scholarly journals Predicting the Functions of Unknown Protein by Analyzing Known Protein Interaction: A Survey

2018 ◽  
Vol 11 (3) ◽  
pp. 1707-1715 ◽  
Author(s):  
Rohini Mugur ◽  
P. S Smitha ◽  
M. S. Pallavi
Keyword(s):  
Protein Complexes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interacting Proteins ◽  
Microarray Gene Expression ◽  
Experimental Proof ◽  
Graph Theoretic ◽  
Unknown Protein ◽  
Markov Clustering ◽  
Physical Interactions

The Protein complexes from PPIs are responsible for the important biological processes about the cell and learning the functionality under these biological process need uncovering and learning complexes and related interacting proteins. One way for studying and dealing with this PPI involves Markov Clustering (MCL) algorithm and has successfully produced result, due to its efficiency and accuracy. The Markov clustering produced result contains clusters which are noisy, these wont represent any complexes that are known or will contains additional noisy proteins which will impact on the correctness of correctly predicted complexes. And correctly predicted correctness of these clusters works well with matched and complexes that are known are quite less. Increasing in the clusters will eventually improve the correctness required to understand and organize of these complexes. The consistency of experimental proof varies largely techniques for assessing quality that have been prepared and used to find the most suitable subset of the interacting proteins. The physical interactions between the proteins are complimented by the, amplitude of data regarding the various types of functional associations among proteins, which includes interactions between the gene, shared evolutionary history and about co-expression. This technique involves the facts and figures from interactions between the proteins, microarray gene-expression profiles, protein complexes, and practical observations for proteins that are known. Clusters communicate not only to protein complex but they also interact with other set proteins by this, graph theoretic clustering method will drop the dynamic interaction by producing false positive rates.

scholarly journals Computational analysis of microarray gene expression profiles of lung cancer

2016 ◽  
Vol 32 (1) ◽  
pp. 70-79 ◽  
Author(s):  
S. A. Babichev ◽  
A. I. Kornelyuk ◽  
V. I. Lytvynenko ◽  
V. V. Osypenko
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Computational Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression ◽  
Microarray Gene

scholarly journals Exploring MicroRNA Biomarkers for Parkinson’s Disease from mRNA Expression Profiles

Cells ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 245 ◽  
Author(s):  
Y-h. Taguchi ◽  
Hsiuying Wang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Principal Component ◽  
Signs And Symptoms ◽  
Microarray Gene Expression ◽  
Kegg Pathways ◽  
Healthy Control ◽  
Physical Examinations

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disease characterized by both motor and nonmotor features. The diagnose of PD is based on a review of patients’ signs and symptoms, and neurological and physical examinations. So far, no tests have been devised that can conclusively diagnose PD. In this study, we explore both microRNA and gene biomarkers for PD. Microarray gene expression profiles for PD patients and healthy control are analyzed using a principal component analysis (PCA)-based unsupervised feature extraction (FE). 244 genes are selected to be potential gene biomarkers for PD. In addition, we implement these genes into Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and find that the 15 microRNAs (miRNAs), hsa-miR-92a-3p, 16-5p, 615-3p, 877-3p, 100-5p, 320a, 877-5p, 23a-3p, 484, 23b-3p, 15a-5p, 324-3p, 19b-3p, 7b-5p and 505-3p, significantly target these 244 genes. These miRNAs are shown to be significantly related to PD. This reveals that both selected genes and miRNAs are potential biomarkers for PD.

scholarly journals Mycoplasma infection significantly alters microarray gene expression profiles

BioTechniques ◽  
2003 ◽  
Vol 35 (4) ◽  
pp. 812-814 ◽  
Author(s):  
Crispin J. Miller ◽  
Heba S. Kassem ◽  
Stuart D. Pepper ◽  
Yvonne Hey ◽  
Timothy H. Ward ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mycoplasma Infection ◽  
Microarray Gene Expression ◽  
Microarray Gene

A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

2004 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Markus Ruschhaupt ◽  
Wolfgang Huber ◽  
Annemarie Poustka ◽  
Ulrich Mansmann
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Statistical Processing ◽  
Primary Data ◽  
High Dimensional ◽  
Microarray Gene Expression ◽  
Data Set ◽  
Dimensional Classification ◽  
Biological Interpretation

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

scholarly journals Differential gene expression patterns in vein regions susceptible versus resistant to neointimal hyperplasia

2018 ◽  
Vol 50 (8) ◽  
pp. 615-627
Author(s):  
Sun Hyung Kwon ◽  
Li Li ◽  
Christi M. Terry ◽  
Yan-Ting Shiu ◽  
Philip J. Moos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vein Graft ◽  
Expression Profiles ◽  
Neointimal Hyperplasia ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Yield Potential ◽  
Microarray Gene Expression ◽  
Graft Placement ◽  
Time Points

Arteriovenous hemodialysis graft (AVG) stenosis results in thrombosis and AVG failure, but prevention of stenosis has been unsuccessful due in large part to our limited understanding of the molecular processes involved in neointimal hyperplasia (NH) formation. AVG stenosis develops chiefly as a consequence of highly localized NH formation in the vein-graft anastomosis region. Surprisingly, the vein region just downstream of the vein-graft anastomosis (herein termed proximal vein region) is relatively resistant to NH. We hypothesized that the gene expression profiles of the NH-prone and NH-resistant regions will be different from each other after graft placement, and analysis of their genomic profiles may yield potential therapeutic targets to prevent AVG stenosis. To test this, we evaluated the vein-graft anastomosis (NH-prone) and proximal vein (NH-resistant) regions in a porcine model of AVG stenosis with a porcine microarray. Gene expression changes in these two distinct vein regions, relative to the gene expression in unoperated control veins, were examined at early (5 days) and later (14 days) time points following graft placement. Global genomic changes were much greater in the NH-prone region than in the NH-resistant region at both time points. In the NH-prone region, genes related to regulation of cell proliferation and osteo-/chondrogenic vascular remodeling were most enriched among the significantly upregulated genes, and genes related to smooth muscle phenotype were significantly downregulated. These results provide insights into the spatial and temporal genomic modulation underlying NH formation in AVG and suggest potential therapeutic strategies to prevent and/or limit AVG stenosis.

scholarly journals A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes

2020 ◽  
Author(s):  
Christopher A Mancuso ◽  
Jacob L Canfield ◽  
Deepak Singla ◽  
Arjun Krishnan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Microarray Gene Expression ◽  
Extensive Evaluation ◽  
Training Samples ◽  
Target Sample

AbstractWhile there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms (LASSO, k-nearest-neighbors, and deep-neural-networks), two gene subsets (GPL96-570 and LINCS), and three imputation tasks (within and across microarray/RNA-seq) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.

Sign in / Sign up

Export Citation Format

Share Document