Literature DB >> 31115885

A Review of Microarray Datasets: Where to Find Them and Specific Characteristics.

Amparo Alonso-Betanzos1, Verónica Bolón-Canedo2, Laura Morán-Fernández1, Noelia Sánchez-Maroño1.   

Abstract

The advent of DNA microarray datasets has stimulated a new line of research both in bioinformatics and in machine learning. This type of data is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for disease diagnosis or for distinguishing specific types of tumor. Microarray data classification is a difficult challenge for machine learning researchers due to its high number of features and the small sample sizes. This chapter is devoted to reviewing the microarray databases most frequently used in the literature. We also make the interested reader aware of the problematic of data characteristics in this domain, such as the imbalance of the data, their complexity, and the so-called dataset shift.

Entities:  

Keywords:  Dataset shift; High dimensionality; Microarray data; Unbalanced data

Year:  2019        PMID: 31115885     DOI: 10.1007/978-1-4939-9442-7_4

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  4 in total

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Authors:  Roberto Magherini; Elisa Mussi; Yary Volpe; Rocco Furferi; Francesco Buonamici; Michaela Servi
Journal:  Sensors (Basel)       Date:  2022-07-01       Impact factor: 3.847

2.  A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia.

Authors:  Chengfeng Xu; Ruochi Zhang; Meiyu Duan; Yongming Zhou; Jizhang Bao; Hao Lu; Jie Wang; Minghui Hu; Zhaoyang Hu; Fengfeng Zhou; Wenwei Zhu
Journal:  Mol Ther Nucleic Acids       Date:  2022-04-06       Impact factor: 10.183

3.  Construction of Adipogenic ceRNA Network Based on lncRNA Expression Profile of Adipogenic Differentiation of Human MSC Cells.

Authors:  Chengcheng Liang; Sayed Haidar Abbas Raza; Muhammad Abuzar Raza Naqvi; Yanrong Feng; Rajwali Khan; Zuhair M Mohammedsaleh; Abdullah F Shater; Bassam M Al-Ahmadi; Fayez M Saleh; Muhammad Ahsan Bilal; Linsen Zan
Journal:  Biochem Genet       Date:  2021-07-24       Impact factor: 1.890

4.  Artificial Intelligence Models Reveal Sex-Specific Gene Expression in Aortic Valve Calcification.

Authors:  Philip Sarajlic; Oscar Plunde; Anders Franco-Cereceda; Magnus Bäck
Journal:  JACC Basic Transl Sci       Date:  2021-04-14
  4 in total

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