Applications of machine learning in pharmaceutical science and medicine

5^th Annual Conference on Stroke and Neurological Disorders

November 12-13, 2018 Istanbul, Turkey

Kaan Yilancioglu

Uskudar University, Turkey

Scientific Tracks Abstracts: J Neurol Disord

Abstract :

Big data and machine learning applications will undoubtedly improve efficiency of research in various medicinal and pharmaceutical fields. These applications will provide new tools of numerous health practices for physicians, consumers, insurers, and regulators. Therefore, the present-day challenge of machine learning in healthcare: finding ways to effectively collect and use many different types of data for better analysis, prevention, and treatment of individuals. Everyday, one of the biggest players is joining the team for taking part of the very near futureâ??s diagnostic and therapeutic services. In this presentation, we discuss about using artificial intelligence (AI) in pharmaceutical and medicinal sciences. Current research efforts in medicinal fields mostly include dosage management in cancer treatment, detection of various cancer types using data science. Personalized medicine is another important field and closely related to AI. Supervised learning methods allows physicians to select from more limited sets of diagnoses. For example, these methods can estimate patient risk based on symptoms and genetic information. In pharmaceutical science, the use of machine learning in early-stage drug discovery has the potential for numerous uses, from initial screening of drug compounds to predicted success rate based on biological and physicochemical factors.

Biography :

Kaan Yilancioglu has complete his PhD in Sabanci University. He is the director of TRGENMER, Uskudar University, a transgenic and epigenetic research center. He has published numerous papers in reputed journals. He is working on data science, medical genetics and molecular microbiology.

E-mail: kaanyl@gmail.com