Bioinformatics analysis of gene expression microarray in lupus nephritis by JIANG Qiu-zhu, YANG Hui, ZHOU Xiao-shuang

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Optimization based tumor classification from microarray gene expression data. by Onur Dagliyan, Fadime Uney-Yuksektepe, I Halil Kavakli, Metin Turkay

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Application of Principal Component Analysis for Gene Sequences (cDNA microarrays) by Yalçın Tahtalı, Zeynel Cebeci

“…In this study, principal component analysis has been applied on data comprising of 6675 gene and 20 sequence collected by using cDNA microarray technology from livers of mice used in toxicology studies in certain time periods. …”
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A microarray platform-independent classification tool for cell of origin class allows comparative analysis of gene expression in diffuse large B-cell lymphoma. by Matthew A Care, Sharon Barrans, Lisa Worrillow, Andrew Jack, David R Westhead, Reuben M Tooze

“…We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). …”
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Evidence for a major role of antisense RNAs in cyanobacterial gene regulation by Jens Georg, Björn Voß, Ingeborg Scholz, Jan Mitschke, Annegret Wilde, Wolfgang R Hess

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Dysregulated circulating SOCS3 and haptoglobin expression associated with stable coronary artery disease and acute coronary syndrome: An integrated study based on bioinformatics an... by Xunnan Zhang, Xi Lv, Xiandong Li, Yaping Wang, Hao-yu Lin, Jicai Zhang, Chunyan Peng

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Construction and evaluation of yeast expression networks by database-guided predictions by Katharina Papsdorf, Siyuan Sima, Gerhard Richter, Klaus Richter

“…DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. …”
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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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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). …”
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Correcting for Signal Saturation Errors in the Analysis of Microarray Data by L.-L. Hsiao, R.V. Jensen, T. Yoshida, K.E. Clark, J.E. Blumenstock, S.R. Gullans

“…As an illustration of this problem, two subclasses of normal human tissue samples (six liver and six lung samples) were analyzed with GeneChip® probe arrays to evaluate the patterns of expression for approximately 7000 human genes. …”
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Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data by Maryam Farhadian, Paulo J. G. Lisboa, Abbas Moghimbeigi, Jalal Poorolajal, Hossein Mahjub

“…An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. …”
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Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature by Martin Sebastian Staege

“…Gene Expression Music Algorithm (GEMusicA) is a method for the transformation of DNA microarray data into melodies that can be used for the characterization of differentially expressed genes. …”
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Prolonged application of high fluid shear to chondrocytes recapitulates gene expression profiles associated with osteoarthritis. by Fei Zhu, Pu Wang, Norman H Lee, Mary B Goldring, Konstantinos Konstantopoulos

“…Confirmation of the expression patterns of select genes was obtained by qRT-PCR. …”
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A RNA-Seq Analysis of the Rat Supraoptic Nucleus Transcriptome: Effects of Salt Loading on Gene Expression. by Kory R Johnson, C C T Hindmarch, Yasmmyn D Salinas, YiJun Shi, Michael Greenwood, See Ziau Hoe, David Murphy, Harold Gainer

“…In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. …”
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Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach. by Mojtaba Ganjali, Taban Baghfalaki, Damon Berridge

“…In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. …”
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Integrated Use of Statistical-Based Approaches and Computational Intelligence Techniques for Tumors Classification Using Microarray by Chia-Ding Hou, Yuehjen E. Shao

“…Microarray experiments can lead to a more thorough grasp of the molecular variations among tumors because they can allow the monitoring of expression levels in cells for thousands of genes simultaneously. …”
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Gene set enrichment in eQTL data identifies novel annotations and pathway regulators. by Chunlei Wu, David L Delano, Nico Mitro, Stephen V Su, Jeff Janes, Phillip McClurg, Serge Batalov, Genevieve L Welch, Jie Zhang, Anthony P Orth, John R Walker, Richard J Glynne, Michael P Cooke, Joseph S Takahashi, Kazuhiro Shimomura, Akira Kohsaka, Joseph Bass, Enrique Saez, Tim Wiltshire, Andrew I Su

“…Recently, many studies have used microarrays to measure gene expression levels across genetic mapping populations. …”
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A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. by Francesco Gregoretti, Vincenzo Belcastro, Diego di Bernardo, Gennaro Oliva

“…The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. …”
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Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer by Yi Tian, Wen-ya Zhao, Yi-ru Liu, Wen-wen Song, Qiao-xin Lin, Yan-na Gong, Yi-ting Deng, Dian-na Gu, Ling Tian

“…Results Through the implementation of machine learning methods, we successfully identified the catalase (CAT) gene. Our analysis revealed that individuals with high expression of the CAT gene experienced improved survival rates. …”
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Mining TCGA data using Boolean implications. by Subarna Sinha, Emily K Tsang, Haoyang Zeng, Michela Meister, David L Dill

“…In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). …”
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