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Results for Model Interpretability

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10 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…
81relevance
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…
80relevance
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.…
76relevance
04
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…
76relevance
05
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…
73relevance
06
PPubMed7 Sep 2026
Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques
Sahar Alkhaibari, Feng Dong
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and…
69relevance
07
PPubMed7 Sep 2026
Artificial intelligence-based prediction of procedural success in transcatheter mitral valve edge-to-edge repair
Mathias Claeys, Neil Fam, Xiaojiao Xiao, Tejas Vyas + 10 more
Background Patient selection for transcatheter mitral valve edge-to-edge repair (MTEER) remains challenging, particularly in individuals with complex mitral valve anatomy. Conventional risk scores incorporate limited clinical variables and do not adequately account for detailed echocardiographic features, resulting in…
69relevance
08
BbioRxiv8 Sep 2026
PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models
Eliezer Masliah
How transient neural representations become integrated and stable enough to function as internal neural models remains incompletely understood. Grounded in efficient coding, Bayesian and predictive frameworks, recurrent and attractor dynamics, neural state-space models, and systems neuroscience, the Principle of…
61relevance
09
PPubMed6 Sep 2026
The Predator–Prey Encounter Detection ( PrED ) Model: Detecting Interactions Between Rotifers and Algae Using Computer Vision and Machine Learning
Braden Charles DeMattei, Stephanie E. Hampton
Understanding species interactions is integral to understanding ecosystem function and predicting how disturbances may affect the services provided to society. Analysis of trophic interactions, however, requires a significant investment of time and resources. Using a simple predator-prey pairing of a semi-sessile…
57relevance
10
PPubMed7 Sep 2026
A flexible framework for robust and efficient Mendelian randomization with debiasing
Linsui Deng, Kejun He, Xianyang Zhang
Mendelian randomization (MR) has been widely used to infer causal relationships between exposures and outcomes in epidemiological studies. However, classical MR assumptions can be violated when genetic variants are associated with outcomes through pathways other than the exposure, leading to uncorrelated and/or…
55relevance

Results for Model 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

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

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

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Artificial intelligence-based prediction of procedural success in transcatheter mitral valve edge-to-edge repair

PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

The Predator–Prey Encounter Detection ( PrED ) Model: Detecting Interactions Between Rotifers and Algae Using Computer Vision and Machine Learning

A flexible framework for robust and efficient Mendelian randomization with debiasing

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

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

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

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Artificial intelligence-based prediction of procedural success in transcatheter mitral valve edge-to-edge repair

PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

The Predator–Prey Encounter Detection ( PrED ) Model: Detecting Interactions Between Rotifers and Algae Using Computer Vision and Machine Learning

A flexible framework for robust and efficient Mendelian randomization with debiasing