856 papers
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.
CellWHISPER is a statistically robust and computationally scalable framework that accurately infers direct, contact-mediated cell-cell communication from spatial transcriptomics data by disentangling true signaling interactions from structural proximity, enabling the discovery of tissue- and disease-specific signaling programs such as gap-junction coupling in the brain.
The paper introduces GATSBI, a graph attention-based framework that generates context-aware protein embeddings by integrating diverse biological data and employing task-aligned evaluation protocols, demonstrating superior generalization—particularly for understudied proteins—compared to existing methods that rely on biologically inappropriate data splits.
The paper introduces **muat**, a portable, transformer-based software distributed via Docker and Bioconda that enables accurate, transferable tumour classification and representation learning from somatic variants across diverse sequencing datasets and secure processing environments without requiring retraining.
PANDA is an end-to-end graphical pipeline that reconstructs contiguous single-molecule methylation patterns from targeted bisulfite amplicon sequencing data by linking unmerged paired-end reads, thereby enabling the analysis of epiallelic heterogeneity and long-range phasing that conventional site-wise averaging methods obscure.
OncoMORPHIA is a free, browser-based web platform that integrates 3D protein structure visualization with clinical, survival, and AI-driven functional annotations to enable researchers to explore cancer mutations across ten public databases without requiring specialized bioinformatics expertise.
DeepTrio is a deep learning-based variant caller that analyzes child-mother-father trios by learning directly from sequence data without explicit inheritance priors, achieving higher accuracy than DeepVariant across Illumina and PacBio HiFi platforms, particularly at lower coverages.
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.
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.
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.
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.