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.
VLab4Mic is a simulation platform that predicts the structural resolvability of protein assemblies across various super-resolution microscopy modalities by modeling probe placement and steric constraints, thereby enabling researchers to assess experimental feasibility before conducting physical experiments.
THEOBROMA is an open database aggregating over 1.13 million natural products from 29 sources that distinguishes itself through per-compound license auditing, a three-tier classification system, and stereochemistry-aware deduplication to enable license-compliant virtual screening and isomer-specific bioactivity analysis.
The authors present gemsparcl, a highly scalable and efficient tool that clusters over 5.6 million bacterial genomes into 92,954 species-level genomic cohesive units in approximately 14 hours, thereby overcoming computational bottlenecks to enable comprehensive, reference-free analysis of prokaryotic diversity and taxonomy.
This study demonstrates that the biological meaning of protein language model embeddings is not fixed but is resolution-dependent, where different alignment levels to biological hierarchies selectively preserve distinct organizational relationships such as membership boundaries, functional groupings, or local family structures.
WitChi is a computationally efficient tool that uses empirical chi-squared testing to detect and iteratively prune compositionally biased sites from large-scale phylogenomic alignments, thereby restoring accurate phylogenetic topologies without the high computational cost of complex composition-aware models.
This study demonstrates that AlphaFold2 and ColabFold exhibit a very low false positive rate when predicting structures for non-protein sequences, while serendipitously revealing that some high-scoring predictions in noncoding human genomic regions correspond to previously unannotated pseudogenes, suggesting potential errors in existing gene annotations and validating the utility of high-scoring structural predictions for further investigation.
ADMETron is an AI-driven SaaS platform that integrates RNN-derived molecular embeddings with gradient boosting machines to predict 34 ADMET endpoints and features an interactive radar graph visualization tool, enabling comprehensive, high-throughput compound prioritization and data-driven decision-making in drug discovery.
MoE-Bind introduces the first sparse Mixture-of-Experts architecture for de novo protein binder generation, achieving faster, more efficient, and interpretable sequence-only design that matches or exceeds dense baselines without requiring prior structural information.
This paper introduces Latent Event Mapping (LEMING), a scalable and interpretable unsupervised modeling technique that reconstructs voxel-level trajectories of multi-modal neuroimaging changes in Alzheimer's disease, revealing new insights into progression-dependent mechanisms such as the late-stage association between acetylcholine receptor density and amyloid pathology.
The paper introduces STITCH, an interpretable method based on tangent principal component analysis in a Kendall shape manifold that effectively links size-independent cellular morphology to gene expression in spatial transcriptomics, outperforming existing deep learning approaches in identifying biologically relevant shape-transcriptome relationships across diverse datasets.