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.
PIGLET is a novel graph transformer method that leverages a proteome-wide knowledge graph to predict drug-target interactions, demonstrating superior performance over existing models on rigorous drug-based splits and proving its utility in real-world drug discovery.
This paper introduces the Information-Content-Informed Kendall-tau (ICI-Kt) methodology, which reinterprets left-censored missing values in metabolomics as useful information to improve outlier detection and feature-feature network construction, supported by parallel R and Python implementations.
ProteomeLM is a novel transformer-based language model that operates on entire proteomes to generate contextualized protein representations, enabling accurate, rapid, and unsupervised prediction of protein-protein interactions as well as state-of-the-art supervised prediction of gene essentiality across diverse taxa.
This paper introduces ConNIS, a novel statistical method that calculates the probability of insertion-free sequences to improve the detection of essential genes in TraDIS libraries, particularly under low-to-medium insertion densities, while also providing a data-driven criterion for optimizing method parameters and thresholds.
This study introduces a robust computational framework that analyzes 560 breast cancer genomes to demonstrate how specific mutational processes, such as APOBEC and aging signatures, systematically rewire gene regulatory networks by causing non-random, subtype-specific gains or losses of transcription factor binding that drive oncogenic programs.
This study reveals that current evaluation metrics for single-cell perturbation response models are fundamentally flawed due to sensitivity to data characteristics, often masking the fact that complex deep learning models underperform simple baselines and fail to meet performance expectations.
This study demonstrates that while ancestry-specific differences in allele frequency distributions can confound the evaluation of variant effect predictors, these tools exhibit comparable accuracy across major genetic ancestry groups when properly stratified, supporting their responsible deployment in clinical genetic diagnosis.
This paper introduces MW-RaMViD, a novel 2D tilt-image generation method based on Random-Mask Video Diffusion that effectively corrects missing-wedge distortions in cryo-electron tomography by progressively synthesizing unacquired images, thereby improving 3D reconstruction fidelity.
This paper introduces DeepMDTOMO, a supervised deep learning framework that efficiently predicts atomic-scale conformational landscapes of biomolecules from noisy cryo-electron tomography data, overcoming the computational limitations of traditional physics-based flexible fitting methods.
The paper introduces SomaSV, a cost-effective hybrid sequencing framework that combines tumor long-read data with matched normal short- and long-read sequencing to achieve superior somatic structural variant detection accuracy and lower costs compared to state-of-the-art methods, while identifying clinically relevant cancer biomarkers.