Transcriptional responses in different mouse models of septic liver injury differ from those in patients with septic liver injury by Qin Yan, Qin Yan, Qin Yan, Wei Fan, Wei Fan, Wei Fan, Xinsen He, Xinsen He, Xinsen He, Shi Zheng, Shi Zheng, Shi Zheng, Xiaolin Zhong, Xiaolin Zhong, Xiaolin Zhong

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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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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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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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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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A multi-omic meta-analysis reveals novel mechanisms of insecticide resistance in malaria vectors by Sanjay C. Nagi, Victoria A. Ingham

“…Here we show an inverse relationship between genetic diversity and gene expression, with highly expressed genes experiencing stronger purifying selection. Gene expression clusters physically in the genome, revealing potential coordinated regulation, and we find that highly over-expressed genes are associated with selective sweep loci. …”
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The impact of the nucleosome code on protein-coding sequence evolution in yeast. by Tobias Warnecke, Nizar N Batada, Laurence D Hurst

“…We identify nucleosome positioning as a likely candidate to set up such a DNA-level selective regime and use high-resolution microarray data in yeast to compare the evolution of coding sequence bound to or free from nucleosomes. …”
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Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. by Enrico Glaab, Jaume Bacardit, Jonathan M Garibaldi, Natalio Krasnogor

“…A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. …”
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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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Analysis of Gene Expression in Human Dermal Fibroblasts Treated with Senescence-Modulating COX Inhibitors by Jeong A. Han, Jong-Il Kim

“…In contrast, celecoxib, another COX-2–selective inhibitor, and aspirin, a non-selective COX inhibitor, accelerated the senescence and aging. …”
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Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues by Qiyu Wang, Jingwei Yuan, Jingwei Yuan, Mengdi Zhang, Haiyan Jia, Hongjie Lu, Yan Wu

“…DEGs selected with the limma package and module genes derived from the WGCNA were intersected using the Venn package in R. …”
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An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set by Jing Zhang, Guang Lu, Jiaquan Li, Chuanwen Li

“…Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. …”
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Aberrant expression of shared master-key genes contributes to the immunopathogenesis in patients with juvenile spondyloarthritis. by Lovro Lamot, Fran Borovecki, Lana Tambic Bukovac, Mandica Vidovic, Marija Perica, Kristina Gotovac, Miroslav Harjacek

“…Microarray results and bioinformatical analysis revealed 745 differentially expressed genes involved in various inflammatory processes, while qRT-PCR analysis of selected genes confirmed data universality and specificity of expression profiles in jSpA patients. …”
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Nanomaterials based biosensors applied for detection of aflatoxin B1 in cereals: a review by Loyce Namanya, Emma Panzi Mukhokosi, Ediriisa Mugampoza

“…Electrochemical biosensors are more reliable, selective, and affordable analytical tools with better detection limits and shorter response times than other biosensors. …”
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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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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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Host genome drives the microbiota enrichment of beneficial microbes in shrimp: exploring the hologenome perspective by Fernanda Cornejo-Granados, Luigui Gallardo-Becerra, Sandra Romero-Hidalgo, Alonso A. Lopez-Zavala, Andrés Cota-Huízar, Melany Cervantes-Echeverría, Rogerio R. Sotelo-Mundo, Adrian Ochoa-Leyva

“…Results Using genome-wide SNP microarray analysis, we confirmed that Gen1 and Gen2 represented distinct genetic populations. …”
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Exploring Flexible Penalization of Bayesian Survival Analysis Using Beta Process Prior for Baseline Hazard by Kazeem A. Dauda, Ebenezer J. Adeniyi, Rasheed K. Lamidi, Olalekan T. Wahab

“…High-dimensional data have attracted considerable interest from researchers, especially in the area of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. …”
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