974 papers
The U-method introduces a fast, probability-based framework that identifies robust single-cell markers by prioritizing detection consistency over expression magnitude, enabling reliable cell population identification and spatial tissue organization mapping across diverse cancer datasets without complex spatial inference.
The paper introduces GeoBridge, a framework that models cellular dynamics as geodesic trajectories to transform sparse, nonlinear single-cell RNA sequencing data into a continuous, linear latent manifold, thereby enabling the reconstruction of unobserved intermediate states, the inference of pseudo-temporal trajectories, and the controlled navigation between distinct cellular phenotypes.
This study introduces MAPPER, a scalable multimodal framework that accurately predicts antibiotic modes of action and detects novel mechanisms in *E. coli* by integrating proteomic, chemical, and growth data, thereby facilitating the prioritization of antibacterial candidates with distinct targets.
HalluCodon is a customizable framework that leverages fine-tuned multimodal language models and a hallucination-based design strategy to generate species-specific coding sequences that replicate natural codon usage patterns and enhance protein expression in diverse plant systems.
This study validates the adoption of FoldX5.1 for the MAVISp database by demonstrating high agreement with the previous FoldX5 version across nearly 540,000 variants, supporting a gradual transition strategy that includes new metadata annotations to ensure data compatibility.
EMITS is a high-performance Rust-based tool that utilizes an expectation-maximization algorithm to resolve ambiguous read mappings and improve species-level abundance estimation in fungal ITS communities from long-read sequencing, significantly outperforming naive counting methods in accuracy and error reduction.
mRNA-GPT is a pre-trained generative model that utilizes reinforcement learning to jointly optimize full-length mRNA sequences across all regions, achieving superior Pareto-optimal designs for therapeutic efficacy by capturing long-range dependencies and balancing competing properties like stability and translation efficiency.
This study demonstrates that a hybrid Random Forest-SVM framework utilizing TF-IDF-based k-mer features outperforms deep learning methods in classifying imbalanced SARS-CoV-2 genomic data, achieving superior macro-averaged F1-scores and robust detection of rare variants.
This study systematically benchmarks 86 SNP heritability estimation configurations across six tool families and ten method groups, revealing that while heritability estimates vary substantially based on algorithmic choices and standardization, this upstream variability has minimal impact on downstream polygenic risk score performance, suggesting heritability should be treated as a configuration-sensitive parameter rather than a stable scalar.
This paper introduces SEGUID v2, an enhanced checksum system that extends the original SEGUID to generate orientation- and rotation-invariant identifiers for circular, linear, single-stranded, and double-stranded biological sequences while adopting Base64url encoding for improved compatibility with filenames and URLs.
This study introduces a novel mathematical model that integrates intensity-dose susceptibility bioassay data to account for resistance heterogeneity in mosquito populations, enabling more accurate predictions of insecticide-treated net effectiveness and the public health impact of insecticide resistance across Africa.
This paper proposes a method for assessing the stability of single-cell RNA-seq clusterings by applying divisive hierarchical spectral clustering with a novel tree-to-nested-cluster mapping and validating the results through consistency checks on complementary cell subsamples.
This paper presents a highly efficient convolutional neural network (CNN) model that utilizes TFxIDF vectorization of amino acid sequences derived from Human Genome Build 38 to accurately predict gene locations, achieving state-of-the-art performance in identifying genes associated with genetic disorders.
This study presents a high-throughput pipeline that leverages AlphaFold3 and MMSeqs2 to systematically predict protein-protein interactions across nearly 500,000 pairs in biosynthetic gene clusters, successfully uncovering hidden molecular networks and functional enzyme complexes involving previously uncharacterized proteins.
This paper introduces a new edit-distance metric specifically designed for comparing Lateral Gene Transfer (LGT) networks, demonstrating that it is computable in linear time when transfer order is unconstrained but NP-hard otherwise (with a fixed-parameter tractable solution provided), and validating its utility through numerical experiments.
This study rigorously benchmarks seven state-of-the-art computational methods for removing ambient RNA from single-cell and single-nucleus RNA sequencing data, revealing that while no single method excels across all scenarios, CellBender, DecontX, and SoupX generally demonstrate superior performance.
This paper introduces the DGIdb Model Context Protocol (MCP) server, which enables large language models to access up-to-date drug-gene interaction data from the DGIdb API via natural language, thereby significantly enhancing their ability to provide accurate biomedical answers.
This study introduces Adaptive Cluster-Count Autoencoders with Dirichlet Process Priors, which significantly enhance the geometric compactness and separation of single-cell latent spaces at a modest cost to label-recovery accuracy, thereby establishing a task-dependent trade-off where nonparametric priors are optimal for trajectory analysis and manifold visualization rather than strict cluster counting.
The paper introduces Topic-FM, a novel family of neural topic VAEs that enforces simplex constraints via a logistic-normal Dirichlet prior and employs a conditional optimal-transport flow to simultaneously enhance clustering performance, supervised discrimination, and biological interpretability of gene programs across diverse single-cell RNA sequencing datasets.
This paper presents a comprehensive benchmark evaluating the performance of various biomarker identification and prognostic modeling methods on diverse censored genomic data through extensive simulations and real-world cancer cohort analysis, ultimately identifying CoxBoost and Adaptive LASSO as top performers across multiple metrics to guide researchers in selecting optimal approaches.