Identifying MicroRNA Markers From Expression Data: A Network...

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BioGenes 的 HCP 监测方法成功的 ELISA 开发策略试剂表征的正交方法今天就向我展示网络研讨会！宿主细胞蛋白网络研讨会 2020”宿主细胞蛋白 ELISA 开发中的挑战和解决方案”

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通用与特定 HCP ELISA 格式我们的强大抗 HCP 抗体方法样品/试剂表征的正交方法是的，我想观看网络研讨会主页资源当前：海报海报

建立评估临床样本中治疗性反义寡核苷酸免疫原性的测定方法 Nikitas Bistolas、Beate Habel、Hans Baumeister (FyoniBio)、Jenny Pressl、Claudia Zahn (BioGenes)2022（了解更多）

宿主细胞蛋白抗体表征的方法学挑战 Pia Paarmann 博士和 Stefan Sommerschuh (BioGenes) 博士。 Mirko Sobotta（勃林格殷格翰）2019（了解更多）

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特定宿主细胞蛋白检测的开发用于植物表达系统小立碗藓 (Moss) Stefan Sommerschuh、Paulina Dabrowska-Schlepp、Claudia Geserick、Nicole Gliese、Mathias Knappenberger、Holger Niederkrüger 和 Andreas Schaaf2016（了解更多）

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BioGenes 可根据要求提供补充 MS 服务，以识别客户 DSP 样品中难以去除的 HCP，并通过正交方法可靠地评估 HCP ELISA 试剂。这些服务是与我们的合作伙伴 Alphalyse 和 FyoniBio 合作提供的。

HCP 试剂表征和风险评估

ELISA 试剂表征对于确定检测方法是否适合 HCP 检测和定量至关重要。根据当前指南的建议，应使用多种正交方法来确定覆盖范围。 IAC-2D DIGE 和 IAC-MS 结合用于 HCP ELISA 试剂表征，提供了可靠的 HCP 监测策略，包括相关 HCP 的定量和鉴定。潜在高风险蛋白质的 MS 分析和鉴定由 SWATH® 质谱 HCP 分析专家 Alphalyse 执行。

目标蛋白质鉴定

对于特定的、难以去除的 HCP 的分析，我们建议对凝胶斑点进行深入的 LC-MS/MS。一维/二维蛋白质分离后，将使用考马斯或荧光颜色染色以提高灵敏度来可视化蛋白质条带/斑点。为了鉴定目标蛋白质，从感兴趣的点中提取蛋白质，然后进行胰蛋白酶消化和随后的 MS 分析。这项服务将与我们经验丰富的质谱合作伙伴 FyoniBio 合作提供。

