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.
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http://dx.doi.org/10.1007/978-1-4939-9442-7_4 | DOI Listing |
Metab Brain Dis
January 2025
Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Catania, Italy.
SERPINA3, a serine protease inhibitor, is strongly associated with neuroinflammation, a typical condition of AD. Its expression is linked to microglial and astrocytic markers, suggesting it plays a significant role in modulating neuroinflammatory responses. In this study, we examined the SERPINA3 expression levels, along with CHI3L1, in various brain regions of AD patients and non-demented healthy controls (NDHC).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
Background: Positron emission tomography (PET) imaging greatly impacted Alzheimer's disease (AD) research and diagnosis. which makes predicting PET brain imaging alterations using blood data is of high interest. Additionally, integrating PET and omics data can provide new insights into AD pathophysiology.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Circuito Escolar, Ciudad Universitaria, 04510 Mexico city, México.
Analyzing gene expression data helps the identification of significant biological relationships in genes. With a growing number of open biological datasets available, it is paramount to use reliable and innovative methods to perform in-depth analyses of biological data and ensure that informed decisions are made based on accurate information. Evolutionary algorithms have been successful in the analysis of biological datasets.
View Article and Find Full Text PDFCancer research has been significantly advanced by the integration of transcriptomic data through high-throughput sequencing technologies like RNA sequencing (RNA-seq). This paper reviews the transformative impact of transcriptomics on understanding cancer biology, focusing on the use of extensive datasets such as The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). While transcriptomic data provides crucial insights into gene expression patterns and disease mechanisms, the analysis is fraught with technical and biological biases.
View Article and Find Full Text PDFInt J Med Sci
January 2025
Department of Otolaryngology, Head and Neck Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan.
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