Results 101 to 110 of about 11,467,392 (329)

Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews
The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior.
M. Kulichenko, B. Nebgen, N. Lubbers, Justin S. Smith, Kipton Barros, Alice E. A. Allen, Adela Habib, Emily Shinkle, Nikita Fedik, Ying-Wai Li, Richard A. Messerly, Sergei Tretiak   +11 more
semanticscholar   +1 more source

Longitudinal Assessment of Biomarkers in ALS: Discriminative Biomarkers for Disease Progression and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To assess the association and discriminative performance of serum biomarkers with clinical disease progression and survival in patients with amyotrophic lateral sclerosis (ALS). Methods This retrospective study, conducted at Houston Methodist Hospital, Houston, TX, used longitudinal serum samples collected between January 2018 and ...
David R. Beers, Yueh‐Yun Lin, Jason R. Thonhoff, Aaron D. Thome, Alireza Faridar, Weihua Zhao, Shixiang Wen, Stanley H. Appel   +7 more
wiley   +1 more source

Enhancing diabetes risk prediction through focal active learning and machine learning models

PLoS ONE
To improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with machine learning models.
Wangyouchen Zhang, Zhenhua Xia, Guoqing Cai, Junhao Wang, Xutao Dong   +4 more
doaj   +2 more sources

Discovery of antimicrobial peptides in the global microbiome with machine learning

Cell
Summary Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes
C. D. Santos-Júnior, M. D. Torres, Yiqian Duan, Álvaro Rodríguez del Río, T. Schmidt, Hui Chong, A. Fullam, Michael Kuhn, Chengkai Zhu, Amy Houseman, Jelena Somborski, Anna Vines, Xing-Ming Zhao, P. Bork, Jaime Huerta-Cepas, Cesar de la Fuente-Nunez, Luís Pedro Coelho   +16 more
semanticscholar   +1 more source

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

Higher Education: Increased and Equitable Access to Quality Learning Opportunities

, 2019
Given that many developing countries seek to increase participation in Higher Education (HE), COL will work with Ministries of Education and HE Institutions to support capacity building including the development and implementation of Open and Distance ...
Commonwealth of Learning
core   +1 more source

regAL: Python package for active learning of regression problems

Machine Learning: Science and Technology
Increasingly more research areas rely on machine learning methods to accelerate discovery while saving resources. Machine learning models, however, usually require large datasets of experimental or computational results, which in certain fields—such as ...
Elizaveta Surzhikova, Jonny Proppe
doaj   +1 more source

Genetic programming and deductive-inductive learning: a multistrategy approach [PDF]

, 1998
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24-27, 1998.Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions ...
Borrajo, Daniel, Aler, Ricardo, Borrajo Millán, Daniel, Aler Mur, Ricardo, Isasi, Pedro, Isasi Viñuela, Pedro   +5 more
core   +1 more source

A Web Survey on the Use of Active Learning to Support Annotation of Text Data [PDF]

, 2009
As supervised machine learning methods for addressing tasks in natural language processing (NLP) prove increasingly viable, the focus of attention is naturally shifted towards the creation of training data.
Olsson, Fredrik, Fredrik Olsson, Katrin Tomanek, Tomanek, Katrin   +3 more
core   +1 more source

Early Clinical, Imaging, and Pathological Characteristics of SRPK3/TTN‐Digenic Myopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective SRPK3/TTN‐digenic myopathy was recently established as a skeletal muscle myopathy caused by digenic inheritance. This study characterizes the early clinical presentation of SRPK3/TTN‐digenic myopathy in one previously reported and seven newly identified pediatric patients.
Rotem Orbach, Sandra Donkervoort, Carola Hedberg‐Oldfors, Giovanni Baranello, Dimah Saade, Precilla D'Souza, Ruchee Patel, Eva Michael, A. Reghan Foley, Diana Bharucha‐Goebel, S. Jin Haugland, Meghan McAnally, Omer Abdul Hamid, Katherine Chao, Ellen F. Macnamara, Alan H. Beggs, Anna Sarkozy, Juliane Mueller, Steven A. Moore, Richard S. Finkel, Cynthia J. Tifft, Francesco Muntoni, Anders Oldfors, Carsten G. Bönnemann   +23 more
wiley   +1 more source