1210 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 introduces DeepMDTOMO, a supervised deep learning framework that efficiently predicts atomic-scale conformational landscapes of biomolecules from noisy cryo-electron tomography data, overcoming the computational limitations of traditional physics-based flexible fitting methods.
The paper introduces SomaSV, a cost-effective hybrid sequencing framework that combines tumor long-read data with matched normal short- and long-read sequencing to achieve superior somatic structural variant detection accuracy and lower costs compared to state-of-the-art methods, while identifying clinically relevant cancer biomarkers.
This study utilizes in silico transcriptomic analysis to identify shared immune-associated molecular signatures and key genes between Hashimoto's thyroiditis and type 2 diabetes, subsequently prioritizing three candidate drugs (gliquidone, oleanolic acid, and glipizide) for potential repurposing to address the comorbidity.
This study systematically evaluates transfer learning strategies for predicting clinical chemotherapy response using TCGA data, revealing that most direct transfer methods fail to improve performance while hybrid approaches and model fine-tuning offer more reliable gains.
The paper introduces rustybam, a Rust-based command-line toolkit for CIGAR-aware manipulation of PAF and BAM alignments, and SafFire, a browser-based tool for interactive genome comparison visualization, both of which are open-source and designed to facilitate alignment analysis and structural variant exploration.
The paper introduces ExCALIBR, a semi-supervised framework that calibrates high-throughput functional assay data using skew normal mixtures to generate variant-specific probabilities of pathogenicity, thereby overcoming the subjectivity of current gene-specific thresholds and significantly reducing variants of uncertain significance across 80 datasets.
The paper introduces resLens, a family of genomic language models that outperform traditional alignment-based methods in detecting antibiotic resistance genes by leveraging latent genomic representations to identify sequences dissimilar to known references.
This paper introduces rbio1, a biological reasoning model trained via reinforcement learning using biological world models as soft verifiers to simulate experiments, thereby achieving state-of-the-art performance in perturbation prediction and zero-shot transfer to disease-state tasks without requiring costly experimental data.
DupyliCate is a versatile, high-throughput Python tool designed to identify, classify, and characterize gene duplications across diverse species, as demonstrated through its application to various plant and non-plant genomes and specific evolutionary case studies.
This study introduces FragBEST-Myo, a deep learning-based semantic segmentation tool that utilizes fragment binding propensity maps derived from molecular dynamics to accurately characterize cardiac myosin binding sites and effectively screen apo-state conformations for ensemble docking.