alexa
Reach Us +44-1202-068036
Intelligent Method For Risk Estimation In Breast Cancer Disease | 8122
ISSN: 2153-0769

Metabolomics:Open Access
Open Access

Like us on:

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Intelligent method for risk estimation in breast cancer disease

International Conference and Exhibition on Metabolomics & Systems Biology

Raed I. Hamed

ScientificTracks Abstracts: J Comput Sci Syst Biol

DOI: 10.4172/0974-7230.S1.02

Abstract
The liver is the main site of drug metabolism in human body. Drug metabolism in liver, converting lipophilic substrates into more polar products which are easily excreted form, occurs in two steps, phase I and phase II. Especially, phase II metabolism is important because it is fast pathway for drug elimination and closely related with excretion, but still practical models are not available. Generally, phase II transformations conjugate a highly polar group to the substrates, then produce more hydrophilic products than its substrates. We categorized these metabolic reactions into four major classes. The reactions are glucuronidation, sulfation, N-acetylation and glutathione conjugation, and enzymes responsible for those reactions are UDP-glucoronosyl transferase (UGT), sulfotransferase (SULT), N-acetyltransferase (NAT) and glutathione transferase (GST) respectively. We made four in silico substrate classification models using random forest method. ECFP_4 is selected as molecular descriptor. ECFPs are topological fingerprints for molecular characterization using Morgan algorithm to capture molecular features. These models effectively predict phase II transformational fate of a drug molecule. And we also found that suggestion of important substructure features is possible by statistical analysis of random forest models.
Biography
Hyoungjun Son is a graduate student and currently Ph.D candidate at Yonsei University.
Top