Integrated analysis of hub genes and intrinsically disordered regions in triple-negative breast cancer by Azhar Iqbal, Faisal Ali, Sulaiman Ali Alharbi, Muhammad Sajid, Saleh Alfarraj, Momina Hussain, Tehmina Siddique, Rakhshanda Mustaq, Fakhra Shafique, Muhammad Sarfaraz Iqbal

“…Two microarray datasets were used to identify possible targets for TNBC, GSE38959 and GSE45827, retrieved from the Gene Expression Omnibus (GEO) database. …”
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HSPA6 augments garlic extract-induced inhibition of proliferation, migration, and invasion of bladder cancer EJ cells; Implication for cell cycle dysregulation, signaling pathway a... by Seung-Shick Shin, Jun-Hui Song, Byungdoo Hwang, Dae-Hwa Noh, Sung Lyea Park, Won Tae Kim, Sung-Soo Park, Wun-Jae Kim, Sung-Kwon Moon

“…In addition, GE impeded the migration and invasion of EJ cells via inhibition of MMP-9 expression followed by decreased binding activities of AP-1, Sp-1, and NF-κB motifs. Based on microarray datasets, we selected Heat shock protein A6 (HSPA6) as the most up-regulated gene responsible for the inhibitory effects of GE. …”
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EsoDetect: computational validation and algorithm development of a novel diagnostic and prognostic tool for dysplasia in Barrett’s esophagus by Migla Miskinyte, Benilde Pondeca, José B. Pereira-Leal, Joana Cardoso

“…Specifically, for the diagnostic application, three RNAseq datasets were employed, while for the prognostic application, nine microarray datasets were identified, and 25 previously described genes were validated. …”
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Noninvasive prediction of fetal growth restriction using maternal plasma cell-free RNA: a case-control study by Yihong Huang, Ruizhi Wang, Lixia Shen, Lingyi Kong, Peisong Chen, Zilian Wang, Zhuyu Li

“…The least absolute shrinkage and selection operator regression was used to select the hub genes from the cell-free RNA DEGs. …”
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miR-4780 Derived from N2-Like Neutrophil Exosome Aggravates Epithelial-Mesenchymal Transition and Angiogenesis in Colorectal Cancer by Liang Wang, Yuqiang Shan, Sixin Zheng, Jiangtao Li, Peng Cui

“…Differentially expressed miRNA in neutrophil exosomes have been sequenced by microarray profile, and the effect of N2-like neutrophil-derived exosomal miR-4780 on epithelial-mesenchymal transition (EMT) and angiogenesis was investigated. …”
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Enhancing Binary Classification Performance in Biomedical Datasets: Regularized ELM with SMOTE and Quantile Transforms Focused on Breast Cancer Analysis by Brilliant Friezka Aina, Meta Kallista, Ig. Prasetya Dwi Wibawa, Ginaldi Ari Nugroho, Ivana Meiska, Syifa Melinda Naf’an

“…Using microarray datasets, this research investigation addresses the problem of unbalanced data in binary classification tasks. …”
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MicroRNA controlled adenovirus mediates anti-cancer efficacy without affecting endogenous microRNA activity. by Ryan Cawood, Suet-Ling Wong, Ying Di, Dilair F Baban, Leonard W Seymour

“…RT-QPCR for mature mir122 in infected livers showed that quantity of mir122 remained unaffected. Genome wide mRNA microarray profiling of infected livers showed that although the transcript level of >3900 different mRNAs changed more than 2-fold following Ad5WT infection, less than 600 were changed by Ad5mir122. …”
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Integrated analysis of genomic and transcriptomic profiles identified a prognostic immunohistochemistry panel for esophageal squamous cell cancer by Yue Yu, Zhihua Li, Chenjun Huang, Haisheng Fang, Fei Zhao, Yue Zhou, Xianglong Pan, Qifan Li, Yu Zhuang, Liang Chen, Jing Xu, Wei Wang

“…Methods An integrated analysis was performed to screen and establish a prognostic panel using exome sequencing profile from 81 pairs of ESCC samples and RNA expression microarray data from 119 ESCC subjects. Two independent ESCC cohorts were recruited as training and validation groups to test the prognostic value. …”
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Inhibition of ATG5-mediated autophagy maintains PMAIP1 stability to promote cell apoptosis and suppress triple-negative breast cancer progression by Fangjian Shang, Hongfeng Nie, Liyan Du, Jin Shang, Xiangquan Song, Ya Chen, Hui Li, Zhuo Wang, Yixin Qi, Liyan Zhao

“…Methods TNBC-related datasets were retrieved from The Cancer Genome Atlas and selected by Prediction Analysis of Microarray 50 analysis to assess the expression of PMAIP1 in the samples. …”
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Two stable variants of Burkholderia pseudomallei strain MSHR5848 express broadly divergent in vitro phenotypes associated with their virulence differences. by A A Shea, R C Bernhards, C K Cote, C J Chase, J W Koehler, C P Klimko, J T Ladner, D A Rozak, M J Wolcott, D P Fetterer, S J Kern, G I Koroleva, S P Lovett, G F Palacios, R G Toothman, J A Bozue, P L Worsham, S L Welkos

