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10 papers · ranked by Valyu relevance

01
PPubMed7 Sep 2026
Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data
Xiaoyue Hu, Yuhao Ma, Ruixing Ming, Heping Zhang + 1 more
Identifying essential biomarkers remains a core challenge in elucidating the pathogenic mechanisms and achieving precise diagnosis of complex diseases. Deep neural networks offer immense predictive power, yet their lack of interpretability severely limits downstream biological insight. Here, we introduce DeepVaris, an…
92relevance
02
PPubMed6 Sep 2026
An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework
Yinuo Zhang, Yan Zhu, Xinxin Zhang, Xinke Shen + 8 more
Postoperative delirium (POD) is a common complication in older surgical patients and substantially worsens clinical outcomes, yet existing intraoperative electroencephalography (EEG) monitoring tools lack spatial and temporal specificity, creating a need for interpretable biomarkers. We prospectively analyzed…
83relevance
03
PPubMed7 Sep 2026
A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening
Danny Vu, Andrew Kowalczewski, Sarah D. Burnett, Courtney Sakolish + 4 more
Cardiotoxicity remains a major cause of drug attrition and postmarket withdrawal, yet the vast majority of environmental chemicals to which humans may be exposed remain uncharacterized for cardiotoxicity risk. Human induced pluripotent stem cell (hiPSC)-based testing has been proposed to address this gap. Here, we…
73relevance
04
PPubMed7 Sep 2026
Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratory measurements, and treatment history
Shangru Jia, Artem Lysenko, Keith A Boroevich, Alok Sharma + 1 more
Prognostic stratification in multiple myeloma (MM) relies on staging systems fixed at diagnosis, discarding temporal information accumulated during treatment. We developed a dynamic multimodal framework that predicts residual overall survival from observation windows of 1-18 months post-diagnosis. The model integrates…
73relevance
05
BbioRxiv7 Sep 2026
A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data
Zichao Jin, Jiaoru Wang, Wenjiang Huang, Jingcheng Zhang + 2 more
Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework.…
73relevance
06
PPubMed7 Sep 2026
Decoding cell–cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats
Yuesong Wu, Haohao Su, Yuehua Cui
Cell-cell communication (CCC) is essential for maintaining tissue organization and driving biological progression, yet its inference from transcriptomic data has long been limited by the absence of spatial context. Advances in spatial transcriptomics (ST) now enable mechanistically grounded analyses of CCC by…
72relevance
07
BbioRxiv8 Sep 2026
scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data
Jackie Rao, Muntadher Jihad, Giulia Biffi, Paul D.W. Kirk
Identifying cell types from single-cell RNA sequencing (scRNA-seq) data typically requires several separate and often uninterpretable steps: dimensionality reduction, batch-correction, clustering, marker-gene identification and the discovery of finer-grained structure. Here we introduce scFLAME (single-cell Factor…
70relevance
08
BbioRxiv8 Sep 2026
Deep-layer cortical tracking abruptly collapses in the absence of language comprehension
ChenTianyi Yang, Andrew Thwaites, Cai Wingfield, Chao Zhang + 1 more
The brain builds meaning from speech in stages, transforming acoustic input into linguistic comprehension. Yet where comprehension separates from general acoustic processing has been difficult to localize, because the two are tightly entangled in continuous speech. Here we align the activity of 145,000 individual…
67relevance
09
BbioRxiv7 Sep 2026
Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions
Priyanka Bhutada, Nitin Goyal, Tatsam K. Lakhankiya, Sai D. Narahari + 8 more
Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI…
57relevance
10
BbioRxiv8 Sep 2026
BioSecBench-Function: A Verifiable Benchmark for Reasoning about Biological Function from Experimental Data
Dianzhuo Wang, Qian Xu, Arjun Banerjee, Rishi Jain + 7 more
Inferring biological function from experimental data is central to understanding emerging pathogens and developing effective countermeasures, yet interpreting these data remains slow and expert-intensive. AI agents could help accelerate this process by reasoning across sequence, structural, and biophysical evidence. We…
51relevance

Results for interpretable deep learning framework

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework

A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening

Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratory measurements, and treatment history

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Decoding cell–cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions

BioSecBench-Function: A Verifiable Benchmark for Reasoning about Biological Function from Experimental Data

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

An Intraoperative EEG Biomarker for Postoperative Delirium Predicting Based on Interpretable Deep Learning Framework

A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening

Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratory measurements, and treatment history

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Decoding cell–cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions

BioSecBench-Function: A Verifiable Benchmark for Reasoning about Biological Function from Experimental Data