💻 Category

bioinformatics

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.

💻 bioinformatics

AI4Life Open Calls and Public Challenges: why, how, and what we have learned.

Through the execution of three Open Calls and three Public Challenges between 2023 and 2025, the AI4Life initiative supported 22 bioimage analysis projects and revealed that while FAIR deep learning adoption faces significant implementation hurdles, the primary bottleneck for scientific AI in biology lies not in method development but in the availability of data, annotations, and shared infrastructure.

Galinova, V., Seifi, M., Serrano Solano, B., Lidayova, K., Dalle Nogare, D., Corbat, A. A., Talks, J., Giacomello, E., G (…)2026-07-30
💻 bioinformatics

Spectronaut-nf: A Nextflow Pipeline for Parallel Processing of DIA Data with Spectronaut

Spectronaut-nf is a scalable Nextflow pipeline that enables efficient, parallelized processing of large-scale DIA proteomics datasets on high-performance computing environments, significantly reducing analysis time while maintaining consistent identification results compared to single-workstation or single-node setups.

Kotimoole, C. N., Arefian, M., McKay, E. C., Kasaragod, S., Skoraczynski, G., Collins, B. C.2026-07-30
💻 bioinformatics

A geometric-to-neural cascade for cerebral microbleed detection in susceptibility-weighted MRI

This paper presents a fully automated, three-stage geometric-to-neural cascade pipeline that combines subject-adaptive unsupervised candidate generation with two lightweight 3D ResNet classifiers trained on minimal human-in-the-loop labels to achieve high-accuracy detection of cerebral microbleeds in susceptibility-weighted MRI while significantly reducing false positives from vascular and non-vascular mimics.

Bogdanov, S., Rudravaram, G., Saunders, A. M., Kim, M. E., LeFevre, J., Charles, J., Jain, S., Schrag, M. S., Landman, B (…)2026-07-28
💻 bioinformatics

Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics

Spaceland is a morphology-guided computational framework that reconstructs continuous, high-resolution 3D molecular atlases of whole organs by integrating sparse spatial transcriptomics with serial H&E histology, thereby enabling scalable virtual tissue modeling without the need for exhaustive experimental sampling.

Xu, F., Zhuang, Z., Zhu, Y., Ying, B., Hou, N., Lin, W., Wang, L., Yang, C., song, j.2026-07-27
💻 bioinformatics

Estimation of biological age using HRV data: comparison of the Klemera-Dubal method with the multiple linear regression method

This study compares multiple linear regression (MLR), bias-corrected MLR, and the Klemera-Doubal Method (KDM) for estimating biological age from heart rate variability data in 343 subjects, finding that KDM yields the lowest estimation error while the bias-corrected MLR offers improved accuracy over uncorrected MLR but is limited by its reliance on chronological age.

Pysaruk, A.2026-07-27
💻 bioinformatics

GPSNorm: Gaussian Process Spatial Normalization for Spatial Transcriptomics

GPSNorm is a novel Bayesian spatial normalization framework that jointly models technical variation, spatial structure, and biological signal using a hierarchical Gaussian process approach to propagate normalization uncertainty into downstream differential expression analysis, thereby improving the robustness and interpretability of spatial transcriptomics data.

Taychameekiatchai, A., Zhan, X., Xiao, G., Ruan, P.2026-07-27
💻 bioinformatics

deepthought: the microscopy acquisition stack as an object of study

This paper presents "deepthought," a modular microscopy acquisition stack that standardizes common components like device access and storage while isolating application-specific interpretation and targeting logic, thereby enabling autonomous, analysis-in-the-loop imaging across diverse biological scenarios such as high-throughput screening and long-term live-cell tracking.

Kesavan, P. S., Devadasan, S., Bohra, D.2026-07-26