Results 121 to 130 of about 109,092 (251)

Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Advanced Science, EarlyView.
We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu, Minhui Ouyang, Shufang Tan, Jianlin Guo, Ziqin Zhang, Xuan Liu, Risheng Liu, Hao Huang   +7 more
wiley   +1 more source

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Advanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi, Mohammad Mahdi Hosseini, Ahmadreza Argha, Roxana Zahedi, Nigel H. Lovell, Hamid Alinejad‐Rokny   +5 more
wiley   +1 more source

Assessing the Relative Importance of Imaging and Serum Biomarkers in Capturing Disability, Cognitive Impairment, and Clinical Progression in Multiple Sclerosis

Advanced Science, EarlyView.
Using machine‐learning analyses in two independent multiple sclerosis cohorts, spinal cord atrophy and cortical degeneration emerged as key predictors of disability and progression independent of relapses. Deep gray matter damage further improved prediction, while serum biomarkers of brain damage provided complementary information, highlighting the ...
Alessandro Cagol, Pascal Benkert, Sabine Schaedelin, Mario Ocampo‐Pineda, Noemi Montobbio, Po‐Jui Lu, Batuhan Ayci, Antonia Wenger, Alfi Aran Shukur, Kornelius Kaim, Lester Melie‐Garcia, Matthias Weigel, Alessio Signori, Pasquale Calabrese, Ludwig Kappos, Maria Pia Sormani, Jens Kuhle, Cristina Granziera   +17 more
wiley   +1 more source

Comparative Analysis of Hyperparameter Optimization Using Optuna and Hyperopt for Convolutional Neural Networks

Современные информационные технологии и IT-образование
The process of training a neural network model is controlled by selecting optimal hyperparameters, which have a significant impact on its quality and performance. This impact has been confirmed both theoretically and empirically by numerous studies.
Tatyana Samoilova
doaj   +1 more source

Proteogenomic Characterization Reveals Subtype‐Specific Therapeutic Potential for HER2‐Low Breast Cancer

Advanced Science, EarlyView.
Multiomic profiling of HER2‐low breast cancer identifies three proteomic subtypes with distinct therapeutic strategies: endocrine, antiangiogenic, and anti‐HER2 therapies. Genomic and lactate modification landscapes are detailed, providing insights for precise management.
Shouping Xu, Keda Yu, Lei Liu, Qin Wang, Xiaohui Wu, Yihai Chen, Guozheng Li, Xin Zhang, Bo Wei, Zitong Fu, Abiyasi Nanding, Zuxianglan Zhao, Lingbing Yang, Xingda Zhang, Jianyu Wang, Wantong Sun, Yi Hao, Zhongyi Cheng, Xiaojiang Cui, Hao Wu, Da Pang   +20 more
wiley   +1 more source

The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification

Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection.
Kadek Eka Sapta Wijaya, Gede Angga Pradipta, Dadang Hermawan   +2 more
doaj   +1 more source

DualPG‐DTA: A Large Language Model‐Powered Graph Neural Network Framework for Enhanced Drug‐Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting in Vivo Anti‐Leukemia Activity

Advanced Science, EarlyView.
This study introduces DualPG‐DTA, a framework integrating two pre‐trained models to generate molecular and protein representations. It constructs dual graphs processed by specialized neural networks with dynamic attention for feature fusion, achieving superior benchmark performance.
Yihao Chen, Jindi Huang, Cong Liu, Shipeng Zhang, Xinze Li, Zhang Zhang, Tie‐Gen Chen, Ling Wang   +7 more
wiley   +1 more source

Fine-Tuning LLMs for E-Commerce Sentiment Analysis: Proprietary Versus Open-Source Approaches

IEEE Access
The increasing volume of online product reviews presents both opportunities and challenges for e-commerce platforms seeking to leverage customer sentiment for strategic decision-making.
Pawanjit Singh Ghatora, Binh Thanh Dang, Huynh Thai Hoc, Shahbaz Pervez   +3 more
doaj   +1 more source

Hyperparameter Optimization in Machine Learning

Foundations and Trends® in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies.
Franceschi, Luca, Donini, Michele, Perrone, Valerio, Klein, Aaron, Archambeau, Cédric, Seeger, Matthias, Pontil, Massimiliano, Frasconi, Paolo   +7 more
openaire   +2 more sources

MGM as a Large‐Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts

Advanced Science, EarlyView.
We present the Microbial General Model (MGM), a transformer‐based foundation model pretrained on over 260,000 microbiome samples. MGM learns contextualized microbial representations via self‐supervised language modeling, enabling robust transfer learning, cross‐regional generalization, keystone taxa discovery, and prompt‐guided generation of realistic,
Haohong Zhang, Yuli Zhang, Zixin Kang, Jiayun Xiong, Ronghua Yang, Kang Ning   +5 more
wiley   +1 more source