858 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.
PMGen is an integrated framework that leverages AlphaFold2 with novel strategies to achieve state-of-the-art structural prediction for variable-length peptide-MHC complexes, thereby enabling high-fidelity structure-guided peptide design and the generation of large-scale datasets to train advanced immunological models.
This paper introduces PaNDA, a software package featuring a novel polynomial-time algorithm to efficiently maximize phylogenetic diversity in phylogenetic networks with bounded scanwidth, while also extending the diversity definition to semi-directed networks and proving the NP-hardness of the optimization problem in that context.
This study demonstrates that modeling plasma proteomics data as compositional systems using pairwise protein ratios significantly outperforms traditional abundance-based approaches in predicting human diseases, achieving substantial accuracy gains in Alzheimer's subtypes and across a broad spectrum of conditions in the UK Biobank.
KuPID is a kmer-based preprocessing tool for long RNAseq reads that accelerates novel isoform discovery by using kmer sketching to pseudo-align reads and filter out extraneous data, thereby significantly improving both pipeline speed and accuracy.
The paper introduces GaugeFixer, a scalable Python package that resolves parameter non-identifiability in sequence-function models through linear-time gauge-fixing, enabling the biologically meaningful interpretation of complex landscapes with millions of parameters.
This paper demonstrates that GFlowNet outperforms traditional reinforcement learning methods like GRPO in generating structurally diverse therapeutic peptides by inherently achieving uniform sequence sampling through proportional reward sampling, thereby eliminating the need for explicit diversity penalties and preventing mode collapse.
This paper demonstrates that distilling a large protein language model into compact students using two complementary protein-specific regularizers—uncertainty-aware position weighting and calibration-aware label smoothing—significantly improves inference speed, memory efficiency, and sample efficiency on scarce data, enabling high-quality domain adaptation on consumer-grade hardware.
This paper introduces ARCH3D, a foundation model for global genome architecture that utilizes a novel masked locus modeling task to capture genome-wide spatial structures and enable the reconstruction of complex chromosomal interactions, ultimately serving as a structural basis for simulating genome behavior.
This study demonstrates that RNA foundation models significantly improve the generalizability of endometriosis classification across independent cohorts compared to traditional baselines, while a novel interpretability method (CA-IG) reveals stable, biologically plausible gene-level signatures that overcome the instability of standard explainability approaches.
PhyMapNet is a computationally efficient, phylogeny-guided Bayesian framework that infers robust and interpretable microbiome networks by integrating evolutionary information and employing a tuning-free consensus strategy to overcome data sparsity and hyperparameter sensitivity.