Gene markers of fracture healing in early stage and the regulatory mechanism during the process using microarray analysis by Chengxue Wang, Baochang Qi, Lei Tan, Jieping Cheng

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Combining gene expression microarrays and Mendelian randomization: exploring key immune-related genes in multiple sclerosis by Shuangfeng Ding, Yunyun Zhang, Yunzhe Tang, Ying Zhang, Mingyuan Liu

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Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets by Zahraa Ahmed, Mesut Çevik

“…However, the late enriched understanding of the genetic underpinnings of AD has been made possible due to recent advancements in data mining analysis methods, machine learning, and microarray technologies. However, the “curse of dimensionality” caused by the high-dimensional microarray datasets impacts the accurate prediction of the disease due to issues of overfitting, bias, and high computational demands. …”
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Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion. by Doris Steger, David Berry, Susanne Haider, Matthias Horn, Michael Wagner, Roman Stocker, Alexander Loy

“…Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias.<h4>Conclusions</h4>Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.…”
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Genetic findings of children with congenital heart diseases using chromosomal microarray and trio-based whole exome sequencing by Rui Guo, Chunhong Duan, Mehdi Zarrei, Miriam S. Reuter, Rui Dong, Guangye Zhang, Xiaomeng Yang, Haiyang Zhang, Ying Wang, Stephen W. Scherer, Yi Liu, Zhongtao Gai

“…In individuals with extracardiac features, the overall detection rate was significantly higher (61.5%) than in individuals with isolated CHD (17.3%) (P = 4.6 × 10− 3). …”
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Deciphering the prokaryotic community and metabolisms in South African deep-mine biofilms through antibody microarrays and graph theory. by Yolanda Blanco, Luis A Rivas, Antonio García-Moyano, Jacobo Aguirre, Patricia Cruz-Gil, Arantxa Palacín, Esta van Heerden, Víctor Parro

“…Both culture-independent techniques coincided in detecting features related to aerobic sulfur-oxidizers, aerobic chemoorganotrophic Alphaproteobacteria and metanotrophic Gammaproteobacteria. 16S rRNA gene sequencing detected phylotypes related to nitrate-reducers and anaerobic sulfur-oxidizers, whereas the EMChip66 detected immunological features from methanogens and sulfate-reducers. …”
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A customizable multiplex protein microarray for antibody testing and its application for tick-borne and other infectious diseases by Hari Krishnan Krishnamurthy, Vasanth Jayaraman, Karthik Krishna, Tianhao Wang, Kang Bei, Chithra Changalath, Shiny Matilda, Alex J. Rai, Renata Welc-Falęciak, Agnieszka Pawełczyk, Lucas S. Blanton, Aleš Chrdle, Andrea Fořtová, Daniel Růžek, Gheyath K. Nasrallah, Laith J. Abu-Raddadi, Duaa W. Al-Sadeq, Marah Abed Alhakim Abdallah, Daniele Lilleri, Chiara Fornara, Piera D’Angelo, Milena Furione, Maria Söderlund-Venermo, Klaus Hedman, Dimosthenis Chochlakis, Eirini Makridaki, Artemis Ntoula, Anna Psaroulaki, Angélica Escárcega-Ávila, John J. Rajasekaran

“…The diagnostic tests currently available for tick-borne diseases are severely limited in their ability to provide accurate results and cannot detect multiple pathogens in a single run. The multiplex protein microarray developed at Vibrant was designed to detect multiple serological antibodies thereby detecting exposure to multiple pathogens simultaneously. …”
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IDENTIFYING IMPORTANT GENES IN OVARIAN CANCER FROM HIGH-DIMENSIONAL MICROARRAY DATA USING SIFS-CART METHOD by Ni Kadek Emik Sapitri, Umu Sa'adah, Nur Shofianah

“…Ovarian cancer can be identified from microarray data using machine learning. Many studies only focus on improving the machine learning classification algorithms to achieve higher performance. …”
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No Amplifications of Hypoxia-Inducible Factor-1α Gene in Invasive Breast Cancer: A Tissue Microarray Study by Marije M. Vleugel, Reinhard Bos, Horst Buerger, Petra van der Groep, Outi R. Saramäki, Tapio Visakorpi, Elsken van der Wall, Paul J. van Diest

“…Methods: Protein and gene expression of HIF‐1α were analyzed on a tissue microarray of 94 breast cancers by immunohistochemistry and fluorescent in situ hybridization (FISH), respectively. …”
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PD-L1 quantification across tumor types using the reverse phase protein microarray: implications for precision medicine by Mariaelena Pierobon, Johann De Bono, Ruth Riisnaes, Giuseppe Giaccone, Matthew Blackburn, Lance Liotta, Vienna Ludovini, Neil J Shah, Giuseppina Improta, Antonella Ravaggi, Niven Mehra, Angelo Sidoni, Elisa Baldelli, K Alex Hodge, Guido Bellezza, Guido Gambara, Martina Mandarano, Chamodya Ruhunusiri, Bryant Dunetz, Maysa Abu-Khalaf, Julia Wulfkuhle, Rosa I Gallagher, Franco Odicino, Maria Isabella Sereni, Angela Zupa, Perry Demsko, Lucio Crino', Emanuel F Petricoin

