Retrospective group fusion similarity search based on eROCE evaluation metric

2013 ◽  
Vol 21 (5) ◽  
pp. 1268-1278 ◽  
Author(s):  
Sorin I. Avram ◽  
Luminita Crisan ◽  
Alina Bora ◽  
Liliana M. Pacureanu ◽  
Stefana Avram ◽  
...  
2009 ◽  
Vol 20 (10) ◽  
pp. 2867-2884 ◽  
Author(s):  
Feng WU ◽  
Yan ZHONG ◽  
Quan-Yuan WU ◽  
Yan JIA ◽  
Shu-Qiang YANG

2020 ◽  
Vol 16 (4) ◽  
pp. 473-485
Author(s):  
David Mary Rajathei ◽  
Subbiah Parthasarathy ◽  
Samuel Selvaraj

Background: Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been synthesized experimentally which would be expensive and time-consuming. Methods: In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search. Results and Discussion: The three-descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2 ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarity search and the inhibition activities of the compounds were predicted using QSAR model, which were close in the range of experimentally observed threshold. Conclusion: The present study suggests that the QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Frederick S. Vizeacoumar ◽  
Hongyu Guo ◽  
Lynn Dwernychuk ◽  
Adnan Zaidi ◽  
Andrew Freywald ◽  
...  

AbstractGastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.


2016 ◽  
Vol 51 (8) ◽  
pp. 1-12
Author(s):  
Sandeep R. Agrawal ◽  
Christopher M. Dee ◽  
Alvin R. Lebeck

Sign in / Sign up

Export Citation Format

Share Document