855 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.
VaxjoOnto is a novel framework that leverages a vaccine ontology-driven heterogeneous knowledge graph and a graph neural network to effectively prioritize adjuvants for both known and novel diseases, addressing a critical bottleneck in vaccine development by shifting focus from antigen discovery to adjuvant selection.
This paper introduces the SBB (Signal, Bounds, and Baselines) framework to rigorously evaluate virtual cell perturbation models, revealing that complex deep learning methods often fail to meaningfully outperform simple linear baselines and highlighting the need for standardized metrics to distinguish genuine biological signal from statistical artifacts.
This study establishes a best-practice framework for constructing simultaneous multi-omics weighted gene co-expression networks to analyze thyroid toxicity and recovery in a rodent model, demonstrating that concatenating unscaled omics layers preserves biological structure while revealing extensive molecular disruption and partial restoration through complementary module preservation and differential connectivity analyses.
The GAE-Δ framework leverages a graph autoencoder to model phenotype-specific gene network rewiring across multi-omics data, achieving superior clinical outcome prediction and identifying biologically relevant cancer drivers compared to existing linear factorization and network-based methods.
The paper introduces ALARMIST, a probabilistic framework that decodes interpretable multicellular communication motifs from spatial transcriptomics data to identify higher-order signaling patterns and their downstream phenotypic impacts, demonstrating its utility in uncovering microenvironmental drivers of tumor progression in lung adenocarcinoma and glioblastoma.
This paper presents a systematic optimization of the CycloneSEQ platform, integrating experimental sample processing with computational assembly workflows to overcome the limitations of short-read sequencing and significantly improve the recovery of complete microbial genomes from long-read gut metagenomics.
This paper reveals that homology-induced data leakage systematically inflates the performance of genome-trained sequence models by causing them to rely on memorized associations rather than generalizable principles, and proposes the hashFrag tool to enable homology-aware data partitioning for more reliable evaluation and improved model generalizability.
This study introduces a systematic computational framework that integrates time-resolved phosphoproteomics with a BFS-guided Beam Search algorithm to reconstruct cell-type-specific EGFR signaling networks, successfully revealing how SHP2 inhibition rewires pathway architectures and drives adaptive resistance mechanisms.
This paper introduces a provenance-aware Text-to-Target framework that integrates schema-constrained large language model retrieval with numeric omics data analysis to generate interpretable, audit-ready disease targets and drug discovery strategies, demonstrating significant validation in Alzheimer's disease and pancreatic ductal adenocarcinoma.
This study integrates multi-omics data to develop and validate a streamlined 15-gene prognostic signature and a clinical-genomic nomogram that accurately predict survival outcomes in triple-negative breast cancer patients.