“…Using a high throughput immunoassay, the reverse phase protein microarray (RPPA), coupled with a fluorescence-based detection system, this study compared the performance of six anti-PD-L1 antibody clones on 666 tumor samples.Methods PD-L1 expression was measured using five antibody clones (22C3, 28–8, CAL10, E1L3N and SP142) and the therapeutic antibody atezolizumab on 222 lung, 71 ovarian, 52 prostate and 267 breast cancers, and 54 metastatic lesions. …”
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Local Mean Normalization of Microarray Element Signal Intensities across an Array Surface: Quality Control and Correction of Spatially Systematic Artifacts by Carlo Colantuoni, George Henry, Scott Zeger, Jonathan Pevsne

“…Here we present a methodology for the normalization of element signal intensities to a mean intensity calculated locally across the surface of a DNA microarray. These methods allow the detection and/or correction of spatially systematic artifacts in microarray data. …”
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Multiplex peptide microarray profiling of antibody reactivity against neglected tropical diseases derived B-cell epitopes for serodiagnosis in Zimbabwe. by Arthur Vengesai, Thajasvarie Naicker, Herald Midzi, Maritha Kasambala, Tariro L Mduluza-Jokonya, Simbarashe Rusakaniko, Francisca Mutapi, Takafira Mduluza

“…In this framework, we present a pilot study to design and produce a peptide microarray for the integrated surveillance of neglected tropical diseases. …”
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Microarray-Based Comparisons of Ion Channel Expression Patterns: Human Keratinocytes to Reprogrammed hiPSCs to Differentiated Neuronal and Cardiac Progeny by Leonhard Linta, Marianne Stockmann, Qiong Lin, André Lechel, Christian Proepper, Tobias M. Boeckers, Alexander Kleger, Stefan Liebau

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Microarray analysis of LTR retrotransposon silencing identifies Hdac1 as a regulator of retrotransposon expression in mouse embryonic stem cells. by Judith Reichmann, James H Crichton, Monika J Madej, Mary Taggart, Philippe Gautier, Jose Luis Garcia-Perez, Richard R Meehan, Ian R Adams

“…Here we show, and experimentally verify, that cryptic repetitive element probes present in Illumina and Affymetrix gene expression microarray platforms can accurately and sensitively monitor repetitive element expression data. …”
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Oral Bacteria of Children with Turner Syndrome by Gülcan Ünsal, Nursen Topçuoğlu, Yeliz Güven, Şükran Poyrazoğlu, Güven Külekçi, Oya Aktören

“…Plaque index and gingival index levels were significantly higher in the Turner group and dft was significantly higher in the control group (p<0.05). As a result, microarray analysis, Prevotella intermedia, Fusobacterium nucleatum, Eikenella corrodens, Aggregatibacter actinomycetemcomitans, Actinomyces viscosus were detected at high levels in the Turner group (p<0.05).Conclusion:Besides dental and craniofacial anomalies, clinicians should be alert to the early diagnosis and treatment of periodontal problems in patients with TS.…”
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Genome Array on Differentially Expressed Genes of Skin Tissue in Cashmere Goat at Early Anagen of Cashmere Growth Cycle Using DNA Microarray by Jiang DI, Xin-ming XU, Lazate Ainiwaer, Yan-hua ZHANG, Ke-chuan TIAN, Li-juan YU, Wei-wei WU, Hanikezi Tulafu, Xue-feng FU, Marzeya Yasen

“…Approximately 10 200 probe sets were detected in skin tissue of 2-yr-old cashmere goat. …”
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Sequential transcriptome profiling: comparative analysis of normal and canine lymphoma preceding detailed T-cell and B-cell subtype comparison by Yeji Kim, Yeji Kim, Jihyun Kim, Jihyun Kim, Yunji Song, Yunji Song, Keunhwan Jang, Se Eun Kim, Se Eun Kim, Se Eun Kim, Ha-Jung Kim, Ha-Jung Kim

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Interdigitated Microarray Electrodes Based on Boron-doped Amorphous Carbon for Highly Sensitive Electroanalysis of Redox Analytes Having a Higher Standard Potential by Kensuke HONDA, Shinpei OHTOMO

“…Interdigitated microarray electrodes were developed based on boron-doped amorphous carbon (B-a-C-IDA) that shows extremely higher overpotential for H2 and O2 evolution. …”
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Candidate Anxiety‐Related Genes in the Hippocampus of Hatano Male Rats: Anxiolytic Action of Neuromedin U in the Hippocampus by Kaito Sato, Atsuhiro Ishii, Shohei Kobayashi, Taichi Hatakeyama, Gen Watanabe, Tomoko Soga, Ishwar Parhar, Takashi Matsuwaki, Shogo Moriya, Ryo Ohta, Shuichi Chiba, Maiko Kawaguchi

“…The present study focuses on the hippocampus, which is associated with anxiety‐like behavior, and used microarray analysis and RT‐qPCR to select genes with differential expression in the hippocampus between HAA and LAA (Experiment 1). …”
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A standardized fold change method for microarray differential expression analysis used to reveal genes involved in acute rejection in murine allograft models by Weichen Zhou, Yi Wang, Masayuki Fujino, Leming Shi, Li Jin, Xiao‐Kang Li, Jiucun Wang

“…We suggest that SFC can be utilized to stably and effectively detect differential gene expression and to explore microarray data in further studies.…”
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