Weighted frequent gene co-expression network mining to identify genes involved in genome stability. by Jie Zhang, Kewei Lu, Yang Xiang, Muhtadi Islam, Shweta Kotian, Zeina Kais, Cindy Lee, Mansi Arora, Hui-Wen Liu, Jeffrey D Parvin, Kun Huang

“…In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. …”
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Have maternal or paternal ages any impact on the prenatal incidence of genomic copy number variants associated with fetal structural anomalies? by Marta Larroya, Marta Tortajada, Eduard Mensión, Montse Pauta, Laia Rodriguez-Revenga, Antoni Borrell

“…We conducted a non-paired case-control study (1:2 ratio) among pregnancies undergoing chromosomal microarray analysis (CMA) because of fetal ultrasound anomalies, from December 2012 to May 2020. …”
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Identification of the Exercise and Time Effects on Human Skeletal Muscle through Bioinformatics Methods by Mufang Feng, Jie Ji, Xiaoliu Li, Xinming Ye

“…To determine the effects of exercise and time on human skeletal muscle, we downloaded the microarray expression profile of GSE1832 and analyzed it to select differentially expressed genes (DEGs). …”
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Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach by Hao-Ju Lo, Chun-Hao Tsai, Tsan-Wen Huang

“…This study explores the mechanisms of glucocorticoid-induced osteoporosis (OP) and Rheumatoid arthritis (RA), focusing on apoptosis and its role in the progression from RA to OP. Using microarray data from the GEO database, differential gene expression analysis was conducted with the limma package, identifying significant genes in RA and OP. …”
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Cuproptosis genes in predicting the occurrence of allergic rhinitis and pharmacological treatment. by Ting Yi

“…<h4>Results</h4>Four AR signature genes (MRPS30, CLPX, MRPL13, and MRPL53) were selected by the MCC, EPC, BottleNeck, and Closeness algorithms. …”
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SMART: unique splitting-while-merging framework for gene clustering. by Rui Fa, David J Roberts, Asoke K Nandi

“…Moreover, two real microarray gene expression datasets are studied using this approach. …”
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Bioinformatic-based differential expression gene expressions of epithelial mesenchymal transformation in diabetic kidney disease and prediction of traditional Chinese medicine by Liu Wu, Zhou Yi, Yu Fang-ning, Zhang Ning

“…ObjectiveBased upon the bioinformatic analysis of gene chip data between patients with diabetic kidney disease （DKD） and normal controls， differentially expressed genes of epithelial mesenchymal transformation of DKD were screened for elucidating its pathogenesis and predicting the potential therapeutic Chinese medicine for DKD.MethodsGSE23338 microarray data were downloaded from gene expression omnibus， related differentially expressed genes screened by R language and differentially expressed genes analyzed by Gene Ontology， Kyoto Encyclopedia of Genes and Genomes. …”
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Diagnosis of Coronary Heart Diseases Using Gene Expression Profiling; Stable Coronary Artery Disease, Cardiac Ischemia with and without Myocardial Necrosis. by Nabila Kazmi, Tom R Gaunt

“…Interestingly it also showed efficacy in discriminating myocardial infarction patients from patients with clinical symptoms of cardiac ischemia but no myocardial necrosis or stable coronary artery disease, despite the influence of batch effects and different microarray gene chips and platforms.…”
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Cellular senescence-associated genes in rheumatoid arthritis: Identification and functional analysis. by You Ao, Qing Lan, Tianhua Yu, Zhichao Wang, Jing Zhang

“…In our study, we analyzed RA microarray data from the Gene Expression Omnibus (GEO) and focused on cellular senescence genes from the CellAge database. …”
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DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies. by Shaun Mahony, Philip E Auron, Panayiotis V Benos

“…Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP-chip data where frequently multiple putative targets of usually unknown TFs are predicted. …”
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MicroRNA biomarkers of type 2 diabetes: A protocol for corroborating evidence by computational genomics and meta-analyses. by Hongmei Zhu, Siu-Wai Leung

“…Biologically relevant microRNAs will then be selected through genomic database corroboration. Their association with T2D is further measured by area under the curve (AUC) of receive operating characteristic (ROC). …”
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Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets by Yongfei Wang, Haoxuan Zeng, Chongxu Liu, Jianjun Chen, Yihong Huang, Xianju Zhou

“…Methods We analyzed GSE143272 and GSE4290 microarray datasets from the NCBI Gene Expression Omnibus database, which is established based on evaluations of peripheral blood samples. …”
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Integrated Bioinformatics Analysis of Hub Genes and Pathways in Anaplastic Thyroid Carcinomas by Xueren Gao, Jianguo Wang, Shulong Zhang

“…The aim of the present study was to identify hub genes and pathways in ATC by microarray expression profiling. Two independent datasets (GSE27155 and GSE53072) were downloaded from GEO database. …”
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Bioinformatic identification of Single Nucleotide Polymorphisms (SNPs) in keratin-associated protein genes in alpacas (Vicugna pacos) by Deyanira Figueroa, Manuel More, Gustavo Gutiérrez, F. Abel Ponce de León

“…Of these, 35 SNPs were included in the 76K alpaca SNP microarray and 32 SNPs were confirmed in a population of 936 alpacas.…”
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Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS by Iwona Stelniec‐Klotz, Stefan Legewie, Oleg Tchernitsa, Franziska Witzel, Bertram Klinger, Christine Sers, Hanspeter Herzel, Nils Blüthgen, Reinhold Schäfer

“…We measured mRNA and protein levels in manipulated cells by microarray, RT–PCR and western blot analysis, respectively. …”
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Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks by James N. Warnock, Bindu Nanduri, Carol A. Pregonero Gamez, Juliet Tang, Daniel Koback, William M. Muir, Shane C. Burgess

“…Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation). …”
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Cytogenomic characterization of mosaic X-ring chromosomes in seventeen patients with Turner syndrome (TS)-42 years of experience at a single-site institution by Anita Madison, Carolyn Applegate, Victoria Stinnett, Diego Marrero Miranda, Chantal Cross, Kamaria Cayton Vaught, Ying S. Zou, Jaclyn B. Murry

“…Current guidelines recommend karyotype as the first-line test rather than SNP microarray analysis when aneuploidy is suspected. Conventional cytogenetics is still necessary to understand structural abnormalities, provide genomic context, and detect low-level mosaicism. …”
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Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression by Itai Yanai, L Ryan Baugh, Jessica J Smith, Casey Roehrig, Shai S Shen‐Orr, Julia M Claggett, Andrew A Hill, Donna K Slonim, Craig P Hunter

“…We used RNAi and time series, whole‐genome microarray analyses to systematically perturb and characterize components of a Caenorhabditis elegans lineage‐specific transcriptional regulatory network. …”
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Comparative systems biology of human and mouse as a tool to guide the modeling of human placental pathology by Brian Cox, Max Kotlyar, Andreas I Evangelou, Vladimir Ignatchenko, Alex Ignatchenko, Kathie Whiteley, Igor Jurisica, S Lee Adamson, Janet Rossant, Thomas Kislinger

“…We collected protein and mRNA expression data through shot‐gun proteomics and microarray expression analysis of the highly vascular exchange region, microdissected from the human and mouse near‐term placenta. …”
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A very rare case of a newborn with tetrasomy 9p and literature review by Merve Süleyman, Sümeyra Oğuz, Gözdem Kaykı, Hasan Tolga Çelik, Pelin Özlem Şimsek-Kiper, Gülen Eda Utine, Şule Yiğit

“…Microarray analysis revealed 38,584 kb tetrasomic region at 9p24.3p13.1. …”
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