Hilmi Uysal | Biology and Life Sciences | Hypothesis Achievement Award

Prof. Dr. Hilmi Uysal | Biology and Life Sciences | Hypothesis Achievement Award

Prof. Dr. Hilmi Uysal | Biology and Life Sciences | Retired at Akdeniz University | Turkey 

Prof. Dr. Hilmi Uysal is a distinguished clinician–scientist and academic neurologist with internationally recognized expertise in neuromuscular and neurodegenerative disorders, particularly amyotrophic lateral sclerosis, spinal muscular atrophy, cortical plasticity, and clinical neurophysiology. He holds a Ph.D. in a neuroscience-related discipline and is affiliated with Akdeniz University, where he combines advanced clinical practice with hypothesis-driven biomedical research. Prof. Dr. Hilmi Uysal has extensive professional experience in neurological diagnostics, electrophysiology, and translational neuroscience, contributing to the development of improved diagnostic pathways, biomarker-driven disease characterization, and treatment monitoring strategies. His research interests encompass motor neuron diseases, neuromuscular junction disorders, neuroplasticity following injury or transplantation, genetic and molecular mechanisms of neurological diseases, and data-driven clinical analytics. He is particularly skilled in electromyography, nerve conduction studies, transcranial magnetic stimulation, MUNE techniques, advanced neuroimaging interpretation, and integrative clinical research methodologies. Prof. Dr. Hilmi Uysal has played a significant role in multicenter and international research collaborations, supporting cross-border studies that enhance global neurological knowledge and patient outcomes.

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Featured Publications

Adaptive neural mechanisms of self face recognition after face transplantation
– Scientific Reports, 2025
Identification of a presymptomatic and early disease signature for amyotrophic lateral sclerosis (ALS): Protocol of the premodiALS study
– Neurological Research and Practice, 2025 
Evaluating the effects of nusinersen treatment in adults with spinal muscular atrophy using axonal excitability and MScanFit MUNE
– Muscle & Nerve, 2025 
Comparison of 3D surface and landmark-based analysis methods: The reliability and efficiency in determining asymmetry after facial palsy
– Journal of Plastic Reconstructive and Aesthetic Surgery, 2025 
Gut microbiota and short-chain fatty acid profiles in facioscapulohumeral dystrophy: Associations with epigenetic alterations
– Canadian Journal of Neurological Sciences, 2025

Álvaro Torres-Martos | Biology and Life Sciences | Best Researcher Award

Álvaro Torres-Martos | Biology and Life Sciences | Best Researcher Award

Mr Álvaro Torres-Martos, University of Granada, Spain

Based on the impressive research experience and academic achievements of Mr. Álvaro Torres-Martos, he appears to be a strong candidate for the Best Researcher Award.

Publication profile

google scholar

Research Experience

Mr. Torres-Martos has been deeply involved in cutting-edge research projects. His work as a Bioinformatic Researcher on the ACTIBATE project, focused on exploring therapeutic targets through exercise and brown adipose tissue activation, demonstrates his expertise in bioinformatics. His role as a Pre-doctoral Researcher on the EXOMAIR project, which aims to use explicable artificial intelligence for precision medicine, further highlights his research excellence and potential impact in the field of metabolic health.

Academic Background

With a Bachelor’s degree in Biochemistry and a Master’s in Bioinformatics and Biostatistics, Mr. Torres-Martos has a solid foundation in the sciences, further strengthened by his ongoing pre-doctoral studies and faculty position in Biochemistry and Molecular Biology at the University of Granada. His academic progression reflects a commitment to interdisciplinary research, particularly in bioinformatics and precision medicine.

Research Publications

Mr. Torres-Martos has contributed to high-impact journals, with publications that showcase his expertise in multi-omics, machine learning, and their applications in childhood obesity and insulin resistance. His research has been recognized in well-regarded journals like Artificial Intelligence in Medicine and Translational Psychiatry, with his work cited by other scholars, indicating the relevance and influence of his studies in the scientific community.

Other Achievements

Mr. Torres-Martos’s achievements extend beyond publications. His victory in the III Bioinformatics Datathon and the development of the ObMetrics app highlight his practical contributions to the field. Additionally, his active participation in national and international conferences and completion of specialized courses in data science and bioinformatics demonstrate his dedication to continuous learning and knowledge dissemination.

Conclusion

In conclusion, Mr. Álvaro Torres-Martos has established himself as a highly skilled researcher with a significant impact on the fields of bioinformatics, precision medicine, and metabolic health. His academic background, research experience, and substantial contributions to high-impact journals make him a deserving candidate for the Best Researcher Award. His ongoing commitment to advancing knowledge in bioinformatics and his innovative approaches to addressing complex health issues position him as a leading researcher with a promising future.

Publication top notes

Impact of physical activity and exercise on the epigenome in skeletal muscle and effects on systemic metabolism

Omics data preprocessing for machine learning: A case study in childhood obesity

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Human multi-omics data pre-processing for predictive purposes using machine learning: a case study in childhood obesity

Integrative analysis of blood cells DNA methylation, transcriptomics and genomics identifies novel epigenetic regulatory mechanisms of insulin resistance during puberty in …

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study

Leveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity

An Unhealthy Dietary Pattern-Related Metabolic Signature Is Associated with Cardiometabolic and Mortality Outcomes: A Prospective Analysis of the UK Biobank Cohort

Big Data and Machine Learning as Tools for the Biomedical Field

Epigenetic Alterations in the Estrogen Receptor Accompany the Development of Obesity-Associated Insulin Resistance during Sexual Maturation