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.
The paper introduces *repare*, an open-source software tool that automatically reconstructs ancient pedigrees from pairwise kinship relations using iterative sampling and pruning, offering a flexible, fault-tolerant alternative to manual reconstruction that can incorporate user constraints and auxiliary data.
This paper presents a deep learning framework that utilizes positive-unlabeled learning to predict the identifiability of small molecule MS2 spectra from their MS1 context and acquisition parameters, effectively overcoming the challenge of missing negative labels to guide acquisition decisions and improve metabolite identification.
This study reveals that polar marine microbial communities serve as a previously overlooked reservoir of diverse, cold-active polyester-degrading enzymes and complete metabolic pathways, offering promising candidates for low-temperature plastic bioremediation.
This study proposes a hybrid stacking-bagging ensemble framework that integrates clinical, gene expression, and copy number variation data to achieve superior robustness and accuracy (ROC AUC of 0.9355) in breast cancer prognosis compared to unimodal and conventional stacking models.
This paper introduces Boltz-sample, a systematic strategy for the Boltz-2 model that modulates conformational sampling by uniformly rescaling latent pair representations, thereby enabling the efficient and transparent recovery of diverse protein alternative states and ensemble coverage without relying on heuristic input perturbations.
This chapter provides a comprehensive guide in R for analyzing clinical metabolomics and proteomics data, covering essential steps from quality control and differential expression to multi-omics integration and network-based enrichment analysis for biomarker discovery and systems-level disease understanding.
The paper introduces In-Pocket, a quantum mechanical optimization method biased by experimental data that enhances the chemical validation and structural reliability of protein-ligand complexes, particularly in cases of insufficient experimental resolution or ambiguous interaction chemistry.
This paper presents a new heuristic algorithm for the NP-hard minimum flow decomposition problem that extends equation-resolving mechanisms to jointly model all graph equations, enabling safe merge operations that iteratively simplify complex graphs to achieve near-optimal solutions significantly faster than integer linear programming formulations.
This paper presents a manually curated database and a computational pipeline that enable the precise, scalable annotation of halogenating enzymes across genomic and metagenomic data, thereby facilitating the discovery of nature's untapped reservoir of microbial halogenated natural products.
This paper presents Ultra-Content Screening (UCS), a novel high-dimensional single-cell proteomics method that uses cyclic immunostaining to analyze up to 40 markers in 100,000 blood cells, demonstrating its potential to reveal disease-specific immune profiles in acute myeloid leukemia for improved diagnostics and personalized medicine.