02031nas a2200253   4500008004100000022001400041245010200055210006900157260000800226300001400234490000700248520121200255653001301467653001501480653001801495653001701513653002101530653001601551100002001567700001801587700003301605700001501638856012401653       2022                        eng d  a1568-026600aReview on computational analysis of big data in breast cancer for predicting potential biomarkers0 aReview on computational analysis of big data in breast cancer fo  cSEP  a1793-18100 v223 a<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>
10aBig data10aBiomarkers10aBreast cancer10aDriver genes10aNetwork analysis10atext mining1 aShaikh, Nilofer1 aBapat, Sanket1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu  uhttps://library.ncl.res.in/content/review-computational-analysis-big-data-breast-cancer-predicting-potential-biomarkers02587nas a2200241   4500008004100000022001400041245011400055210006900169260000800238300001200246490000800258520175700266653002402023653002402047653002802071653001302099653002402112653002102136653001802157100001902175700002102194856013002215       2023                        eng d  a1617-461500aIdentification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma0 aIdentification of potential microRNAs regulating metabolic plast  cJAN  a161-1810 v2983 a<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>
10aCellular phenotypes10aFunctional analysis10aGlioblastoma metabolism10amicroRNA10amiRNA-based therapy10aNetwork analysis10aTranscriptome1 aBhowmick, Rupa1 aSarkar, Ram, Rup  uhttps://library.ncl.res.in/content/identification-potential-micrornas-regulating-metabolic-plasticity-and-cellular-phenotypes