Results 131 to 140 of about 25,341,143 (317)

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Machine learning-based comprehensive analysis of m6A RNA methylation regulators in colorectal cancer: implications for prognosis, immune microenvironment, and immunotherapy response

Experimental Biology and Medicine
N6-methyladenosine (m6A) RNA methylation regulators have been implicated in colorectal cancer (CRC) progression. However, systematic evaluation using multiple machine learning approaches for prognostic prediction remains limited.
Feifei Kong, Feifei Kong, Jiawei Feng, Haixia Shan, Youlong Zhu, Ling-Jun Zhu   +5 more
doaj   +1 more source

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

A Novel SHAP-GAN Network for Interpretable Ovarian Cancer Diagnosis

Mathematics
Ovarian cancer stands out as one of the most formidable adversaries in women’s health, largely due to its typically subtle and nonspecific early symptoms, which pose significant challenges to early detection and diagnosis.
Jingxun Cai, Zne-Jung Lee, Zhihxian Lin, Ming-Ren Yang   +3 more
doaj   +1 more source

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

Engineering Applications of Computational Fluid Mechanics
In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to ...
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, K. O. Ahmed, Salim Heddam   +5 more
semanticscholar   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application

Scientific Reports
The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems.
Xiaolei Zhou, Xingyue Wang, Ruifeng Guo
doaj   +1 more source

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

Machine Learning‐Driven Digital Twin of a Field‐Effect Transistor‐Based Hormone Biosensor for Real‐Time Estradiol Monitoring

Advanced Intelligent Systems, EarlyView.
A machine learning‐driven digital twin simulates an aptamer‐functionalized BioFET measuring 17β‐estradiol. Real‐time Isd signals are processed, features are extracted, and trained models estimate hormone concentration. In parallel, a one‐step‐ahead forward model learns biosensor dynamics and generates realistic synthetic signals, enabling in silico ...
Anastasiia Gorelova, Alexandra Parichenko, Shirong Huang, Santiago Melia, Gianaurelio Cuniberti   +4 more
wiley   +1 more source