QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data by Nayoung Kim, Herin Park, Ningning He, Hyeon Young Lee, Sukjoon Yoon

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Tissue‐based quantitative proteomics to screen and identify the potential biomarkers for early recurrence/metastasis of esophageal squamous cell carcinoma by Xu‐Wei Cai, Wei‐Wei Yu, Wen Yu, Qin Zhang, Wen Feng, Mi‐Na Liu, Meng‐Hong Sun, Jia‐Qing Xiang, Ya‐Wei Zhang, Xiao‐Long Fu

“…Thirteen proteins were selected by cutoff value of 0.67 fold for underexpression and 1.5‐fold for overexpression. …”
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Transcriptional responses in different mouse models of septic liver injury differ from those in patients with septic liver injury by Qin Yan, Qin Yan, Qin Yan, Wei Fan, Wei Fan, Wei Fan, Xinsen He, Xinsen He, Xinsen He, Shi Zheng, Shi Zheng, Shi Zheng, Xiaolin Zhong, Xiaolin Zhong, Xiaolin Zhong

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Differential gene expression analysis in patients with primary hyperhidrosis by Ting Pu, Muhammad Ameen Jamal, Muhammad Nauman Tahir, Salman Ullah, Maher Un Nisa Awan, Asif Shahzad, Faisal Mahmood

“…Based on the highest expression of ITPR2 in hyperhidrosis, it was selected for PCR amplification as well as sequencing. …”
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Mapping quantitative trait loci regions associated with Marek’s disease on chicken autosomes by means of selective DNA pooling by Ehud Lipkin, Jacqueline Smith, Morris Soller, David W. Burt, Janet E. Fulton

“…Distribution of P and LD values were used to assess the QTLR causative elements. Allele substitution effects were calculated based on both pooled SNP microarray genotypes, and individual genotypes of QTLRs markers. …”
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Protein-specific immune response elicited by the Shigella sonnei 1790GAHB GMMA-based candidate vaccine in adults with varying exposure to Shigella by Arlo Z. Randall, Valentino Conti, Usman Nakakana, Xiaowu Liang, Andy A. Teng, Antonio Lorenzo Di Pasquale, Melissa Kapulu, Robert Frenck, Odile Launay, Pietro Ferruzzi, Antonella Silvia Sciré, Elisa Marchetti, Christina Obiero, Jozelyn V. Pablo, Joshua Edgar, Philip Bejon, Adam D. Shandling, Joseph J. Campo, Angela Yee, Laura B. Martin, Audino Podda, Francesca Micoli

“…An ideal vaccine would provide protection against the most prevalent species, Shigella flexneri and Shigella sonnei; therefore, it could be relevant to identify common antigens. We developed a microarray containing 3,150 full-length or fragmented proteins selected across Shigella species. …”
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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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Effects of oxidized phospholipids on gene expression in RAW 264.7 macrophages: a microarray study. by Daniel Koller, Hubert Hackl, Juliane Gertrude Bogner-Strauß, Albin Hermetter

“…In this study we present the effects of 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine (PGPC) and 1-palmitoyl-2-(5-oxovaleroyl)-sn-glycero-3-phosphocholine (POVPC) on gene expression in RAW 264.7 macrophages using cDNA microarrays. …”
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Method for Printing Functional Protein Microarrays by James B. Delehanty, Frances S. Ligler

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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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Expression of long non-coding RNA in patients with non-IgA mesangial proliferative glomerulonephritis by CONG Shan, SUI Wei-guo, ZOU Gui-mian, XUE Wen, LI Huan, YAN Qiang, CHEN Jie-jing, LUO Ya-dan, CHEN Huai-zhou

