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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Identification of potential biomarkers and pathways related to major depressive disorder by integrated bioinformatic analysis and experimental validation by Ying Zeng, Lu-Qi Peng, Mei Zhang, Rong Zhong, Ke-Chao Nie, Wei Huang

“…Objective: To identify promising biomarkers for the pathogenesis of major depressive disorder (MDD). Methods: Microarray chips of MDD patients, including the GSE98793, GSE52790, and GSE39653 datasets, were obtained from the Gene Expression Omnibus database. …”
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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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Assessment of the carcinogenic potential of particulate matter generated from 3D printing devices in Balb/c 3T3-1-1 cells by CheolHong Lim, DongSeok Seo

“…Various assays, such as the comet assay, cell transformation assays, microarray analysis, and glucose consumption measurement, were employed. …”
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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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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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The Gene Expression Profile of Milk Somatic Cells of Small Ruminant Lentivirus-Seropositive and -Seronegative Dairy Goats (<i>Capra hircus</i>) During Their First Lactation by Joanna Pławińska-Czarnak, Alicja Majewska, Joanna Magdalena Zarzyńska, Jarosław Kaba, Emilia Bagnicka

“…Statistical analysis was performed in GeneSpring 12 software. Results: Microarrays showed reduced expression of <i>DUSP26</i>, <i>PRLR</i>, <i>SCARA3</i>, <i>APBB2</i>, and <i>OR4F4</i> genes in SRLV-SP goats. …”
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Decreased mitochondrial‐related gene expression in adipose tissue after acute sprint exercise in humans: A pilot study by Mona Esbjörnsson, Håkan C. Rundqvist, Barbara Norman, Ted Österlund, Eric Rullman, Jens Bülow, Eva Jansson

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A decade of change – lessons learned from prenatal diagnostics in Central Denmark region in 2008–2018 by Dorte Launholt Lildballe, Naja Becher, Else Marie Vestergaard, Rikke Christensen, Stina Lou, Puk Sandager, Lars Henning Pedersen, Kasper Gadsbøll, Olav Bjørn Petersen, Ida Vogel

“…This retrospective study summarizes 11 years of using chromosomal microarray in invasive prenatal testing and presents the effect on diagnostic yield and turnaround time. …”
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Genetic etiology and pregnancy outcomes of fetal hyperechoic kidneys: a retrospective analysis by Meiying Cai, Na Lin, Ziheng Xiao, Ziheng Xiao, Hailong Huang, Lin Zheng, Liangpu Xu

“…Chromosome karyotyping and chromosomal microarray analysis (CMA) were performed on fetuses displaying this phenotype on prenatal ultrasound. …”
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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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Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma by Liam Burrows, Declan Sculthorpe, Hongrun Zhang, Obaid Rehman, Abhik Mukherjee, Ke Chen

“…This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. …”
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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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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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Cost-effective solutions for high-throughput enzymatic DNA methylation sequencing. by Amy Longtin, Marina M Watowich, Baptiste Sadoughi, Rachel M Petersen, Sarah F Brosnan, Kenneth Buetow, Qiuyin Cai, Cayo Biobank Research Unit, Michael D Gurven, James P Higham, Heather M Highland, Yi-Ting Huang, Hillard Kaplan, Thomas S Kraft, Yvonne A L Lim, Jirong Long, Amanda D Melin, Michael J Montague, Jamie Roberson, Kee Seong Ng, Michael L Platt, India A Schneider-Crease, Jonathan Stieglitz, Benjamin C Trumble, Vivek V Venkataraman, Ian J Wallace, Jie Wu, Noah Snyder-Mackler, Angela Jones, Alexander G Bick, Amanda J Lea

“…While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). …”
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A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches by Saba Bashir, Irfan Ullah Khattak, Aihab Khan, Farhan Hassan Khan, Abdullah Gani, Muhammad Shiraz

“…For this purpose, the proposed research focused on analyzing and identifying effective feature selection algorithms. A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. …”
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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification by Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J

“…Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. …”
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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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Enucleation-Induced Rat Adrenal Gland Regeneration: Expression Profile of Selected Genes Involved in Control of Adrenocortical Cell Proliferation by Marianna Tyczewska, Marcin Rucinski, Agnieszka Ziolkowska, Marta Szyszka, Marcin Trejter, Anna Hochol-Molenda, Krzysztof W. Nowak, Ludwik K. Malendowicz

“…Factors encoded by these genes obscure possible priming effects of various cytokines on initiation of regeneration. …”
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