About Arwa Bin Raies Arwa Bin Raies Ph.D., Computer Science cheminformatics bioinformatics text mining computer networks Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining Events Presented Events Oct 28 - Nov 3, 2018 Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology Arwa Bin Raies, Ph.D., Computer Science Nov 1, 11:00 - 13:00 B2 L5 R5220 artificial intelligence High Performance Computing machine learning bioinformatics Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a
Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology Arwa Bin Raies, Ph.D., Computer Science Nov 1, 11:00 - 13:00 B2 L5 R5220 artificial intelligence High Performance Computing machine learning bioinformatics Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a
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