In Silico Technologies for the Management of Age-Related and Muscle Loss-Related Diseases
A special issue of Cells (ISSN 2073-4409).
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 602
Special Issue Editors
Interests: skeletal muscle; cell culture technology; extracellular matrix biology; in silico drug design and screening
Special Issues, Collections and Topics in MDPI journals
Interests: bioinformatics; extracellular matrix biology; muscle biology; drug designing; virtual screening
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Aging is a natural and progressive decline in physiological function, resulting in decreased tissue and cellular efficacy and increased susceptibility to age-related pathologies such as neurological, cardiovascular, metabolic, musculoskeletal, and immune system disorders. In older adults, aging causes changes in body composition that affect functional abilities. These changes include decreased muscle mass, strength, and quality, as well as increased fat mass. Aging-related muscle mass loss in the lower extremities has a significant impact on mobility. Several diseases, such as sarcopenia, cachexia, and muscular dystrophy, are directly linked to muscle loss. Presently, in silico techniques, including machine learning and conventional bioinformatics, are enhancing both diagnosis and treatment, as well as advancing the study of the underlying mechanisms of aging, muscle decline, and age-related illnesses.
Novel experimental techniques have generated a vast amount of research data, providing a comprehensive understanding of these diseases. However, the analysis and utilization of these massive data necessitate the application of adapted computational methods, such as advanced artificial intelligence (AI) technologies. The appeal of AI lies in its capability to identify patterns in complex, nonlinear data without prior knowledge of biological mechanisms.
This Special Issue aims to obtain further insights into the advancement in computational studies using state-of-the-art techniques that explore the mechanisms, diagnosis, and treatment of aging and age-related and muscle loss-related diseases. Original research papers, review articles, communications, perspectives, and commentaries are welcome.
We look forward to your contributions to this Special Issue.
Prof. Dr. Inho Choi
Dr. Khurshid Ahmad
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- aging
- skeletal muscle
- muscle loss
- diseases
- in silico
- multi-omics
- artificial intelligence
- drug design
