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 SLOPE, an unsupervised dimensionality reduction method that models Alzheimer's disease amyloid progression on a continuous scale, revealing biologically consistent spreading patterns and offering greater sensitivity to early-stage changes than traditional global amyloid measures.
This paper introduces a Monte Carlo algorithm that performs on-the-fly deduplication of k-mer color sets via incremental fingerprinting, enabling the construction of compressed colored de Bruijn graph indices with significantly reduced peak memory usage and a provably low error probability.
This paper demonstrates a proof-of-principle supervised learning framework that successfully maps noisy cryo-EM single-particle images directly to atomic coordinates via latent representations, achieving high-accuracy structural predictions without requiring explicit pose recovery or 2D projection calculations.
This paper introduces Guided Tokenization, a strategy that prioritizes biologically and statistically significant subsequences to enhance the performance and biological awareness of compact genomic language models across various classification and profiling tasks.
This paper demonstrates that open-weight large language models, when integrated into a retrieval and semantic filtering workflow, can achieve near-perfect accuracy in automating the curation of unstructured metadata from public life science databases, thereby overcoming the limitations of traditional keyword searches and enabling scalable, reproducible data reuse.
The paper introduces Wayfarer, a multiscale R framework for spatial -omics that transforms arbitrary spatial aggregation choices into diagnostic signals by tracking how spatial association metrics evolve across nested scales, revealing reproducible scale-dependent shifts in tumor progression that are missed by single-resolution analyses.
SCiMS is a novel, scalable bioinformatic tool that accurately predicts host sex from metagenomic sequencing data—even in low-biomass samples and across diverse species—by leveraging sex-chromosome read density ratios within a Bayesian classifier to recover missing metadata and ensure research integrity.
The paper introduces DEFT, a hybrid machine learning method that combines protein language models for broad enzyme classification with structure-based alignment for fine-grained subcategorization, achieving superior accuracy and computational efficiency in predicting Enzyme Commission numbers for gut bacterial mucin-degrading enzymes.
This paper addresses the overestimation of performance in multi-input biological association prediction caused by degree ratio shortcuts by introducing an entity-balanced evaluation framework and the UnbiasNet training strategy to ensure fair, robust, and meaningful assessments of genuine relational learning.
This paper demonstrates that a novel prefix-based downsampling algorithm, which reduces bacterial genomes into k-mer representations, enables accurate and explainable phenotype prediction using efficient ensemble models, offering a lightweight alternative to full-genome analysis and deep learning architectures.