Artificial Intelligence in Central-Peripheral Interaction Organ Crosstalk: The Future of Drug Discovery and Clinical Trials DOI Creative Commons

Yufeng Chen,

Mingrui Yang, Qian Hua

и другие.

Pharmacological Research, Год журнала: 2025, Номер unknown, С. 107734 - 107734

Опубликована: Апрель 1, 2025

Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture complexity of biological systems. The emergence protein-protein interaction network studies in 2001 marked a turning point and promoted holistic approach that considers human body as an interconnected system. This is particularly evident study bidirectional interactions between central nervous system (CNS) peripheral which are critical for understanding health disease. Understanding these complex requires integrating multi-scale, heterogeneous data from molecular organ levels, encompassing both omics (e.g., genomics, proteomics, microbiomics) non-omics imaging, clinical phenotypes). Artificial intelligence (AI), multi-modal models, has demonstrated significant potential analyzing CNS-peripheral by processing vast, datasets. Specifically, AI facilitates identification biomarkers, prediction therapeutic targets, simulation drug effects multi-organ systems, thereby paving way novel strategies. review highlights AI's transformative role research, focusing its applications unraveling disease mechanisms, discovering optimizing trials through patient stratification adaptive trial design.

Язык: Английский

Advanced Analysis of Alpha EEG Patterns for Identifying Meditative States in Alpha Power Activation Yoga (APAY) DOI Open Access
K. Ravikumar,

Pravin R. Kshirsagar,

R Thiagarajan

и другие.

International Research Journal of Multidisciplinary Technovation, Год журнала: 2025, Номер unknown, С. 148 - 164

Опубликована: Март 30, 2025

Meditation, especially Alpha-Power Activation Yoga (APAY), is popular today for well-being. Apay promotes relaxation and focuses using yoga attention. However, the inspiring settings app effectiveness evaluation made challenging. EEG can measure attentive brain activity. This work improves Alfa pattern analysis discovery of EFEM. functions are classified through moral machine learning time. approach reflects neurological attention process. Preliminary research found that alpha-EEG patterns change with training stages such as concentration, absorption relaxation. Deep concentration reduces hiking increases frontal lateral regions. Constant front behind alpha, suggests treatment sensory awareness. shows app-inspired requires more study to understand neurophysiology. Strong biomarker will track skill changes its mental health benefits. Kaggle Alpha Wave Dataset detects meditation (closes eyes) non-meditation (opening when relaxing subject. In this dataset, decisions identify accurately trees in decision, innocent bays random forest phenomena. These findings be repeated a large population investigated see how monkey practice affects psychological processes over Researchers brainwave emotional welfare connections explain these results. It inspire new -based treatments. Doctors provide better care by adding techniques parting treatment. A full disposition goal improve awareness body. show diet exercise affect health.

Язык: Английский

Процитировано

0
Artificial Intelligence in Central-Peripheral Interaction Organ Crosstalk: The Future of Drug Discovery and Clinical Trials DOI Creative Commons

Yufeng Chen,

Mingrui Yang, Qian Hua

и другие.

Pharmacological Research, Год журнала: 2025, Номер unknown, С. 107734 - 107734

Опубликована: Апрель 1, 2025

Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture complexity of biological systems. The emergence protein-protein interaction network studies in 2001 marked a turning point and promoted holistic approach that considers human body as an interconnected system. This is particularly evident study bidirectional interactions between central nervous system (CNS) peripheral which are critical for understanding health disease. Understanding these complex requires integrating multi-scale, heterogeneous data from molecular organ levels, encompassing both omics (e.g., genomics, proteomics, microbiomics) non-omics imaging, clinical phenotypes). Artificial intelligence (AI), multi-modal models, has demonstrated significant potential analyzing CNS-peripheral by processing vast, datasets. Specifically, AI facilitates identification biomarkers, prediction therapeutic targets, simulation drug effects multi-organ systems, thereby paving way novel strategies. review highlights AI's transformative role research, focusing its applications unraveling disease mechanisms, discovering optimizing trials through patient stratification adaptive trial design.

Язык: Английский

Процитировано

0