1200 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 study reveals that both archaellum-based motility and F1-type chemosensory systems were likely transferred from Archaea to Chloroflexota bacteria via horizontal gene transfer, establishing a shared evolutionary trajectory between these distinct domains.
CardioSafe is a multi-task neural network that integrates chemical and transcriptomic features to predict cardiac ion channel activity, demonstrating superior performance over existing methods once a reverse-leak audit revealed and removed training-data contamination that had previously inflated benchmark results for Nav1.5 and Cav1.2 channels.
This study utilizes unbiased molecular dynamics simulations to quantify the insertion energetics of 28 amino acid analogs in a POPC bilayer, generating depth-dependent potentials of mean force that successfully reproduce experimental hydrophobicity scales and elucidate the thermodynamic roles of protonation states and aromatic orientation.
CausalKnowledgeTrace is a scalable, Python-based computational framework that automates the construction of evidence-based causal graphs from biomedical literature to systematically identify confounders and bias structures for improved causal inference in observational studies.
The paper introduces MucOneUp, a specialized simulation framework designed to generate benchmark datasets for evaluating the performance of variant calling tools in detecting MUC1-VNTR frameshift mutations across multiple sequencing platforms.
This study demonstrates that the lack of standardized methodology in antibiotic resistance gene detection leads to massive discrepancies among pipelines, causing the same metagenomic data to yield conflicting biological interpretations and underscoring the need for researchers to carefully justify and communicate their chosen analytical approaches.
This study demonstrates that open-weight large language models, particularly Qwen2.5-32B-Instruct, can effectively predict synthetic lethal interactions by leveraging pre-trained biological knowledge to outperform random chance and non-LLM methods, offering a scalable and interpretable alternative for prioritizing novel therapeutic targets in cancer.
This paper introduces Latent-Aligned Multiview Normalizing (LAMNr) flows, a deep learning framework that learns shared latent subspaces across heterogeneous multimodal datasets to enable exact-likelihood modeling, closed-form cross-view imputation, and a computational anatomy interpretation of population templates and geodesic interpolation, supported by a comprehensive open-source PyTorch implementation integrated with the ANTsX ecosystem.
This study introduces the Cadence model, a Narrative Velocity framework utilizing self-distilled PubMedBERT embeddings within a residual MLP, which demonstrates statistically significant improvements in next clinical event prediction accuracy and time-to-event regression over strong baselines on the MIMIC-IV dataset while highlighting specific calibration and generalization challenges.
This study benchmarks six Oxford Nanopore read simulators against R10.4.1 data, finding that while PBSIM3 excels at replicating general read-level properties, no tool fully captures the complex error profiles of real data, suggesting that the optimal choice depends on whether read-level realism or specific error structures are more critical for a given application.