Results 71 to 80 of about 31,305 (267)

Architecture‐Engineered Semi‐Crystalline Microporous Polymer Membranes for Precise Molecular Separation

Advanced Science, EarlyView.
Guided by a Random Forest model, a π–π‐driven ordered stacking strategy deploys functionally distinct substructures to restrict chain segment motion and increase free volume, while preserving the intermolecular interactions that maintain structural integrity.
Zi‐Meng Xu, Hao Zhuo, Song‐Hui Zhi, Xiao‐Feng Zhong, Pan‐Pan Zhang, Si‐Yuan Yang, Wen‐Yong Yang, Guang‐Yong Shen, Ya‐Zhao Li, Yi Li, Ming Xue   +10 more
wiley   +1 more source

Inflammatory-neurological deficit versus metabolic dysregulation: unsupervised clustering and SHAP analysis

Frontiers in Immunology
ObjectiveThis retrospective cohort study enrolled 20,538 middle-aged and older patients with acute ischemic stroke (AIS) to identify immune-metabolic clinical subtypes by unsupervised clustering, to examine the differential association between subtype ...
Ziying Wang, Lingling Wang
doaj   +1 more source

SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited datasets, to address the challenge posed by data privacy regulations that restrict access to medical datasets.
Yan Hu, Ahmad Chaddad
openaire   +2 more sources

Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data‐Scarce Conditions: A Case Study on IN718

Advanced Science, EarlyView.
An attention‐based multimodal deep learning framework is developed to predict the creep life of Ni‐based superalloys by fusing processing parameters with microstructural micrographs. The model achieves high accuracy (R2 = 0.92), aligns with metallurgical principles by capturing δ‐phase influence, and incorporates uncertainty quantification, offering a ...
Haopeng Lv, Dayong Wu, Ziyuan Rao, Qian Wang, Haikun Ma, Jie Kang, Wang Li, Huicong Dong, Yandong Wang, Zhinan Yang, Ru Su   +10 more
wiley   +1 more source

Comments on “Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants”

Redox Biology
Cardiovascular disease (CVD) and cancer are leading causes of death worldwide, and the comorbid risk between these conditions has become an important area of public health research.
Yuwen ShangGuan, Zhenhao Lin, Kunpeng Wu, Young-Je Sim, Weibo Xuan   +4 more
doaj   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

Explainable artificial intelligence for chemical process informatics: Energy-efficient design of styrene monomer production

Chemical Engineering Journal Advances
Parameter optimization in process design for chemical products is ideally performed at the system-wide level. However, such optimization presents a multivariate, multi-objective problem, requiring an extensive number of simulations. Here we optimized the
Sora Mimura, Taichi Masuda, Souta Miyamoto, Katsuaki Tanabe   +3 more
doaj   +1 more source

A Comparative Analysis of LIME and SHAP Interpreters With Explainable ML-Based Diabetes Predictions

IEEE Access
Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques.
Shamim Ahmed, M. Shamim Kaiser, Mohammad Shahadat Hossain, Karl Andersson   +3 more
openaire   +3 more sources

Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

Advanced Science, EarlyView.
A three‐tier livestock multi‐omics framework resolves four typical analytical pitfalls. Moving from statistical association through machine learning preprocessing to triple‐modal causal inference, it converts omics results into genomic selection and gene editing strategies to achieve One Health, underpinned by multi‐omics data, multimodal sequencing ...
Jiying Wen, Zhongyu Wang, Jieping Huang, Fen Li, Ningbo Chen, Yun Ma   +5 more
wiley   +1 more source

Comparative analysis of interpretable artificial intelligence methods

Journal of Computer Sciences Institute
The aim of this article is to analyze and compare methods for explaining the results of artificial intelligence methods. Three methods were analyzed: Grad-CAM, SHAP, and LIME, evaluated in terms of their effectiveness on different data types.
Aleksandra Kuszewska, Małgorzata Charytanowicz   +1 more
doaj   +1 more source