GeneRank: Using search engine technology for the analysis of microarray experiments. Background: Interpretation of simple microarray experiments is usually based on the fold-change 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 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. Conclusion: 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 fold-change alone and may be used as a basis for further analytical developments.

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  1. Irie, Yuki: Walks: a beginner’s guide to graphs and matrices (2020)
  2. Ding, Weiyang; Ng, Michael; Wei, Yimin: Fast computation of stationary joint probability distribution of sparse Markov chains (2018)
  3. Kepner, Jeremy; Jananthan, Hayden: Mathematics of big data. Spreadsheets, databases, matrices, and graphs. With a foreword by Charles E. Leiserson (2018)
  4. Xie, Ya-Jun; Ma, Chang-Feng: A relaxed two-step splitting iteration method for computing PageRank (2018)
  5. Tan, Xueyuan: A new extrapolation method for PageRank computations (2017)
  6. Sonobe, Tomohiro: An efficient Monte Carlo approach to compute PageRank for large graphs on a single PC (2016)
  7. Gleich, David F.: PageRank beyond the web (2015)
  8. Gleich, David F.; Lim, Lek-Heng; Yu, Yongyang: Multilinear PageRank (2015)
  9. Huang, Na; Ma, Chang-Feng: Parallel multisplitting iteration methods based on M-splitting for the PageRank problem (2015)
  10. Oates, Chris J.; Amos, Richard; Spencer, Simon E. F.: Quantifying the multi-scale performance of network inference algorithms (2014)
  11. Salkuyeh, Davod Khojasteh; Edalatpour, Vahid; Hezari, Davod: Polynomial preconditioning for the generank problem (2014)
  12. Benzi, Michele; Kuhlemann, Verena: Chebyshev acceleration of the GeneRank algorithm (2013)
  13. Gu, Chuanqing; Wang, Lei: On the multi-splitting iteration method for computing PageRank (2013)
  14. Wu, Gang; Xu, Wei; Zhang, Ying; Wei, Yimin: A preconditioned conjugate gradient algorithm for GeneRank with application to microarray data mining (2013)
  15. Agarwal, Shivani: Learning to rank on graphs (2010)
  16. Wu, Gang; Wei, Yimin: An Arnoldi-extrapolation algorithm for computing pagerank (2010)
  17. Denton, Anne M.: Subspace sums for extracting non-random data from massive noise (2009) ioport