bioinformatics papers | Gist.Science

Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.

On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.

Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.

💻 bioinformatics

Optimisation of Weighted Ensembles of Genomic Prediction Models in Maize

This study evaluates three weight optimisation approaches (linear transformation, Nelder-Mead, and Bayesian) for weighted ensembles of genomic prediction models in maize, finding that while these methods generally improve prediction accuracy over naive equal-weight ensembles—particularly when optimal weights differ significantly from equal distribution—no single approach demonstrated clear superiority across all scenarios.

Tomura, S., Powell, O. M., Wilkinson, M. J., Lefevre, J., Cooper, M.2026-04-02

💻 bioinformatics

Towards a Cytometry Foundation Model: Interpretable Sample-level Predictive Modelling via Pretrained Transformers

This paper introduces the Generalised Pretrained Cytometry Transformer (GPCT), an interpretable foundation model that leverages a novel pretraining regime to learn transferable cellular representations from heterogeneous marker panels, thereby overcoming scalability limitations and enabling high-accuracy, biologically validated sample-level predictions across diverse flow cytometry datasets.

Zhuang, Z., Mashford, B. S., Zheng, L., Andrews, T. D.2026-04-02

💻 bioinformatics

DESPOT: Direction-Enhanced Scoring POTentials

The paper introduces DESPOT, an anisotropic knowledge-based scoring framework that models directional preferences and steric exclusion by inverting the probabilistic formulation of atom interactions, thereby significantly outperforming traditional isotropic methods in protein-ligand pose discrimination and virtual screening.

Poelmans, R., Bruncsics, B., Arany, A., Van Eynde, W., Shemy, A., Moreau, Y., Voet, A. R.2026-04-02

💻 bioinformatics

When Multimodal Fusion Fails: Contrastive Alignment as a Necessary Stabilizer for TCR--Peptide Binding Prediction

The paper introduces TRACE, a multimodal framework that employs CLIP-style intra-entity contrastive alignment to stabilize TCR-peptide binding predictions by regularizing noisy structural data against strong sequence embeddings, thereby demonstrating that constrained modality interaction is more critical than naive fusion for robust bioinformatics performance.

Qi, C., Wang, W., Fang, H., Wei, Z.2026-04-02

💻 bioinformatics

Decoding antibiotic modes of action from multimodal cellular responses

This study introduces MAPPER, a scalable multimodal framework that accurately predicts antibiotic modes of action and detects novel mechanisms in *E. coli* by integrating proteomic, chemical, and growth data, thereby facilitating the prioritization of antibacterial candidates with distinct targets.

Hesse, J., Schum, D., Leidel, L., Gareis, L. R., Herrmann, J., Müller, R., Sieber, S. A.2026-04-02

💻 bioinformatics

EMITS: expectation-maximization abundance estimation for fungal ITS communities from long-read sequencing

EMITS is a high-performance Rust-based tool that utilizes an expectation-maximization algorithm to resolve ambiguous read mappings and improve species-level abundance estimation in fungal ITS communities from long-read sequencing, significantly outperforming naive counting methods in accuracy and error reduction.

O'Brien, A., Lagos, C., Fernandez, K., Ojeda, B., Parada, P.2026-04-02

💻 bioinformatics

TF-IDF k-mer-based Classical and Hybrid Machine Learning Models for SARS-CoV-2 Variant Classification under Imbalanced Genomic Data

This study demonstrates that a hybrid Random Forest-SVM framework utilizing TF-IDF-based k-mer features outperforms deep learning methods in classifying imbalanced SARS-CoV-2 genomic data, achieving superior macro-averaged F1-scores and robust detection of rare variants.

Haque, N., Mazed, A., Ankhi, J. N., Uddin, M. J.2026-04-02

💻 bioinformatics

Benchmarking Heritability Estimation Strategies Across 86 Configurations and Their Downstream Effect on Polygenic Risk Score Performance

This study systematically benchmarks 86 SNP heritability estimation configurations across six tool families and ten method groups, revealing that while heritability estimates vary substantially based on algorithmic choices and standardization, this upstream variability has minimal impact on downstream polygenic risk score performance, suggesting heritability should be treated as a configuration-sensitive parameter rather than a stable scalar.

Muneeb, M., Ascher, D.2026-04-02

💻 bioinformatics

Inferring a novel insecticide resistance metric and exposurevariability in mosquito bioassays across Africa

This study introduces a novel mathematical model that integrates intensity-dose susceptibility bioassay data to account for resistance heterogeneity in mosquito populations, enabling more accurate predictions of insecticide-treated net effectiveness and the public health impact of insecticide resistance across Africa.

Denz, A., Kont, M. D., Sanou, A., Churcher, T. S., Lambert, B.2026-04-01

💻 bioinformatics

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

This paper presents a highly efficient convolutional neural network (CNN) model that utilizes TFxIDF vectorization of amino acid sequences derived from Human Genome Build 38 to accurately predict gene locations, achieving state-of-the-art performance in identifying genes associated with genetic disorders.

Motta, J. A., Gomez, P. D.2026-04-01

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