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Articles 1 - 3 of 3
Full-Text Articles in Translational Medical Research
Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed
Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed
Parallel Computing and Data Science Lab Technical Reports
Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for …
The Pharmacogene Variation (Pharmvar) Consortium: Incorporation Of The Human Cytochrome P450 (Cyp) Allele Nomenclature Database, Andrea Gaedigk, Magnus Ingelman-Sundberg, Neil A. Miller, J Steven Leeder, Michelle Whirl-Carrillo, Teri E. Klein
The Pharmacogene Variation (Pharmvar) Consortium: Incorporation Of The Human Cytochrome P450 (Cyp) Allele Nomenclature Database, Andrea Gaedigk, Magnus Ingelman-Sundberg, Neil A. Miller, J Steven Leeder, Michelle Whirl-Carrillo, Teri E. Klein
Manuscripts, Articles, Book Chapters and Other Papers
The Human Cytochrome P450 (CYP) Allele Nomenclature Database, a critical resource to the pharmacogenetics and genomics communities, will be transitioning to the Pharmacogene Variation (PharmVar) Consortium. In this report we provide a summary of the current database, provide an overview of the PharmVar consortium and highlight the PharmVar database which will serve as the new home for pharmacogene nomenclature.
Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones
Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones
Theses and Dissertations--Computer Science
In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …