Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods DOI

Chengyan Wu,

Zhi‐Xue Xu, Nan Li

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108207 - 108207

Published: Sept. 11, 2024

Language: Английский

Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies DOI
Faijan Akhtar, Md Belal Bin Heyat, Arshiya Sultana

et al.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Aug. 4, 2024

Abstract This comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential medical intelligence [medical + artificial (AI)]. By addressing fundamental research questions, this study investigated foundations underlying disorders, shedding light intricate interplay genetic factors contributing to etiology progression anxiety. Furthermore, delves into emerging implications biomaterials, defibrillators, state‐of‐the‐art devices for elucidating their roles in diagnosis, treatment, patient management. A pivotal contribution is development AI‐driven model analysis. innovative approach offers promising avenue enhancing precision timeliness diagnosis monitoring. Leveraging machine learning AI techniques enables accurate classification persons with based data, thereby ushering new era personalized data‐driven mental health care. Identifying themes knowledge gaps lays foundation future directions roadmap scholars practitioners navigate field. In conclusion, serves vital resource, consolidating diverse perspectives fostering deeper understanding disorders from biological, technological standpoints, ultimately advancing clinical practice. categorized under: Application Areas > Health Care Science Technology Technologies Classification

Language: Английский

Citations

7
Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies DOI
Hasan Zulfiqar,

Ramala Masood Ahmad,

Ali Raza

et al.

Methods in molecular biology, Journal Year: 2024, Volume and Issue: unknown, P. 33 - 44

Published: Jan. 1, 2024

Language: Английский

Citations

0
Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods DOI

Chengyan Wu,

Zhi‐Xue Xu, Nan Li

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108207 - 108207

Published: Sept. 11, 2024

Language: Английский

Citations

0