIDER-seq predicts protein-protein interaction sites by considering partner-specific context, outperforming AlphaFold3 on disordered interfaces and revealing partner-specific patterns in TP53 interactions.

Predicting unknown binding sites for transition metal based compounds in proteins

This paper evaluates two tools for predicting transition metal binding sites in proteins, showing they can identify known sites from apo structures but have limitations in complex cases.

Large-Scale Statistical Dissection of Sequence-Derived Biochemical Features Distinguishing Soluble and Insoluble Proteins

The paper performs a large-scale statistical analysis of sequence-derived biochemical features to distinguish soluble and insoluble proteins, finding that size-related features and negative charge proportion are the most discriminative.