01751nas a2200205   4500008004100000022001400041245006800055210006600123260012100189300001200310490000700322520099900329653001501328653001601343653002001359653002201379100002001401700002401421856010001445       2012                        eng d  a1229-919700aElectrospinning of PHBV/ZnO membranes: structure and properties0 aElectrospinning of PHBVZnO membranes structure and properties  aKOREA SCIENCE TECHNOLOGY CTR \#501 635-4 YEOGSAM-DONG, KANGNAM-GU, SEOUL 135-703, SOUTH KOREAbKOREAN FIBER SOCcJUL  a692-6970 v133 a<p>Polyhydroxy butyrate-co-valerate (PHBV) - Zinc oxide (ZnO) nano composite fibers were prepared using electrospinning. The structural and optical properties were studied using Fourier transform infrared spectroscopy (FT-ER), X-ray diffraction (XRD) and photoluminescence study (PL). The morphology observed with scanning electron microscope (SEM) revealed no significant changes in the nano composite fibers as compared to bare polymer. The low concentration of ZnO nanoparticles resulted in an increase in overall crystallinity of the polymer matrix which was confirmed from FT-IR and XRD results. The photoluminescence (PL) study indicated the quenching of visible emission in the composite fibers. The ratio of UV to visible emission (I-uv/I-vis) intensity was found to be 12.8 times enhanced in the composite fibers compared to bare ZnO nanoparticles. The nanofibrous mats are self supported and hence offer potential applications in optoelectronic devices and the biomedical imaging.</p>10aBiomarkers10aBiopolymers10aelectrospinning10aZnO nanoparticles1 aNaphade, Rounak1 aJog, Jyoti, Prakash  uhttps://library.ncl.res.in/content/electrospinning-phbvzno-membranes-structure-and-properties-002031nas 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-biomarkers