填写我们的查询表以获取更多信息或详细报价Part of the Communications in Computer and Information Science book series (CCIS, volume 836)AbstractThe identification of bioMarkers is very important to know the presence or severity of a particular disease state in the patient body. According to the latest studies on miRNAs and their behaviors, it is known to us that miRNAs involve in the regulation mechanism of several biological processes. Sometimes the abnormal change in miRNA expressions in different conditions may lead to malignant growth in tissues. In this article, our proposed approach not only helps to detect differentially coexpressed modules but also helps to identify biomarker candidates from those modules. The proposed algorithm uses the WCGNA software package to explore coexpression profiles of the miRNAs. The algorithm has been applied to existing miRNA datasets to point out the miRNA markers. Then, biological validation analysis has been performed for the obtained miRNA markers.KeywordsMicroRNA marker Microarray analysis Differentially coexpressed network Topological overlap Intramodular connectivity MiRNA-target interaction Carcinomas This is a preview of subscription content, log in to check access.References1.Abeel, T., Helleputte, T., Van de Peer, Y., Dupont, P., Saeys, Y.: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3), 392–398 (2009)CrossRefGoogle Scholar2.Ambros, V.: The functions of animal micrornas. Nature 431(7006), 350–355 (2004)CrossRefGoogle Scholar3.Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2), 281–297 (2004)CrossRefGoogle Scholar4.Barter, R.L., Schramm, S.J., Mann, G.J., Yang, Y.H.: Network-based biomarkers enhance classical approaches to prognostic gene expression signatures. BMC Syst. Biol. 8(4), S5 (2014)CrossRefGoogle Scholar5.Bolstad, B.M., Irizarry, R.A., Astrand, M., Speed, T.P.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003)CrossRefGoogle Scholar6.Choi, J.K., Yu, U., Yoo, O.J., Kim, S.: Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21(24), 4348–4355 (2005)CrossRefGoogle Scholar7.Della Vittoria Scarpati, G.: Analysis of differential miRNA expression in primary tumor and stroma of colorectal cancer patients. BioMed Res. Int. 2014, 840921 (2014)CrossRefGoogle Scholar8.Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)CrossRefGoogle Scholar9.Fukushima, A.: DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 518(1), 209–214 (2013)CrossRefGoogle Scholar10.Hsu, C.L., Juan, H.F., Huang, H.C.: Functional analysis and characterization of differential coexpression networks. Scientific Rep. 5, 13295 (2015)CrossRefGoogle Scholar11.Langfelder, P., Horvath, S.: WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9(1), 559 (2008)CrossRefGoogle Scholar12.Langfelder, P., Zhang, B., Horvath, S.: Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24(5), 719–720 (2008)CrossRefGoogle Scholar13.Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A.: Microrna expression profiles classify human cancers. Nature 435(7043), 834–838 (2005)CrossRefGoogle Scholar14.Mukhopadhyay, A., Maulik, U.: An SVM-wrapped multiobjective evolutionary feature selection approach for identifying cancer-microrna markers. IEEE Trans. NanoBioscience 12(4), 275–281 (2013)CrossRefGoogle Scholar15.Ray, S., Chakraborty, S., Mukhopadhyay, A.: DCoSpect: a novel differentially coexpressed gene module detection algorithm using spectral clustering. In: Das, S., Pal, T., Kar, S., Satapathy, S.C., Mandal, J.K. (eds.) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. AISC, vol. 404, pp. 69–77. Springer, New Delhi (2016).  https://doi.org/10.1007/978-81-322-2695-6_7CrossRefGoogle Scholar16.Raza, K., Jaiswal, R.: Reconstruction and analysis of cancer-specific gene regulatory networks from gene expression profiles. Int. J. Bioinform. Biosci. (IJBB) 3(2), 25–34 (2013)Google Scholar17.Sauter, E.R., Patel, N.: Body fluid micro(mi)RNAs as biomarkers for human cancer. J. Neuclic Acids Investig. 2(1), 1 (2011)CrossRefGoogle Scholar18.Taguchi, Y., Murakami, Y.: Principal component analysis based feature extraction approach to identify circulating microrna biomarkers. PloS One 8(6), e66714 (2013)CrossRefGoogle Scholar19.Tesson, B.M., Breitling, R., Jansen, R.C.: DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinform. 11(1), 497 (2010)CrossRefGoogle Scholar20.Ye, Y., Godzik, A.: Comparative analysis of protein domain organization. Genome Res. 14(3), 343–353 (2004)CrossRefGoogle Scholar21.Yip, A.M., Horvath, S.: Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinform. 8(1), 22 (2007)CrossRefGoogle Scholar22.Zhang, B., Horvath, S.: A general framework for weighted gene co-expression network analysis. Stat. Appl. Genetics Mol. Biol. 4(1), 1–43 (2005)MathSciNetzbMATHGoogle ScholarCopyright information© Springer Nature Singapore Pte Ltd. 2018Authors and AffiliationsParamita Biswas1Email authorAnirban Mukhopadhyay11.Department of Computer Science and EngineeringUniversity of KalyaniKalyaniIndia About this paper CrossMark Biswas P., Mukhopadhyay A. (2018) Identifying MicroRNA Markers From Expression Data: A Network Analysis Based Approach. In: Mandal J., Sinha D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_25