bioinformatics papers | Gist.Science

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

A Hierarchy-aware Gene Exploration Platform for Multi-layered Toxicogenomic Analysis: A Case Study on Acetaminophen-induced Hepatotoxicity

This paper presents a hierarchy-aware gene exploration platform that integrates HGNC biological knowledge into a hyperdiffusion-based similarity kernel to significantly enhance the interpretability and functional coherence of transcriptomic analysis, as demonstrated by its successful application in identifying key toxicological modules in acetaminophen-induced hepatotoxicity.

Kim, M., Cui, Y., Kim, M. G.2026-04-14

💻 bioinformatics

Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

This study systematically benchmarks single-cell drug response models across diverse datasets, revealing that while scDEAL demonstrates superior robustness to class imbalance, most current methods struggle to predict intrinsic pre-treatment resistance despite effectively capturing post-treatment transcriptional changes, thereby highlighting the need for next-generation models with greater clinical relevance.

Shen, L., Sun, X., Zheng, S., Hashmi, A., Eriksson, J., Mustonen, H., Seppänen, H., Shen, B., Li, M., Vähä-Koskela, M (…)2026-04-14

💻 bioinformatics

found: Inferring cell-level perturbation from structured label noise in single-cell data

This paper introduces **found**, a flexible Python and R implementation of the HiDDEN method that enables robust inference of cell-level perturbations from structured batch-level label noise in single-cell data through customizable pipelines and systematic benchmarking of modeling choices.

Afanasiev, E., Goeva, A.2026-04-14

💻 bioinformatics

Reconstructing intra-tumor fitness landscapes from scSeq CNA genotypes via simulation-based Bayesian inference and Deep Learning

This paper introduces CloneMLP-NPE, a likelihood-free, simulation-based framework utilizing neural posterior estimation and normalizing flows to accurately infer intra-tumor selection coefficients from single-cell CNA genotypes, outperforming both set-based and consensus-based baseline approaches.

KafiKang, M., Skums, P.2026-04-14

💻 bioinformatics

BioClaw: Human-Bot Research Collaboration Ecosystems in Group Chats

This paper presents BioClaw, a human-bot collaboration ecosystem that transforms group chats into persistent, secure workspaces by converting natural language requests into automated, tool-grounded biomedical analyses executed within isolated Docker containers.

Xu, M., Yan, J., Feng, R., Cai, Q., Zhang, P., Zhao, C., He, C., Wei, Z., Li, J., Lin, S., Dong, H., Jin, R., Hou, T., L (…)2026-04-14

💻 bioinformatics

GraphMana: graph-native data management for population genomics projects

GraphMana introduces a graph-native data management system for population genomics that replaces fragmented file-based workflows with a persistent database to enable incremental sample addition, provenance tracking, and efficient multi-format export, as demonstrated by its ability to complete a complex 46-operation lifecycle for the 1000 Genomes Project in under two hours.

Estaji, E., Zhao, S.-W., Chen, Z.-Y., Nie, S., Mao, J.-F.2026-04-14

💻 bioinformatics

GraphPop: graph-native computation decouples population genomics complexity from sample count

GraphPop is a graph-native engine that decouples population genomics complexity from sample count by pre-aggregating allele counts into a graph database, enabling O(V x K) analysis with massive speedups and constant memory usage while facilitating complex, annotation-conditioned queries across diverse species.

Estaji, E., Zhao, S.-W., Chen, Z.-Y., Nie, S., Mao, J.-F.2026-04-14

💻 bioinformatics

TB-Bench: A Systematic Benchmark of Machine Learning and Deep Learning Methods for Second-Line TB Drug Resistance Prediction

This study presents TB-Bench, a systematic benchmark evaluating 20 machine learning and deep learning models across 14 second-line tuberculosis drugs on a large WHO dataset, revealing that traditional machine learning methods often outperform deep learning in internal tests while both struggle with cross-dataset generalization compared to catalogue-based approaches.

VP, B., Jaiswal, S., Meshram, A., PVS, D., S C, S., Narayanan, M.2026-04-13

💻 bioinformatics

IMAS enables target-aware integration of tumour multiomics to resolve communication-guided regulatory mechanisms

The paper introduces IMAS, a target-aware integrative framework that leverages pan-cancer single-cell resources to augment sparse tumour multiomic data, reconstruct structured regulatory networks, and prioritize communication-guided mechanisms for interpretable discovery in data-limited cancer systems.

Deyang, W., Yamashiro, T., Inubushi, T.2026-04-13

💻 bioinformatics

Introducing the digital PCR data essentials standard to harmonize data structure for clinical and research use

To address interoperability challenges caused by proprietary dPCR software, this paper introduces the Digital PCR Data Essentials Standard (DDES), a community-developed, cross-platform file format designed to harmonize data structures and enable FAIR, reproducible, and collaborative clinical and research applications.

Trypsteen, W., Vynck, M., Untergrasser, A., Whale, A. S., Rodiger, S., Dobnik, D., Bogozalec Kosir, A., Milavec, M., Kub (…)2026-04-13

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