950 papers
Leveraging the 2021 release of AlphaFold2 as a quasi-experiment, this study demonstrates that AI predictions can expand scientific frontiers by reversing the decline in research on novel proteins and redirecting collective attention toward understudied genes and targets, thereby challenging concerns that AI merely reinforces established scientific canons.
This study benchmarks CPU and GPU pipelines for Nanopore ITS fungal analysis, demonstrating that while GPU-accelerated workflows maximize species-level accuracy through error correction, optimized CPU-based machine learning approaches offer a viable, resource-efficient alternative for achieving high genus-level taxonomic resolution.
This paper presents a suite of open-source plugins for Fiji (ImageJ) and Python designed to analyze signal-to-noise ratio, contrast, and resolution in Scanning Electron Microscopy images, including support for custom FIB-SEM instruments.
RastQC is a high-performance, unified sequencing quality control tool written in Rust that replaces the Java-based FastQC by offering a single 2.1 MB static binary capable of analyzing both short and long reads with matching accuracy, significantly faster processing speeds, and reduced memory consumption.
This study demonstrates that the protein fold repertoire of archaea is broadly conserved across deep phylogenetic distances, revealing that the scarcity of experimentally determined archaeal structures stems from classification sensitivity to divergent sequences rather than a lack of known structural diversity.
By applying corrected statistical procedures and alternative tests to address previous methodological concerns, this study reaffirms that synonymous codons in the *Escherichia coli* proteome are associated with distinct amino acid backbone conformations.
NovoTax is an end-to-end pipeline that enables strain-level identification of prokaryotic organisms directly from raw mass spectrometry-based proteomics data by combining de novo peptide sequencing with optimized genome database searching, thereby facilitating downstream proteome analysis without prior knowledge of sample composition.
This study utilizes multistage machine learning on chick models to identify a critical circadian window (ZT8–ZT12) that drives coordinated retina-choroid gene expression programs in myopia, revealing conserved molecular mechanisms that translate to complex human regulatory networks.
This paper argues that while large language models fail to provide complete, source-attributed answers for plant science queries due to their parametric nature, a GraphRAG architecture leveraging structured, provenance-linked knowledge graphs offers a reproducible and reliable alternative for generating exhaustive, citation-backed results.
EV-Net is a novel bioinformatics framework that adapts the NicheNet tool to model extracellular vesicle-mediated cell-to-cell communication by analyzing proteomics and RNA-seq data to identify and prioritize regulatory cargo molecules in recipient tissues.
BABAPPASnake is an integrated, reproducible workflow that unifies orthogroup construction, alignment, phylogeny, and selection testing to facilitate robust, sensitivity-aware episodic selection analysis with comprehensive robustness summaries.
The paper introduces RAMBO, a novel, reference-free pipeline that utilizes unsupervised clustering and consensus generation to accurately resolve mixed amplicon signals and distinguish highly similar DNA variants in Oxford Nanopore sequencing data, thereby enabling high-resolution DNA barcoding even in complex samples containing pseudogenes or contaminants.
The authors developed the BayesMonSTR algorithm to detect mosaic microsatellite mutations at single-cell resolution, revealing that aging human neurons accumulate a higher burden of longer STR deletions enriched at regulatory regions compared to other tissues.
This paper demonstrates that a widespread practice of applying supervised feature screening before cross-validation causes severe data leakage in cancer drug response prediction, systematically inflating reported accuracy and corrupting biomarker discovery by introducing statistical artifacts that mimic biological signals.
By integrating transcriptomic data across four diverse tumor models, this study reveals a conserved "inflammatory-proliferative paradox" in acquired resistance to immune checkpoint blockade, where tumors simultaneously maintain interferon-gamma-driven inflammatory signatures and activate cell-cycle programs through a shared regulatory network, suggesting that resistance is an active, tumor-intrinsic adaptive process rather than a simple loss of immune recognition.
OpenAc4C is the first comprehensive, freely accessible knowledgebase that integrates deep learning-identified ac4C sites and pathogenic variants across 33 species to facilitate the study of the ac4C epitranscriptome's landscape, regulation, and role in disease.
The authors present VDJcraft, a novel pipeline specifically designed for accurate V(D)J recombination analysis using long-read transcriptomic data, which outperforms existing methods in gene detection and recombination accuracy while enabling the discovery of novel gene subclasses and disease-associated immune signatures.
This paper introduces three new repeat-robust k-mer-based estimators for mutation rates that overcome the limitations of existing sketch-based methods on highly repetitive sequences, demonstrating superior performance on alpha satellite data.
The paper introduces GP-GHS, a scalable Bayesian framework that models cell-cell interactions as spatially varying networks using Gaussian processes and group horseshoe priors, enabling the detection of context-specific immunosuppressive mechanisms in colorectal cancer that existing global or marginal methods miss.
This study presents an interpretable, attention-based deep learning framework that integrates mRNA, miRNA, and DNA methylation data to significantly improve prognostic accuracy for hepatocellular carcinoma patients compared to existing models, while identifying biologically relevant biomarkers and demonstrating robust performance on external validation cohorts.