About Othman Soufan Othman Soufan Ph.D., Computer Science artificial intelligence data mining bioinformatics cheminformatics toxicogenomics drug discovery Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent Events Presented Events Nov 13 - Nov 19, 2016 Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan Othman Soufan, Ph.D., Computer Science Nov 16, 14:00 - 15:00 H2 B9 machine learning data mining Computational biology biomedical applications Chemical compounds visualization Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its
Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan Othman Soufan, Ph.D., Computer Science Nov 16, 14:00 - 15:00 H2 B9 machine learning data mining Computational biology biomedical applications Chemical compounds visualization Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its
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