1005 papers
This study integrates population-scale lipidomics with genome-wide association analysis in 266 sorghum accessions to elucidate the complex genetic architecture of grain oil, identifying 55 key loci and 27 elite accessions that reveal coordinated regulation between lipid metabolism and specialized pathways for targeted crop improvement.
This paper introduces ITEC, a fully unsupervised method that achieves high-fidelity, long-term reconstruction of complete cell lineages and fate maps across multiple species by iteratively tracking and correcting errors in terabyte-scale embryonic imaging data.
This paper introduces scPloidyR, a hidden Markov model that jointly analyzes sequencing read depth and B-allele frequency from targeted single-cell DNA panels to significantly improve the accuracy of copy number variation detection compared to depth-only methods, provided that allelic information is available.
SpotGraphs is an R package that enhances spatial transcriptomics analysis by providing direct, flexible access to spot adjacency graphs via the igraph infrastructure, enabling advanced spatial manipulation and offering performance comparable to Python's SquidPy.
This paper introduces a chemically informed peptide representation based on 2D molecular structures and convolutional autoencoders that enables machine learning models to generalize beyond the canonical amino acid alphabet to unseen post-translational modifications while providing chemically interpretable insights.
UniST is a unified generative AI framework that integrates point cloud upsampling, optical flow interpolation, and graph autoencoders to computationally reconstruct dense, continuous, and biologically meaningful 3D spatial transcriptomics landscapes from sparse and heterogeneous 2D serial sections without requiring changes to experimental protocols.
This study of 1,154 fungal genomes reveals that genes involved in translational machinery, such as proteasome regulation, ion transport, and rRNA synthesis, are significantly enriched near tRNA loci, suggesting a functional organization that enhances the efficiency of protein production and quality control.
The paper introduces pertTF, a transformer-based AI model trained on a unique multi-cell type dataset that outperforms existing methods in predicting genome-scale genetic perturbation effects across unseen cellular contexts and infers critical phenotypic shifts in cell identity and population composition.
This paper introduces a simulation framework demonstrating that hierarchical reinforcement learning effectively optimizes antibiotic prescribing policies under antimicrobial resistance dynamics by leveraging temporal abstraction and risk stratification to outperform fixed rules and flat RL approaches in complex, partially observable environments.
This study demonstrates that exogenous strigolactone treatment inhibits corm development in *Pisang Awak* banana by dynamically regulating the expression of genes involved in multiple hormonal pathways, with the SPL15 gene identified as a key mediator integrating strigolactone signaling with auxin, cytokinin, abscisic acid, brassinosteroids, gibberellins, and jasmonic acid pathways.
This paper demonstrates that applying conformal prediction to LC-MS/MS molecular retrieval generates spectrum-specific candidate sets with guaranteed coverage probabilities, effectively balancing reliability and efficiency across both in-distribution and shifted data scenarios.
The paper introduces pipeML, a flexible R-based machine learning framework that prevents data leakage in omics studies by recomputing global dataset features independently within each cross-validation fold, thereby ensuring robust and unbiased evaluation of clinical outcome predictors.
This study pioneers the integration of replication-timing proxies derived from methylation data and morphological embeddings into personalized LIONESS gene networks, demonstrating that these epigenetic and structural features significantly enhance the robustness and classification accuracy of basal-like versus classical subtypes in pancreatic adenocarcinoma.
AetherCell is a generative foundation model that unifies fragmented clinical and experimental transcriptomic data to overcome generalization barriers, enabling high-fidelity prediction of drug responses across biological scales and successfully validating the in vivo repurposing of teriflunomide and dabigatran.
This study enhances the Metabuli metagenomic classifier by integrating spaced metamers, reduced amino acid alphabets, and syncmers to significantly improve sensitivity, precision, and speed while reducing reference database size.
DCS Tools is a high-performance, CPU-optimized software suite that significantly accelerates population-scale genomic analysis and achieves substantial data compression without relying on specialized hardware, offering a cost-effective solution for petabyte-scale storage and processing.
The paper introduces AI-IDP, a deep-learning framework that accurately predicts conformational ensembles for intrinsically disordered proteins by integrating sequence data with physical principles, revealing that transient secondary structures are evolutionarily tuned features and providing a practical tool to understand the structural and functional logic of disordered proteomes in health and disease.
The Grass Expression Atlas (GExA) is a freely available, interactive web resource that integrates and uniformly processes thousands of RNA-seq datasets from four millet species, along with barley and sorghum, to facilitate rapid exploration of gene expression across diverse tissues, developmental stages, and conditions.
Stoic is a fast and accurate method that leverages protein language model embeddings and graph neural networks to predict the stoichiometry of protein complexes by identifying interface residues, thereby overcoming the computational limitations of current brute-force approaches.
PepCABO is a novel latent-space Bayesian optimization framework that leverages a dual variational autoencoder with contrastive alignment to efficiently discover high-affinity peptide-MHC binders by enabling structured knowledge transfer across alleles and improving sample efficiency in both low- and high-budget experimental settings.