Publications by authors named "Aamir Zainulabadeen"
Article Synopsis
- Analyzing omics data types separately can limit the ability to find important variations that are consistent across different assays.
- Integrating multiple omics data types into one model can enhance statistical power, but it poses challenges due to the different levels at which these data are measured.
- The iNETgrate package was developed to integrate transcriptome and DNA methylation data into a single gene network, showing improved prognostic capabilities over standard clinical methods in five independent datasets.
Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package (https://bioconductor.
View Article and Find Full Text PDFIntegration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas ( = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups.
View Article and Find Full Text PDFBecause the prognosis of melanoma is challenging and inaccurate when using current clinical approaches, clinicians are seeking more accurate molecular markers to improve risk models. Accordingly, we performed a survival analysis on 404 samples from The Cancer Genome Atlas (TCGA) cohort of skin cutaneous melanoma. Using our recently developed gene network model, we identified biological signatures that confidently predict the prognosis of melanoma (p-value < 10-5).
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