A novel approach to significant pathway identification using pathway interaction network from PPI data

Jadamba, Erkhembayar; Shin, Moo Whan

doi:10.1007/s13206-014-8104-4

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…For instance, in context of disease-specific datasets, enrichment analysis of disease-specific genes can be a vital means of filtering a large number of altered pathways to eliminate the pathways with little therapeutic relevance. Methods like PIN-PageRank18 demonstrated significant improvement in prediction of key pathways using known disease genes. Yet none of the available tools, except PAGI19, exploit supervised algorithm for identification of components important for disease progression within altered pathways, though PAGI method simply labels all significant Differentially Expressed (DE) genes as disease genes.…”

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confidence: 99%

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Kaushik

Ali

Gupta

2017

Sci Rep

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Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

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confidence: 99%

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Kaushik

Ali

Gupta

2017

Sci Rep

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“…The reproducibility of gene will determine the robustness and significant level towards cancer. The higher the reproducibility of genes, the more the robustness and significant level of respective gene towards cancer (Jadamba and Shin, 2014). Pathway that contain higher significant level of gene will be predicted as risk pathway and further evaluated by restart probabilities.…”

Section: Methodsmentioning

confidence: 99%

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Seah

Kasim

Fudzee

et al. 2017

Saudi Journal of Biological Sciences

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Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

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Pathway Marker Identification Using Gene Expression Data Analysis: A Particle Swarm Optimisation Approach

Ghosh

Mitra

Acharyya

2021

Advanced Techniques for IoT Applications

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A novel approach to significant pathway identification using pathway interaction network from PPI data

Cited by 4 publications

References 16 publications

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

Pathway Marker Identification Using Gene Expression Data Analysis: A Particle Swarm Optimisation Approach

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