984 papers
This study demonstrates that because agreement among computational variant effect predictors does not correlate with their accuracy against experimental data, prioritizing proteins with high inter-predictor disagreement is an effective strategy for selecting targets for resource-intensive experimental characterization to maximize informational value.
The paper introduces ISdetector, a scalable and robust bioinformatics pipeline that outperforms existing tools in precisely mapping insertion sequences and detecting associated structural variations in bacterial and archaeal genomes using short-read sequencing data.
This paper introduces Pro2RNA, a multimodal reverse-translation language model that integrates protein, taxonomy, and RNA representations to generate host-adapted, species-specific mRNA coding sequences, outperforming existing optimization methods in benchmark evaluations.
This paper demonstrates that applying temperature scaling and epistemic neural networks significantly improves the reliability of transformer-based genomic language models when facing distribution shifts, such as noisy data or novel variants, across diverse genomic prediction tasks.
This study demonstrates that unsupervised embeddings from the pretrained Evo2 genome language model capture meaningful host-range signals for bacteriophages, achieving competitive retrieval performance that is further enhanced when combined with traditional alignment and k-mer-based methods in a hybrid pipeline.
PanXpress is a unified, efficient, and accurate framework for bacterial gene expression quantification that utilizes a pan-transcriptomic gapped k-mer index to overcome reference bias in mixed-strain samples by directly constructing references from genomic data and outperforming existing tools in precision, speed, and memory usage.
This study presents an AI-assisted curation tool integrated with MorphoBank that leverages Large Language Models to automate the extraction and standardization of morphological character data into NEXUS format, thereby significantly enhancing data accuracy, efficiency, and FAIR compliance in biological and paleontological research.
SOORENA is a two-stage transformer model that systematically identifies and classifies protein autoregulation in millions of PubMed abstracts, generating a comprehensive, interactive database to bridge literature mining and systems biology for improved network modeling.
GatorSC is a unified, self-supervised representation learning framework that integrates multi-scale cell and gene graphs via a Mixture-of-Experts architecture to produce robust embeddings, outperforming state-of-the-art methods in cell clustering, gene imputation, and biological discovery across diverse single-cell RNA sequencing datasets.
This study analyzes nucleic acid sequences from the Zag meteorite, revealing that they exhibit constrained complexity and low-dimensional structure distinct from both known biological patterns and random models, suggesting an unusual origin that warrants further independent investigation.
This study demonstrates that Large Language Models can accurately extract prognostic histologic features from unstructured pathology reports in Ewing sarcoma, revealing that Neuron-Specific Enolase (NSE) positivity and S100 negativity are significant independent predictors of inferior survival, particularly in non-metastatic patients.
The paper introduces FlashS, a scalable frequency-domain method that enables accurate, well-calibrated detection of spatially variable genes in atlas-scale spatial transcriptomics datasets by leveraging Random Fourier Features and sparse sketching to overcome the computational and expressiveness trade-offs of existing approaches.
The paper introduces Harmony2, an optimized software version capable of efficiently and accurately integrating massive, complex single-cell RNA-seq datasets exceeding 100 million cells without specialized hardware while preventing overintegration in biologically heterogeneous data.
The paper introduces ChiMER, a novel graph-based framework that integrates chromatin architecture with splicing graphs to systematically detect low-abundance chimeric enhancer RNAs, demonstrating superior sensitivity over conventional methods and revealing their association with active regulatory environments and R-loop structures.
The paper introduces STpath, a framework that leverages cancer-specific XGBoost models to translate uninterpretable histopathology foundation model embeddings into biologically meaningful cellular and molecular features, thereby enabling their use in clinical outcome studies.
This study presents the largest harmonized single-cell transcriptomic atlas of *Macaca fascicularis*, integrating over 2.5 million cells across 43 organs and leveraging a human foundation model to enable scalable cross-species comparisons that improve target qualification, mechanistic toxicity interpretation, and the reduction of non-human primate use in preclinical research.
ProteinSage is a data-efficient protein language modeling framework that leverages explicit structural constraints, such as structure-guided masking and causal objectives, to learn transferable representations that outperform sequence-only models and successfully identify novel microbial rhodopsin homologs.
The paper introduces SNMF, a novel reference-free deconvolution method for spatial transcriptomics that leverages spatial correlations and GPU acceleration to achieve ultrafast, highly accurate cell-type composition analysis, outperforming existing state-of-the-art tools.
By integrating AlphaFold-3 modeling with molecular dynamics simulations, this study reveals how RHEB binding induces global structural and energetic remodeling of the mTORC1 complex to stabilize ATP coordination and preorganize the kinase for catalysis.
The paper introduces evedesign, a unified open-source framework and web interface that overcomes current limitations in protein engineering by enabling flexible, method-agnostic, and iterative multi-objective biosequence design workflows accessible to both experts and non-experts.