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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Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing by Dean Thompson, Jemma Castle, Martin Sill, Stefan M. Pfister, Simon Bailey, Debbie Hicks, Steven C. Clifford, Edward C. Schwalbe

“…We further assessed and optimised reference-free aneuploidy detection using low-pass WGBS and assessed concordance with microarray-derived calls. …”
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Whole genome uniparental isodisomy detected using single nucleotide polymorphism (SNP) microarray in molar pregnancy: a case report by Onyinye O. Okonkwo, Veronica Ortega, Sheila Kane, Galina Aldrete, Paulina Ramirez, Philip T. Valente, Gopalrao V.N. Velagaleti

“…High-resolution SNP microarray studies detected whole genome uniparental isodisomy. …”
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CFS-MOES Ensemble Model on Metaheuristic Search-Based Feature Selection by Santosini Bhutia, Bichitrananda Patra, Mitrabinda Ray

“…Due to the availability of highly specialized cancer datasets, molecular classification of cancer by gene expression, machine learning, and deep learning, a part of artificial intelligence (AI) techniques is used in detecting the disease. 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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Optimized T7 Amplification System for Microarray Analysis by C. Pabón, Z. Modrusan, M.V. Ruvolo, I.M. Coleman, S. Daniel, H. Yue, L.J. Arnold, M.A. Reynolds

“…Glass cDNA microarray technologies offer a highly parallel approach for profiling expressed gene sequences in disease-relevant tissues. …”
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Analysis of a Series of 26 Cases With Prenatal Skeletal Dysplasia via Multiplatform Genetic Detection by Li‐min Cui, Hua‐ying Hu, Xiao‐mei Zhai, Ming‐fei Qi, Yan‐ming Liu, Cong‐ying Han, Jing Zhang, Ming Shen, Yu‐lan Xiang, Wen‐qi Chen, Kai Yang, Dong‐liang Zhang, Huan‐xia Xing

“…Materials and Methods In this study, we recruited 26 cases of SD and analyzed them with a designed sequential genetic detection. Chromosome karyotyping, microarray analysis (CMA), and whole exome sequencing (WES) techniques are performed as needed. …”
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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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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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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

“…Microarrays printed on glass slides are often constructed by covalently linking oligonucleotide probes to a derivatized surface. …”
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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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P685: A single-center reevaluation and reanalysis of copy number variants of uncertain significance detected by chromosome microarray from consecutive pediatric patients by Wenjiao Li, Hongyan Chai, Autumn Diadamo, Yuan Dai, Peining Li, Michele Spencer-Manzon, Hui Zhang, Jiadi Wen

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Clinically relevant genetic variants of drug-metabolizing enzyme and transporter genes detected in Thai children and adolescents with autism spectrum disorder by Medhasi S, Pasomsub E, Vanwong N, Ngamsamut N, Puangpetch A, Chamnanphon M, Hongkaew Y, Limsila P, Pinthong D, Sukasem C

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A Sensitive and Fast microRNA Detection Platform Based on CRlSPR-Cas12a Coupled with Hybridization Chain Reaction and Photonic Crystal Microarray by Bingjie Xue, Bokang Qiao, Lixin Jia, Jimei Chi, Meng Su, Yanlin Song, Jie Du

“…The sensitive and fast detection of miRNAs is critical for diagnosis and prognosis. …”
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Application of chromosome microarray analysis and karyotyping in fetal cardiac abnormalities by Yun Guo, Xiaoqin Xin, Linju Zhou, Jungao Huang

“…Chromosome microarray analysis detected abnormalities in 6.58% (5/76) of the isolated cardiac abnormalities group and 27.27% (6/22) in the group with combined abnormalities, showing a significant statistical difference (P < 0.05). …”
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Computer Vision for Substrate Detection in High‐Throughput Biomaterial Screens Using Bright‐Field Microscopy by Robert Owen, Aishah Nasir, Mahetab H. Amer, Chenxue Nie, Xuan Xue, Laurence Burroughs, Chris Denning, Ricky D. Wildman, Faraz A. Khan, Morgan R. Alexander, Felicity R. A. J. Rose

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A universal DNA microarray for rapid fish species authentication by Patrizia Bade, Sebastian Stix, Kristina Kappel, Jan Fritsche, Ilka Haase, Andrew Torda, Nils Wax, Markus Fischer, Dirk Brandis, Ute Schröder

“…DNA microarrays are now used in fields such as gene expression analysis, pathogen/virus detection and identification of biomarkers. …”
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