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Results for TreeSHAP interpretability

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6 papers · ranked by Valyu relevance

01
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…
64relevance
02
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…
58relevance
03
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.…
53relevance
04
BbioRxiv7 Sep 2026
BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era
Amélie Barozet, Vincent Cabeli, Jean Ogier du Terrail, Alexey Rukhovich + 6 more
The development of climate-resilient crops would be greatly accelerated by models able to reason directly over plant genomic sequences and to pinpoint trait-associated regions or loci. Anticipating the impact of DNA base changes (variants) remains challenging, and understanding regulatory mechanisms is still an active…
52relevance
05
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…
51relevance
06
PPubMed7 Sep 2026
scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data
Anthony Christidis, Andrew Ghazi, Smriti Chawla, Nitesh Turaga + 2 more
Although cell type annotation has become an integral part of single-cell analysis workflows, the assessment of computational annotations remains challenging. Many annotation tools transfer labels from an annotated reference dataset to a new query dataset of interest, but blindly transferring labels from one dataset to…
50relevance

Results for TreeSHAP interpretability

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

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

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

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

scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data

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

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

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

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

scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data