Explainable artificial intelligence for omics data

Explainable artificial intelligence for omics data - Oxford Academic

Abstract. Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the ...

Explainable artificial intelligence for omics data: a systematic ...

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying ...

Explainable AI Methods for Multi-Omics Analysis: A Survey - arXiv

This approach enables a comprehensive understanding of biological systems by capturing different layers of biological information. Deep learning ...

Artificial intelligence for omics data analysis | BMC Methods | Full Text

The BMC Methods Collection "Artificial intelligence for omics data analysis" will feature novel artificial intelligence approaches leveraging ...

(PDF) Explainable artificial intelligence for omics data: a systematic ...

PDF | Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying ...

Artificial Intelligence in Omics - PMC

Artificial intelligence (AI) is a powerful approach for solving complex problems in the processing, analysis, and interpretation of omics data, ...

The promise of explainable deep learning for omics data analysis

This is the reason why emerging artificial intelligence applications have offered new opportunities in the analysis of omics data. AI plays a ...

AutoXAI4Omics: an Automated Explainable AI tool for Omics and ...

Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic ...

Orchestrating explainable artificial intelligence for multimodal and ...

AI systems can profit significantly from assimilating multimodal data into prediction and classification models to imitate integrative human ...

Cancer omic data based explainable AI drug recommendation ...

Although the DL model can fuse side information with meaningful semantics into the drug recommendation, the prediction process still cannot locate the key steps ...

Explainable Artificial Intelligence Based Modeling Applied to OMICS ...

The goal of the project is to produce billions of certified values from multiple measurement methods with well-characterized uncertainty in ...

Multi-Omics Data -Session 8- eXplainable Artificial Intelligence (XAI ...

Multi-Omics Data -Session 8- eXplainable Artificial Intelligence (XAI) for predictive purposes · ATHLETE Project - Exposome · Nvidia Finally ...

Advances in AI and machine learning for predictive medicine - Nature

Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit ...

Artificial intelligence applied to 'omics data in liver disease - Gut

This surge in data volume has necessitated the development of artificial intelligence (AI) techniques tailored for analysis of multidimensional biological ...

Explainable AI Methods for Multi-Omics Analysis: A Survey

This review explores how xAI can improve the interpretability of deep learning models in multi-omics research, highlighting its potential to ...

Explainable artificial intelligence for omics data: a systematic ... - OUCI

Abstract Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological ...

XAIOmics: Explainable Artificial Intelligence in Life Science: An ...

The objective of the XAIOmics research project is to design, develop, and evaluation an XAI approach to biomedical (i.e., omics) data. In ...

The Promise of Explainable Deep Learning for Omics Data Analysis

AI plays a critical role in allowing big datasets to be tractable. And among artificial intelligence approaches, deep learning. (DL) provides ...

Explainable AI Methods for Multi-Omics Analysis: A Survey - arXiv

Multi-omics refers to the integrative analysis of data derived from multiple 'omes', such as genomics, proteomics, transcriptomics, metabolomics, and ...

Mechanism-aware and multimodal AI: beyond model-agnostic ...

Combining metabolic modelling, 'omics, and imaging data via multimodal AI can generate predictions that can be interpreted mechanistically ...