eXplainable Artificial Intelligence (XAI) in aging clock models
원문 초록
eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Do Protein Transformers Have Biological Intelligence?
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
Addressing the Scarcity of Benchmarks for Graph XAI
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors
토론
이 논문과 연결된 포럼 글이 아직 없습니다.