LIMA1-alpha staining predicts curative intent surgery response in HPV negative head and neck cancer by Xi Qiao, Johannes Routila, Mari Tienhaara, Heikki Irjala, Priyadharshini Parimelazhagan Santhi, Teemu Huusko, Linda Nissi, Ilkka Paatero, Noora Lehtinen, Juha Rantala, Toni Viljanen, Ilmo Leivo, Petri Koivunen, Anna Jouppila-Mättö, Rami Taulu, Leif Bäck, Tommy Wilkman, Eeva Haapio, Ilpo Kinnunen, Kari Kurppa, Jukka Westermarck, Sami Ventelä

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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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In-depth immunochemical characterization of the serum antibody response using a dual-titration microspot assay by Ágnes Kovács, Ágnes Kovács, Zoltán Hérincs, Krisztián Papp, Jakub Zbigniew Kaczmarek, Daniel Nyberg Larsen, Daniel Nyberg Larsen, Pernille Stage, László Bereczki, Eszter Ujhelyi, Tamás Pfeil, Tamás Pfeil, József Prechl

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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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Identification of three small nucleolar RNAs (snoRNAs) as potential prognostic markers in diffuse large B‐cell lymphoma by Mei‐wei Li, Feng‐xiang Huang, Zu‐cheng Xie, Hao‐yuan Hong, Qing‐yuan Xu, Zhi‐gang Peng

“…Results Twelve prognosis‐correlated snoRNAs were selected from the DLBCL patient cohort of microarray profiles, and a three‐snoRNA signature consisting of SNORD1A, SNORA60, and SNORA66 was constructed. …”
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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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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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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Gene-expression analysis can be an alternative, where the different machine learning and artificial intelligence techniques can be developed to learn microarray RNA details for breast cancer detection. …”
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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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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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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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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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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

“…Microarray datasets are comprised of high-quality features and very few samples of data. …”
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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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Genetic Comparison and Selection of Reproductive and Growth-Related Traits in Qinchuan Cattle and Two Belgian Cattle Breeds by Xiaopeng Li, Peng Niu, Xueyan Wang, Fei Huang, Jieru Wang, Huimin Qu, Chunmei Han, Qinghua Gao

“…These findings provide valuable molecular markers for enhancing reproductive efficiency, growth, and meat production through genetic selection and selective breeding strategies.…”
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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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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

“…Allele substitution effects were calculated based on both pooled SNP microarray genotypes, and individual genotypes of QTLRs markers. …”
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