China
Africa
Explore chapters and articles related to this topic
Proteins and proteomics
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2018
The technique has been used to show overexpression of oncogenes and downregulation of tumor-suppressor genes in cancerous cells when compared to “normal” tissue, as well as the gene expression in the rejection of transplanted organs. Analysis of gene expression can be done by several different methods, including RT-PCR, RNase protection assays, microarrays, serial analysis of gene expression, as well as northern blotting. Microarrays are quite commonly used and are usually consistent with data obtained from northern blots; however, at times northern blotting is able to detect small changes in gene expression that microarrays cannot. The advantage that microarrays have over northern blots is that thousands of genes can be visualized at a time, while northern blotting is usually looking at one or a small number of genes.
Biological Data Mining:
Published in Wahiba Ben Abdessalem Karaa, Nilanjan Dey, Mining Multimedia Documents, 2017
Amira S. Ashour, Nilanjan Dey, Dac-Nhuong Le
Gene expression data can be employed to predict clinical results. Haferlach et al. [51] proposed a gene expression summarizing classifier to categorize patients into 18 diverse subclasses of either lymphoid or myeloid leukemia. The all-pairwise classification system was proposed using the trimmed mean of the difference between mismatch and perfect match intensities with quantile normalization. The difference of quantile normalized values (DQN) technique was clarified in [52,53]. Salazar et al. [54] constructed a gene expression classifier and extracted the gene features using leave-one-out cross-validation technique to define which gene probes were powerfully correlated with the metastasis-free survival (DMFS) with a t-test as the conclusive factor.
Big Data and Transcriptomics
Published in Shampa Sen, Leonid Datta, Sayak Mitra, Machine Learning and IoT, 2018
Sudharsana Sundarrajan, Sajitha Lulu, Mohanapriya Arumugam
Differentially expressed genes are those whose expression levels differ significantly under different conditions. The fold change determines the difference between the groups and their biological significance. Various univariate statistical analyses such as t-test, two-sample t-tests, F-statistic, and modified t-test (SAM) are available to determine the relative expression of genes from normalized microarray data. For multiple classes analysis of variance (ANOVA) is used.4 Various software packages, such as Bioconductor packages, implemented in R are available to identify the differentially expressed genes. A list of programs available for differential gene expression analysis is summarized in Table 5.1.
Deep multi-modal fusion network with gated unit for breast cancer survival prediction
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Our study uses the METABRIC dataset, from the International Breast Cancer Society’s Molecular Classification Database, which aims to further classify breast cancer tumors based on molecular features that help determine the best course of treatment (METABRIC Group, 2012). This dataset has metabolic tracking data for 1980 authentic breast cancer patients, details of which are presented in Table 1. The information for each breast cancer patient is derived from data in three modalities: clinical data, CNA data, and gene expression data. Clinical characteristics include patient age, tumor size, tumor stage and grade, receptor status, etc., as detailed in Table 2. The copy number of the genome changes during cancer development, and each CNA data represents the copy number of a specific gene in a specific sample. Gene expression is the process of synthesizing genetic information from genes into functional gene products. Different types of cancer cause different gene expression patterns in humans, and each gene expression data indicates the expression level of a specific gene in a specific sample.
Re-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning
Published in Cybernetics and Systems, 2023
Çiğdem Erol, Tchare Adnaane Bawa, Yalçın Özkan
DNA microarray analysis is one of the technologies that help to measure the expression levels of multiple genes simultaneously via chips. It is possible to define the gene expression profile of the tumor with DNA microarray technology (D'Angelo, Di Rienzo, and Ojetti 2014). Gene expression analysis is a study used to classify cancers, predict clinical outcomes, and discover disease-associated biomarkers (Chen et al. 2014). In general, microarray studies require experimental intensive labor, time, and cost. The data obtained as a result of all these efforts are shared in public databases such as the National Center for Biotechnology Information, Gene Expression Omnibus (NCBI GEO), as well as being used in the publication in which it was produced. In order to obtain valuable information from data in today’s data age, it is necessary to conduct research in these data stacks with different perspectives, new algorithms, and new approaches.
The interplay between DNA methylation and cardiac autonomic system functioning: a systematic review
Published in International Journal of Environmental Health Research, 2023
Nayara Cristina Dos Santos Oliveira, Fernanda Serpeloni, Simone Gonçalves de Assis
Epigenetics refers to a dynamic and biological phenomenon that may be heritable and sensitive to environmental factors, which appears to influence the cardiovascular autonomic regulation (Figure 1). Epigenetic mechanisms involve modifications of histone proteins and non-coding RNAs, including microRNAs and DNAm, which regulate gene expression profiles without altering the DNA sequence but instead through chromatin reorganization (Allis and Jenuwein 2016). This review focuses on the DNAm because it has been linked to several cardiovascular-related biomarkers (Baccarelli et al. 2010; Rosa-Garrido et al. 2018). DNAm is the addition of a methyl group to the 5′ position of cytosine in CpG dinucleotides (CpGs), at promoters and other regions (Jones 2012). Patients with atherosclerotic cardiovascular disease, for instance, exhibited lower global DNAm status in peripheral blood leukocytes (Castro et al. 2003).