“…Microscopic and colony morphology differences on six differential media were observed and only the Rough variant metabolized sugars in selective agar. Antimicrobial susceptibilities and lipopolysaccharide (LPS) features were characterized and phenotype microarray profiles revealed distinct metabolic and susceptibility disparities between the variants. …”
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A multi-omic meta-analysis reveals novel mechanisms of insecticide resistance in malaria vectors by Sanjay C. Nagi, Victoria A. Ingham

“…This study, employing a cross-species approach, integrates RNA-Sequencing, whole-genome sequencing, and microarray data to elucidate drivers of insecticide resistance in Anopheles gambiae complex and An. funestus. …”
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Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis. by Chao Hu, Fan Wang, Hui Xu, XiQi Dong, XiuJuan Xiong, Yun Zhang, TianCheng Zhao, YuanQiao He, LiBin Deng, XiongBing Lu

“…Lymph_inside % can be an effective biomarker for predicting MIBC prognosis. …”
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Characterization of transcriptional changes in ERG rearrangement-positive prostate cancer identifies the regulation of metabolic sensors such as neuropeptide Y. by Petra Massoner, Karl G Kugler, Karin Unterberger, Ruprecht Kuner, Laurin A J Mueller, Maria Fälth, Georg Schäfer, Christof Seifarth, Simone Ecker, Irmgard Verdorfer, Armin Graber, Holger Sültmann, Helmut Klocker

“…Functional analyses do not fully explain the selective pressure causing ERG rearrangement during the development of prostate cancer. …”
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Pan-cancer analysis identifies CD155 as a promising target for CAR-T cell therapy by Xiaohong Liu, Yue Sun, Boxu Lin, Hao Xiong, Xinyue Lu, Binghe Tan, Chenglin Zhang, Mingyao Liu, Juliang Qin, Na Zhang, Bing Du

“…Therefore, the exploration and rational selection of novel tumor targets is urgently needed. …”
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Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms by Xu Cui, Houlin Ji, Shengyang Guo, Ju Liu, Linyuan Zhang, Yongwei Jia, Yin Cui, Xiaoxiao Zhou

“…ObjectiveTo construct a diagnostic model of osteoarthritis related to methylation genes using machine learning algorithms, and analyze its prognostic value and biological functions.MethodsThe GSE 63695 and GSE162484 datasets including human osteoarthritis (OA) and normal samples were downloaded from the GEO datasets. The microarray chip data of chondrocytes were analyzed using R software to obtain differentially methylated genes. …”
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Penaeus vannamei Genetic Evaluation for Growth and Survival Traits During White Spot Syndrome Virus Infection Based on 55K SNP Chip by Yijing HE, Mianyu LIU, Sheng LUAN, Jie KONG, Xupeng LI, Baoxiang CAO, Kun LUO, Jian TAN, Jiawang CAO, Ping DAI, Guangfeng QIANG, Zhaoxin WANG, Juan SUI, Xianhong MENG

“…The genetic correlation between the survival time of individuals against WSSV and SS50 was 0.95±0.03, which was not significantly different from 1 (P > 0.05).The study showed that the genetic assessment of the growth of P. vannamei by microarray can effectively improve the accuracy of the assessment, the evaluation of WSSV survival time traits may be affected by the selection of individuals and other factors, and the prediction accuracy was not significantly improved. …”
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Evaluation of gene expression classification studies: factors associated with classification performance. by Putri W Novianti, Kit C B Roes, Marinus J C Eijkemans

“…The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. …”
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Sperm associated antigen 9 (SPAG9) a promising therapeutic target of ovarian carcinoma by Nirmala Jagadish, Rukhsar Fatima, Aditi Sharma, Sonika Devi, Vitusha Suri, Vikash Kumar, Anil Suri

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Long Noncoding RNAs and mRNA Regulation in Peripheral Blood Mononuclear Cells of Patients with Chronic Obstructive Pulmonary Disease by Xiaoyan Qu, Xiaomin Dang, Weijia Wang, Ying Li, Dan Xu, Dong Shang, Ying Chang

“…Methods. lncRNA and mRNA profiling of PBMCs from 17 smokers and 14 COPD subjects was detected by high-throughput microarray. The expression of dysregulated lncRNAs was validated by qPCR. …”
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Machine learning-based single-sample molecular classifier for cancer grading by Zoia Antysheva, Nikita Kotlov, Mariia V. Guryleva, Ivan Valiev, Viktor Svekolkin, Anna Belozerova, Sheila T. Yong, Dmitry Tabakov, Alexander Bagaev, Vladimir Kushnarev

“…Next, we showed that mGrades were effective in assessing risk levels for G2 samples. …”
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