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Active Module Discovery: Integrated Approaches of Gene Co-Expression and PPI Networks and MicroRNA Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hatem, Ayat |
| Copyright Year | 2014 |
| Abstract | Integrating protein-protein interaction (PPI) networks with gene expression data to extract active modules is shown to be promising in detecting meaningful biomarkers for cancer and other diseases. However, current algorithms suffer from many drawbacks such as focusing only on the highly differentially expressed genes, analyzing dependencies between genes in the PPI network only; totally neglecting the genes whose interactions are not known yet, and finally using mRNA gene expression data; ignoring other types of data such as gene mutation information and microRNAs expressions. In addition, lately, using the next generation sequencing technology to sequence the mRNA (RNA-Seq) has become the new standard for gene expression. However, existing algorithms either cannot handle the RNA-Seq data, or they return large modules which are hard to analyze. Therefore, we need new approaches to address the current drawbacks while utilizing and integrating the RNA-Seq data to the module discovery process. This work explores some of the drawbacks of current active module discovery algorithms. We first discuss the differences between RNA-Seq data and microarray data. With experimental evidence, we show that RNA-Seq is more powerful than microarray in providing better active modules at the expense of generating larger ones. Therefore, new approaches are needed to handle RNA-Seq data. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://etd.ohiolink.edu/!etd.send_file?accession=osu1398949621&disposition=inline |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
