1199 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.
This paper benchmarks 62 text summarization methods on 1,000 biomedical abstracts, revealing that general-purpose, medium-sized language models outperform both statistical extractive methods and specialized or frontier-scale models in generating accurate and semantically coherent scientific summaries.
The paper introduces FUSED, a novel multivariate functional representation that jointly models the structural and elemental compositional information of protein ligand binding sites as continuous functions of distance, demonstrating that this approach yields compact, interpretable, and competitive predictive performance for ligand discrimination and binding-site classification compared to existing methods.
This study employs an integrative computational toxicology framework to demonstrate that PFOS and PFHxS exposure drives inflammatory keratinocyte programs that converge with psoriasis pathology, identifying specific molecular effectors and spatial niches through multi-omics integration and virtual perturbation.
This study presents the computational design and validation of a novel multi-epitope vaccine candidate targeting three previously unexplored *Mycobacterium tuberculosis* virulence proteins (EccB3, MycP, and polyketide synthase), demonstrating strong structural stability, effective TLR binding, and robust immune responses through a comprehensive reverse vaccinology pipeline.
This study demonstrates that the choice of taxonomic profiler, reference database, and parameterization significantly influences alpha diversity estimates and statistical conclusions in metagenomic analyses, underscoring the critical need for researchers to conduct sensitivity analyses to ensure robust and reliable scientific findings.
This paper introduces improved 3D radial phyllotaxis trajectories, specifically UPhy and FlexiPhy, which achieve uniform readout direction density and utilize randomized interleave ordering to significantly reduce ringing artifacts and enhance the robustness of retrospective sequential binning in dynamic MRI.
SSUplex is a fast, open-source Rust tool that detects, extracts, and origin-sorts full-length small-subunit rRNA reads from both DNA strands into five categories (bacteria, archaea, eukaryota, mitochondria, and chloroplast), offering a high-speed, memory-efficient alternative to Metaxa2 for environmental DNA metabarcoding workflows.
The paper introduces TEDlm, a domain-centric protein language model pretrained on structurally defined domain segments that outperforms larger full-sequence models in remote-homology detection and molecular function prediction, demonstrating that focusing on domain-intrinsic signals yields compact, structurally informed representations.
The amR R package suite offers a comprehensive, interpretable framework for predicting antimicrobial resistance in bacterial pathogens by integrating multi-scale genomic feature extraction, machine learning model training, and interactive visualization to uncover cross-species and multi-drug resistance mechanisms.
This paper evaluates AlphaFold3's performance across diverse biomolecular applications, finding that while it offers powerful all-atom modeling capabilities, its accuracy and reliability are uneven and heavily dependent on training-set overlap, necessitating cautious interpretation compared to its predecessor.