995 papers
MetaXtract is an open-source tool that extracts structured acquisition metadata directly from Thermo Fisher raw mass spectrometry files to enhance FAIR data practices, enable real-time quality control, and support reproducible, large-scale data analysis workflows.
This study introduces XAMP, a dual-engine AI framework integrating ESM-2 and Transformer models to overcome prediction biases and rapidly identify novel, broad-spectrum antimicrobial peptides from deep-sea microbiomes, which were experimentally validated for their potent activity against multidrug-resistant pathogens.
ARCADIA is a novel generative framework that integrates unpaired single-cell RNA sequencing and spatial proteomics data by aligning modality-specific archetypes to reconstruct tissue architecture and uncover spatially dependent transcriptional programs without requiring cell barcode pairing or direct feature correspondence.
RingNet is a web-based interactive platform that integrates an efficient R backend with a flexible JavaScript frontend to enable domain experts to visualize, explore, and export multi-omics network data without requiring advanced computational expertise.
Neretva is an open-source framework that utilizes auto-encoding variational Bayes to efficiently and accurately perform allele-level genotyping and phasing of highly polymorphic gene families like CYP and KIR, outperforming current state-of-the-art methods in scalability and accuracy.
The Biodiversity Observatory System (BiOS) is an open-source, modular framework that overcomes data heterogeneity and fragmentation in biodiversity research by decoupling backend management from frontend presentation to provide both programmatic API access and an intuitive web interface for integrating diverse datasets under FAIR principles.
The paper introduces SC-BIG, a hierarchical Bayesian model that leverages bulk sequencing data to jointly estimate cancer cell fractions and improve the accuracy and uncertainty quantification of somatic SNV detection in single cells, outperforming existing methods in the presence of copy-number alterations and clonal admixtures.
This paper presents a comprehensive benchmark of 26 spatial domain detection methods across diverse real and semi-synthetic datasets, revealing that performance is primarily driven by data resolution and cellular heterogeneity rather than architectural novelty, and introduces a modular framework to guide future tool development and selection.
This paper presents a transformer-based language model integrated into the CIPHER platform that achieves 95% accuracy in identifying computable phenotype definitions within biomedical literature, utilizing a novel sliding-window approach and a user feedback loop to enable scalable, adaptive curation.
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.