Global gene expression profiles during acute dengue revealed by microarray analysis by Hoang, LT, Hibberd, M, Farrar, J, Simmons, C

An optimized microarray platform for assaying genomic variation in Plasmodium falciparum field populations by Tan, J, Miller, B, Tan, A, Patel, J, Cheeseman, I, Anderson, T, Manske, M, Maslen, G, Kwiatkowski, D, Ferdig, M

Neuro−Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis by Wang, Z, Palade, V, Xu, Y

Identification of core and variable components of the Salmonella enterica subspecies I genome by microarray. by Anjum, M, Marooney, C, Fookes, M, Baker, S, Dougan, G, Ivens, A, Woodward, M

“…We have performed microarray hybridization studies on 40 clinical isolates from 12 common serovars within Salmonella enterica subspecies I to identify the conserved chromosomal gene pool. …”

Key issues in conducting a meta-analysis of gene expression microarray datasets. by Ramasamy, A, Mondry, A, Holmes, C, Altman, D

Optimal amounts of fluorescent dye improve expression microarray results in tumor specimens. by Naderi, A, Ahmed, A, Wang, Y, Brenton, J, Caldas, C

“…Expression microarrays have great potential for clinical use but variability of the results represents a challenge for reliable practical application. …”

Acoustic whole blood plasmapheresis chip for prostate specific antigen microarray diagnostics. by Lenshof, A, Ahmad-Tajudin, A, Järås, K, Swärd-Nilsson, A, Aberg, L, Marko-Varga, G, Malm, J, Lilja, H, Laurell, T

“…Further, we successfully linked the plasmapheresis microchip to our previously developed porous silicon sandwich antibody microarray chip for prostate specific antigen (PSA) detection. …”

Key issues in conducting a meta-analysis of gene expression microarray datasets. by Ramasamy, A, Mondry, A, Holmes, C, Altman, D

Functions of Burkholderia virulence factors: input from proteomics and DNA microarray analyses by Vellasamy, K.M., Mariappan, V., Hashim, Onn Haji, Vadivelu, J.

“…In this article, we reviewed the current knowledge on interactions of Burkholderia spp. pathogens with their host mainly from the perspective of data that was generated from recent proteomics and DNA microarray investigations. …”

Utilization of Microarray Technology for Identification of Disease Response Genes in Banana (Musa Spp.) by Lim, Kean Jin

“…The emphasis was give to clones that have putative function in pathogen response or are pathogenesis related following Fusarium fungal infection. The cDNA microarray analysis identified 55 M. acuminata x balbisiana cv Mutiara clones that were transcriptional responsive to the Fusarium fungus infection. …”
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Identification of Differentially Expressed Genes in Human Bladder Cancer using Oligonucleotide Microarray by Nik Hassan, Nik Norliza

“…Labeled cRNA probes were co-hybridized to the microarray slide containing 1,853 cancer related genes. …”
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An integrated framework based deep learning for cancer classification using microarray datasets. by Alrefai, Nashat, Ibrahim, Othman, Shehzad, Hafiz Muhammad Faisal, Altigani, Abdelrahman, Abu-ulbeh, Waheeb, Alzaqebah, Malek, Alsmadi, Mutasem K.

“…In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. …”

Classification of breast cancer microarray data using radial basis function network by Mazlan, Umi Hanim

“…By using the microarray, thousands of genes expression can be determined simultaneously. …”
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Using bayesian networks to construct gene regulatory networks from microarray data by Ai, Kung Tan, Mohamad, Mohd. Saberi

“…In this research, Bayesian network is proposed as the model to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset due to its capability of handling microarray datasets with missing values. The goal of this research is to study and to understand the framework of the Bayesian networks, and then to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset by developing Bayesian networks using hill-climbing algorithm and Efron’s bootstrap approach and then the performance of the constructed gene networks of Saccharomyces cerevisiae are evaluated and are compared with the previously constructed sub-networks by Dejori [14]. …”
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A pathway-based approach for analyzing microarray data using random forests by Tan Ah Chik @ Mohamad, Mohd. Saberi, Shi, Chin Hui, Deris, Safaai, Ibrahim, Zuwairie

“…The use of random survival forests for survival outcomes in analyzing microarray data allows researchers to obtain results that are more closely tied with the biological mechanism of diseases.…”

A pathway-based approach for analyzing microarray data using random forests by Chin, Hui Shi, Mohamad, Mohd. Saberi, Deris, Safaai, Ibrahim, Dzuwairie

“…Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, less study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret Thus, the pathway-based survival analysts using machine learning methods represents a promising approach for generating new biological hypothesis from micro array studies. …”

Review on weighting category of pathway topology-based microarray analysis in multiclass classification by Zalmiyah Zakaria, Azurah A Samah

Deep learning-based cancer classification for microarray data: a systematic review by Alrefai, Nashat, Ibrahim, Othman

“…Deep neural networks are robust techniques and recently used extensively for building cancer classification models from different types of data. Nowadays, microarray gene expression datasets consider an essential source of data that is used in cancer classifications. …”

Linear epitopes of bony fish β-parvalbumins by Eric Franciskovic, Linnea Thörnqvist, Lennart Greiff, Lennart Greiff, Maria Gasset, Mats Ohlin, Mats Ohlin

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Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis by Dandan Jin, Yujie Jiao, Jie Ji, Wei Jiang, Wenkai Ni, Yingcheng Wu, Runzhou Ni, Cuihua Lu, Lishuai Qu, Hongbing Ni, Jinxia Liu, Weisong Xu, MingBing Xiao

Subjects: “…Microarray analysis…”
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