Transfer learning for accelerated failure time model with microarray data by Yan-Bo Pei, Zheng-Yang Yu, Jun-Shan Shen

“…Abstract Background In microarray prognostic studies, researchers aim to identify genes associated with disease progression. …”
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Monitoring of Representational Difference Analysis Subtraction Procedures by Global Microarrays by T. Andersson, P. Unneberg, P. Nilsson, J. Odeberg, J. Quackenbush, J. Lundeberg

“…The compromise between focusing on only the important genes in certain cellular processes and achieving a complete picture is critical for the selection of strategy. We demonstrate how global microarray technology can be used for the exploration of the differentially expressed genes extracted through representational difference analysis (RDA). …”
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The Comparison of Three Measures in Feature Selection by SONG Zhi-chao, KANG Jian, SUN Guang-lu, HE Yong-jun

“…Three representative linear or nonlinear measures，linear correlation coefficient，symmetrical uncertainty，and mutual information are selected. By combining them with the fast correlation-based filter ( FCBF) feature selection method，we make the comparison of selected feature subset from 8 gene microarray and image datasets. …”
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Consensus PCR and Microarray for Diagnosis of the Genus Staphylococcus, Species, and Methicillin Resistance by S. Hamels, J.-L. Gala, S. Dufour, P. Vannuffel, N. Zammatteo, J. Remacle

“…Products were then identified on a glass array. The microarray contained five selective DNA capture probes for the simultaneous and differential identification of the five most clinically relevant staphylococcal species (S. aureus, S. epidermidis, S. haemolyticus, S. hominis, and S. saprophyticus), while a consensus capture probe could detect all femAsequences, allowing the identification of the genus Staphylococcus. …”
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Optimization based tumor classification from microarray gene expression data. by Onur Dagliyan, Fadime Uney-Yuksektepe, I Halil Kavakli, Metin Turkay

“…<h4>Background</h4>An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. …”
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Application of Chromosome Microarray in Diagnosis of Amniotic Fluid in Older Pregnant Women by Guangting Lu, Weiwu Liu, Chao Ou

“…Background: To improve the detection rate of chromosome abnormalities in fetuses and to reduce the birth defects rate in elderly pregnant women using chromosome karyotype analysis combined with the chromosome microarray analysis (CMA) technique. Methods: Overall, 210 elderly pregnant women with singleton pregnancies aged between 16 and 30 weeks (mean gestational age, 19.19 weeks) and 35 and 47 years (mean age, 38.08 years) were selected from January 1, 2020 to June 1, 2021 in the Eugenics Genetics Department of Yulin Maternal and Child Health Hospital. …”
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High reproducibility using sodium hydroxide-stripped long oligonucleotide DNA microarrays by Zhiyuan Hu, Melissa Troester, Charles M. Perou

“…In addition, when there is limited availability of mRNA from tissue sources, RNA amplification can and is being used to produce sufficient quantities of cRNA for microarray hybridization. Taking advantage of the selective degradation of RNA under alkaline conditions, we have developed a method to “strip” glass-based oligonucleotide microarrays that use fluorescent RNA in the hybridization, while leaving the DNA oligonucleotide probes intact and usable for a second experiment. …”
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Robust Microarray Meta-Analysis Identifies Differentially Expressed Genes for Clinical Prediction by John H. Phan, Andrew N. Young, May D. Wang

“…Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. …”
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Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data by Nicola J. Armstrong, Mark A. van de Wiel

“…We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. …”
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A tailored lectin microarray for rapid glycan profiling of therapeutic monoclonal antibodies by Shen Luo, Baolin Zhang

“…In this study, we introduce a custom-designed lectin microarray featuring nine distinct lectins: rPhoSL, rOTH3, RCA120, rMan2, MAL_I, rPSL1a, PHAE, rMOA, and PHAL. …”
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CRISPR screens and lectin microarrays identify high mannose N-glycan regulators by C. Kimberly Tsui, Nicholas Twells, Jenni Durieux, Emma Doan, Jacqueline Woo, Noosha Khosrojerdi, Janiya Brooks, Ayodeji Kulepa, Brant Webster, Lara K. Mahal, Andrew Dillin

“…We used CRISPR screens to uncover the expanded network of genes controlling high mannose levels, followed by lectin microarrays to fully measure the complex effect of select regulators on glycosylation globally. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

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Using effective subnetworks to predict selected properties of gene networks. by Gemunu H Gunaratne, Preethi H Gunaratne, Lars Seemann, Andrei Török

“…Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. …”
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THE VALIDATION OF THE RESULTS OF MICROARRAY STUDIES OF ASSOCIATION BETWEEN GENE POLYMORPHISMS AND THE FREQUENCY OF RADIATION EXPOSURE MARKERS by M. V. Khalyuzova, N. V. Litvyakov, A. E. Sazonov, Ye. N. Albakh, D. S. Isubakova, A. B. Karpov, R. M. Takhauov

“…The results from the selective validation research into the association between genetic polymorphisms and the frequency of cytogenetic abnormalities on a large independent sample are analyzed. …”
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Microarray analysis of virulence gene profiles in Salmonella serovars from food/food animal environment by Wen Zou, Sufian F Al-Khaldi, William S Branham, Tao Han, James C Fuscoe, Jing Han, Steven L Foley, Joshua Xu, Hong Fang, Carl E Cerniglia, Rajesh Nayak

“…Methodology:  The spotted array consisted of 69 selected Salmonella-specific virulence gene probes (65bp each).  …”
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Genetic feature selection algorithm as an efficient glioma grade classifier by Ting-Han Lin, Hung-Yi Lin

“…Genetic testing is a rapidly evolving modality for cancer management. The advent of DNA microarrays enabled the utility of computational analyses in such management on a molecular basis. …”
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Machine learning identification of molecular targets for medulloblastoma subgroups using microarray gene fingerprint analysis by Alicia Reveles-Espinoza, Ulises Villela, Edgar Hernandez-Martinez, Isaac Chairez, Sergio Juárez-Méndez, J. Casanova-Moreno, Ma. del Pilar Eguía-Aguilar, Luis Figueroa-Yáñez, Adriana Vallejo-Cardona, Iván Salgado

“…An artificial neural network (ANN) model trained on microarray gene expression data determined minimal gene combinations for each subgroup. …”
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Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases. by Rocio Chavez-Alvarez, Arturo Chavoya, Andres Mendez-Vazquez

“…DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. …”
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Molecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays. by Thomas Sanford, Paul H Chung, Ariel Reinish, Vladimir Valera, Ramaprasad Srinivasan, W Marston Linehan, Gennady Bratslavsky

“…<h4>Experimental design</h4>A search of publicly available databases was performed to identify microarray datasets with multiple histologic sub-types of renal cortical neoplasms. …”
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CFS-MOES Ensemble Model on Metaheuristic Search-Based Feature Selection by Santosini Bhutia, Bichitrananda Patra, Mitrabinda Ray

“…The application of several classification and feature selection methods on microarray gene expression datasets helps learn models that are able to predict a given disease. …”
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