An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.

Microarrays Pub Date : 2015-06-01 DOI:10.3390/microarrays4020287

Enery Lorenzo, Katia Camacho-Caceres, Alexander J Ropelewski, Juan Rosas, Michael Ortiz-Mojer, Lynn Perez-Marty, Juan Irizarry, Valerie Gonzalez, Jesús A Rodríguez, Mauricio Cabrera-Rios, Clara Isaza

引用次数: 33

Abstract

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

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一个优化驱动的分析管道揭示生物标志物和信号通路:宫颈癌。

确定一系列潜在的重要基因是如何相互关联的，对于理解癌症等疾病的起源和进化具有重要意义。高通量生物实验在提供这方面的信息方面发挥了关键作用。然而，一个特殊的挑战是，试图调和来自不同微阵列实验的信息，以建立一个潜在的遗传信号通路。这项工作提出了一个基于优化的两步分析管道，以接近旨在建立遗传信号通路代理的元分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microarrays

自引率

0.00%

发文量

审稿时长

11 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.