858 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.
The paper introduces MAP, a knowledge-driven framework that integrates a large-scale biological knowledge graph and contrastive learning to generate mechanism-aware embeddings, enabling accurate zero-shot prediction of single-cell responses to unprofiled drugs and improving generalization over existing baselines.
SpaMOAL is a graph-based contrastive learning method that integrates spatial coordinates, histological images, and multi-omics molecular profiles to accurately identify spatial tissue domains, outperforming existing approaches in benchmarking studies.
This study introduces an optimal transport-based framework that reconstructs continuous CD8 T cell differentiation and migration trajectories across tissues during viral infection, revealing distinct waves of intestinal entry and identifying AP4 as a key regulator distinguishing circulating from tissue-resident memory fates.
The paper introduces **keju**, a hierarchical statistical model that significantly improves the sensitivity and false positive rate control of Massively Parallel Reporter Assay (MPRA) inference by accounting for batch-specific and modality-specific uncertainties through conditioning on DNA counts.
This study benchmarks various model-free sure screening methods for high-dimensional omics classification, demonstrating that the BcorSIS approach offers the most effective and computationally efficient feature selection across multiple biomedical datasets, including those related to type 1 diabetes.
GeCCo introduces a mathematically grounded framework that constructs cell identities by projecting cells onto a rigorously derived hierarchy of gene programs, thereby resolving the geometric inconsistency of existing clustering methods to achieve superior hierarchical consistency and reveal novel biological states in single-cell transcriptomics.
The paper introduces bionSBM, a graph-based topic modelling method that integrates single-cell multi-omic data to achieve superior clustering performance and biological interpretability compared to state-of-the-art approaches.
ITSxRust is a high-performance Rust-based tool that significantly improves the throughput, accuracy, and robustness of ITS region extraction for long-read fungal amplicon sequencing by integrating efficient HMMER searches with a novel partial-chain recovery strategy and structured diagnostics.
This paper introduces POTTR, a novel combinatorial algorithm that utilizes incomplete partially ordered sets (posets) and a conflict graph approach to solve the NP-hard maximum k-common induced incomplete subposet problem, thereby identifying statistically significant recurrent genetic trajectories and conserved differentiation routes across diverse biological datasets while effectively resolving uncertainties inherent in tumor phylogenies and lineage tracing.
This paper introduces MaxGeomHash, a novel parallelizable and permutation-invariant sketching algorithm that generates variable-size random samples of distinct k-mers with sub-linear complexity, offering a balanced trade-off between storage efficiency and similarity estimation accuracy compared to existing fixed-size (MinHash) and linear-size (FracMinHash) methods.