Metabolic Model Refinement Using Phenotypic Microarray Data

2013 ◽  
pp. 47-59 ◽  
Author(s):  
Pratish Gawand ◽  
Laurence Yang ◽  
William R. Cluett ◽  
Radhakrishnan Mahadevan
Keyword(s):  
Microarray Data ◽  
Metabolic Model ◽  
Model Refinement ◽  
Phenotypic Microarray

scholarly journals Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0118392 ◽  
Author(s):  
Minna Vehkala ◽  
Mikhail Shubin ◽  
Thomas R Connor ◽  
Nicholas R Thomson ◽  
Jukka Corander
Keyword(s):  
Microarray Data ◽  
Phenotypic Microarray

Expression and network analysis of Illumina microarray data

2009 ◽  
Author(s):  
Giovanni Coppola ◽  
Kellen Winden ◽  
Genevieve Konopka ◽  
Fuying Gao ◽  
Daniel Geschwind
Keyword(s):  
Network Analysis ◽  
Microarray Data

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

The tyrosine kinase TEK is differentially expressed in non-small cell lung adenocarcinomas.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
United States ◽  
Tyrosine Kinase ◽  
Lung Adenocarcinoma ◽  
Microarray Data ◽  
The United States ◽  
Small Cell ◽  
Small Cell Lung ◽  
Significant Differential Expression ◽  
Lung Cancers ◽  
Lung Adenocarcinomas

Non-small cell lung adenocarcinoma (NSCLC) is a leading cause of death in the United States and worldwide (1, 2). We mined published microarray data (3, 4, 5) to discover genes associated with NSCLC. We identified significant differential expression of the tyrosine kinase TEK in tumors from patients with NSCLC. TEK may be of relevance to the initiation, progression or maintenance of non-small cell lung cancers.

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