Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/510698
Title: Advancing graph based computational approaches to decipher omic signature of diseases
Researcher: Mishra, Shreya
Guide(s): Kumar, Vibhor
Keywords: Biology
Biology and Biochemistry
Life Sciences
University: Indraprastha Institute of Information Technology, Delhi (IIIT-Delhi)
Completed Date: 2022
Abstract: Omic signatures of disease are important for personalized treatment because of the heterogeneity of diseases. Despite the advancement of computational tools, there are limited methods that can capture the latent inter-relationships between the individual components (amino acids, genes) of proteins, and transcriptomic profiles. This gap may be addressed by the graph-based learning approach in both a supervised and unsupervised way which enables the creation of scientifically driven learning problems on graphs. We used graph signal processing which implements a range of tools for processing graph signal that are functions defined over the nodes in a graph. These functions represent the individual components of a biological unit. Further, these data points at the nodes are transformed into different spaces in order to bring out the latent features of the biological unit for downstream analysis. These tools elaborate on traditional signal processing and provide access to several functionalities, including filtering and frequency analysis. In the first contribution, we devised an approach to address the noise in gene-expression profiles based on graph-wavelet-driven gene-expression filtering to enhance gene-network inference. By using this approach, we were able to demonstrate how gene regulatory networks of young and elderly lung cells are different. Additionally,we contrasted differences in gene expression in lungs infected with COVID-19 with the pattern of changes in the effect of genes brought on by ageing. IN the second contribution, we have proposed a smart graph-based embedding system in our search engine (ScEpiSearch) which is capable of embedding and providing an integrative visualization of single-cell ATAC-seq profiles from various sources regardless of the species from which they originated and batch effect. Our method(scEpiSearch) calculates distance between query cells on the basis of the similarity with reference expression and epigenome cells.
Pagination: 164 p.
URI: http://hdl.handle.net/10603/510698
Appears in Departments:Department of Computational Biology

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02_prelim pages.pdf638.34 kBAdobe PDFView/Open
03_content.pdf66.51 kBAdobe PDFView/Open
04_abstract.pdf46.15 kBAdobe PDFView/Open
05_chapter 1.pdf330.53 kBAdobe PDFView/Open
06_chapter 2.pdf12.74 MBAdobe PDFView/Open
07_chapter 3.pdf23.68 MBAdobe PDFView/Open
08_chapter 4.pdf2.49 MBAdobe PDFView/Open
09_annexures.pdf92.1 kBAdobe PDFView/Open
80_recommendation.pdf60.16 kBAdobe PDFView/Open
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