“…Objective To study differential expression profile of mRNA and long non-coding RNA(IncRNA) through microarray analysis between non-IgA mesangial proliferative glomerulonephritis(MsPGN) patients and the controls,and then explore the potential role of IncRNA in the pathogenesis of non-IgA MsPGN.Methods Through simple random sampling,4 patients with non-IgA MsPGN and 2 controls were selected as disease group and control group,respectively.Renal cortical tissues from two groups were collected.Total RNA was extracted,quantified and prepared to ds-cDNA through reverse transcription ds-cDNA was labeled with NimbleGen one-color DNA labeling kit and used for array hybridization.All experimental data were processed through GO analysis,Pathway analysis and the gene loci correlation analysis of mRNA and IncRNA.Some IncRNAs that were closely related to non-IgA MsPGN were screened out.Finally,part of the array results was detected by PCR to verify the reliability of array test Results By fold change filtering,4317 differentially expressed mRNAs and 3502 differentially expressed IncRNAs were screened out.Five IncRNAs were found to play potential roles in the pathogenesis of non-IgA MsPGN:AF1180924(close to coding gene FGG),AK092233(close to coding gene COL18A1),AK130579(close to coding gene CREBBP),AK023598(close to coding gene LEPR),and AK055915(close to coding gene CDC42EP3).These results provided an important basis for revealing the pathogenesis of non-IgA MsPGN.Conclusions Some IncRNAs can potentially regulate related genes and plays an important role in the pathogenesis and development of non-IgA MsPGN.…”
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A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis by Tarneem Elemam, Mohamed Elshrkawey

“…To examine the proposed algorithm, many tests have been carried out on four cancerous microarray datasets, employing in the process 10-fold cross-validation and hyperparameter tuning. …”
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Microarray analysis of the effects of Acthar Gel versus methylprednisolone in a model of focal segmental glomerulosclerosis in female rats by Kyle Hayes, Dale Wright

“…On Day 56, animals were sacrificed, and RNA samples of kidney cortex tissue were analyzed using microarrays. Compared with control, Acthar significantly decreased the expression of more genes related to inflammation, immune function, and fibrosis than MP. …”
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A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification by Chellamuthu Gunavathi, Kandasamy Premalatha

“…Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. …”
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RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

“…Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. …”
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Possible cross‑talk between atrial fibrillation and obstructive sleep apnea revealed by transcriptomic analysis by Xinmiao Chen, Yi Li, Yao Liu

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Fabrication of DNA Microarrays Using Unmodified Oligonucleotide Probes by Douglas Ruben Call, Darrell P. Chandler, Fred Brockman

“…Our method provides a cost-effective alternative to conventional attachment strategies that is particularly suitable for genotyping PCR products with nucleic acid microarrays.…”
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Fast Spot Locating for Low-Density DNA Microarray by MinGin Kim, Jongwon Kim, Sun-Hee Kim, Jong-Dae Kim

“…Low-density DNA microarrays are crucial in molecular diagnostics due to their cost-effectiveness and high sensitivity. …”
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An efficient leukemia prediction method using machine learning and deep learning with selected features. by Mahwish Ilyas, Muhammad Ramzan, Mohamed Deriche, Khalid Mahmood, Anam Naz

“…The suggested work predicts and classifies leukemia subtypes in gene data CuMiDa (GSE9476) using feature selection and ML techniques. The Curated Microarray Database (CuMiDa) collected 64 samples representing five classes of leukemia genes out of 22283 genes. …”
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Drug and cell type-specific regulation of genes with different classes of estrogen receptor beta-selective agonists. by Sreenivasan Paruthiyil, Aleksandra Cvoro, Xiaoyue Zhao, Zhijin Wu, Yunxia Sui, Richard E Staub, Scott Baggett, Candice B Herber, Chandi Griffin, Mary Tagliaferri, Heather A Harris, Isaac Cohen, Leonard F Bjeldanes, Terence P Speed, Fred Schaufele, Dale C Leitman

“…U2OS cells stably transfected with ERalpha or ERbeta were treated with E(2) or the ERbeta-selective compounds for 6 h. Microarray data demonstrated that ERB-041, MF101 and liquiritigenin were the most ERbeta-selective agonists compared to estradiol, followed by nyasol and then diarylpropionitrile. …”
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