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GeneRank: Using search engine technology for the analysis of microarray experiments
 BMC Bioinformatics
"... Background: Interpretation of simple microarray experiments is usually based on the foldchange of gene expression between a reference and a “treated ” sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of dif ..."
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Background: Interpretation of simple microarray experiments is usually based on the foldchange of gene expression between a reference and a “treated ” sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method – based on the PageRank algorithm employed by the popular search engine Google – that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. Results: GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene 1 ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Conclusions: Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed foldchange alone and may be used as a basis for further analytical developments.
Google PageRank as mean playing time for pinball on the reverse web
 Applied Mathematics Letters, 18(12):1359 – 1362
, 2005
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BMC Bioinformatics Research article GeneRank: Using search engine technology for the analysis of microarray experiments
, 2005
"... © 2005 Morrison et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ..."
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© 2005 Morrison et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License
METHODOLOGY ARTICLE A networkassisted coclus e
"... mation is critically important to tailor more effective different clusters such that items in one cluster have ..."
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mation is critically important to tailor more effective different clusters such that items in one cluster have
Random Web Surfer PageRank Algorithm
"... In this paper analyzes how the Google web search engine implements the PageRank algorithm to define prominent status to web pages in a network. It describes the PageRank algorithm as a Markov process, web page as state of Markov chain, Link structure of web as Transitions probability matrix of Marko ..."
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In this paper analyzes how the Google web search engine implements the PageRank algorithm to define prominent status to web pages in a network. It describes the PageRank algorithm as a Markov process, web page as state of Markov chain, Link structure of web as Transitions probability matrix of Markov chains, the solution to an eigenvector equation and Vector iteration power method. It mainly focus on how to relate the eigenvalues and eigenvector of Google matrix to PageRank values to guarantee that there is a single stationary distribution vector to which the PageRank algorithm converges and efficiently compute the PageRank for large sets of web Pages. Finally, it will demonstrate example of the PageRank algorithm.
unknown title
, 2014
"... This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. A networkassisted coclustering algorithm to discover cancer subtypes based on gene expression BMC Bioinformatics 2014, 15:37 doi:10.1186/14 ..."
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This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. A networkassisted coclustering algorithm to discover cancer subtypes based on gene expression BMC Bioinformatics 2014, 15:37 doi:10.1186/147121051537
MATH 51 LECTURE NOTES: HOW GOOGLE RANKS WEB PAGES
"... During a typical use of a search engine, (1) the user types a word, (2) the engine finds all web pages that contain the given word, and (3) the engine lists the pages in some order, ideally from most interesting to least interesting. When Google was launched, there were already a number of search en ..."
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During a typical use of a search engine, (1) the user types a word, (2) the engine finds all web pages that contain the given word, and (3) the engine lists the pages in some order, ideally from most interesting to least interesting. When Google was launched, there were already a number of search engines. Google’s main innovation was a superior way of doing step 3, that is, a better way of ranking web pages. Google’s method is called the PageRank algorithm and was developed by Google founders Sergey Brin and Larry Page while they were graduate students at Stanford. At that time, Brin and Page were 23 and 24 years old, respectively. In the PageRank method, the ranking of pages is based solely on how pages are linked and not on the content of the pages or on how often the pages are visited. Of course the web is constantly changing, so the rankings change, too. Google calculates the ranks once a month. Each time you do a search, Google gives a list of the relevant pages in order of that month’s ranking. The rankings form the entries of a vector v ∈ RN, where N is the total number of pages on the web. The entries of v are nonnegative numbers, and vi is supposed to be a measure of the value (or interest or importance) of page i. Thus in a given search, the first page Google will list is the page i (among those containing the search word) with the highest value of vi. In this note, we describe a very simple ranking vector v, followed by several improvements leading to the PageRank method. 1. The Very Simple Method If page j has a link to page i, imagine that page j is recommending page i. One could rank pages purely by the number of recommendations each page gets. Let N be the total number of web pages (over 4 billion, according to Google.) Let A be the N by N matrix whose ij entry is aij = 1 if page j has a link to page i 0 if not. Then the number of recommendations that page i gets is ai1 + ai2 + · · ·+ ain. We can think of this as a measure of the “value ” vi of page i (1) vi = ai1 + ai2 + · · ·+ ain.
GeneRank: Using search engine technology for the analysis of
, 2005
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