@article {48103,
	title = {Review on computational analysis of big data in breast cancer for predicting potential biomarkers},
	journal = {Current Topics in Medicinal Chemistry},
	volume = {22},
	year = {2022},
	month = {SEP},
	pages = {1793-1810},
	type = {Review},
	abstract = {<p>
	Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.</p>
},
	keywords = {Big data, Biomarkers, Breast cancer, Driver genes, Network analysis, text mining},
	issn = {1568-0266},
	doi = {10.2174/1568026622666220907121942},
	author = {Shaikh, Nilofer and Bapat, Sanket and Karthikeyan, Muthukumarasamy and Vyas, Renu}
}
@article {48449,
	title = {Identification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma},
	journal = {Molecular Genetics and Genomics},
	volume = {298},
	year = {2023},
	month = {JAN},
	pages = {161-181},
	type = {Article},
	abstract = {<p>
	MicroRNAs (miRNAs) play important role in regulating cellular metabolism, and are currently being explored in cancer. As metabolic reprogramming in cancer is a major mediator of phenotypic plasticity, understanding miRNA-regulated metabolism will provide opportunities to identify miRNA targets that can regulate oncogenic phenotypes by taking control of cellular metabolism. In the present work, we studied the effect of differentially expressed miRNAs on metabolism, and associated oncogenic phenotypes in glioblastoma (GBM) using patient-derived data. Networks of differentially expressed miRNAs and metabolic genes were created and analyzed to identify important miRNAs that regulate major metabolism in GBM. Graph network-based approaches like network diffusion, backbone extraction, and different centrality measures were used to analyze these networks for identification of potential miRNA targets. Important metabolic processes and cellular phenotypes were annotated to trace the functional responses associated with these miRNA-regulated metabolic genes and associated phenotype networks. miRNA-regulated metabolic gene subnetworks of cellular phenotypes were extracted, and important miRNAs regulating these phenotypes were identified. The most important outcome of the study is the target miRNA combinations predicted for five different oncogenic phenotypes that can be tested experimentally for miRNA-based therapeutic design in GBM. Strategies implemented in the study can be used to generate testable hypotheses in other cancer types as well, and design context-specific miRNA-based therapy for individual patient. Their usability can be further extended to other gene regulatory networks in cancer and other genetic diseases.</p>
},
	keywords = {Cellular phenotypes, Functional analysis, Glioblastoma metabolism, microRNA, miRNA-based therapy, Network analysis, Transcriptome},
	issn = {1617-4615},
	doi = {10.1007/s00438-022-01966-3},
	author = {Bhowmick, Rupa and Sarkar, Ram Rup}
}
