ArticleProceedings of the National Academy of Sciences of the United States of America2011
Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 47 citations in OpenAlex.
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- PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia.BioData mining · 2018Article
- Bioinformatics analysis of key genes and latent pathway interactions based on the anaplastic thyroid carcinoma gene expression profile.Oncology letters · 2017Article
- Subpathway-CorSP: Identification of metabolic subpathways via integrating expression correlations and topological features between metabolites and genes of interest within pathways.Scientific reports · 2016Article
- Dissecting dysfunctional crosstalk pathways regulated by miRNAs during glioma progression.Oncotarget · 2016Article
- Latent Pathways Identification by Microarray Expression Profiles in Thyroid-Associated Ophthalmopathy Patients.Endocrine pathology · 2015Article
- A comprehensive analysis of candidate genes and pathways in pancreatic cancer.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2015Article
- A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways.Journal of the Royal Society, Interface · 2015Article
- Interaction of key pathways in sorafenib-treated hepatocellular carcinoma based on a PCR-array.International journal of clinical and experimental pathology · 2015Article
- Functional characterization of breast cancer using pathway profiles.BMC medical genomics · 2014Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (i) metastatic cancer progression, (ii) drug treatment in human lung carcinoma cells, and (iii) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.