Mechanism-based screen for G1/S checkpoint activators identifies a selective activator of EIF2AK3/PERK signalling. by Simon R Stockwell, Georgina Platt, S Elaine Barrie, Georgia Zoumpoulidou, Robert H Te Poele, G Wynne Aherne, Stuart C Wilson, Peter Sheldrake, Edward McDonald, Mathilde Venet, Christelle Soudy, Frédéric Elustondo, Laurent Rigoreau, Julian Blagg, Paul Workman, Michelle D Garrett, Sibylle Mittnacht

“…Our work therefore identifies CCT020312 as a novel small molecule chemical tool for the selective activation of EIF2A-mediated translation control with utility for proof-of-concept applications in EIF2A-centered therapeutic approaches, and as a chemical starting point for pathway selective agent development. …”
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Themis2/ICB1 is a signaling scaffold that selectively regulates macrophage Toll-like receptor signaling and cytokine production. by Matthew J Peirce, Matthew Brook, Nicholas Morrice, Robert Snelgrove, Shajna Begum, Alessandra Lanfrancotti, Clare Notley, Tracy Hussell, Andrew P Cope, Robin Wait

“…<h4>Background</h4>Thymocyte expressed molecule involved in selection 1 (Themis1, SwissProt accession number Q8BGW0) is the recently characterised founder member of a novel family of proteins. …”
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Quantitative and Qualitative Variations in TILs in Risk Stratification and Patient Selection for Neoadjuvant Chemotherapy of the Luminal B and Triple-Negative Breast Cancer Immunop... by Ana Car Peterko, Koraljka Rajković Molek, Anita Savić Vuković, Tamara Gulić, Petra Valković Zujić, Eleonora Cini Tešar, Damir Juranić, Aleksandra Pirjavec Mahić, Franjo Lovasić, Gordana Đorđević, Manuela Avirović

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Screening of molecular markers associated with hornless traits in Qira black sheep by Wen Zhou, Lijun Zhu, Yuwei Peng, Xiaopeng Li, Xuechen Zhang, Zhipeng Han, Jingping Li, Xinyu Bai, Qifeng Gao, Ruizhi Yang, Tao Jiang, Shudong Liu

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A practical distribution pattern of α-SMA-positive carcinoma associated fibroblasts indicates poor prognosis of patients with pancreatic ductal adenocarcinoma by Bo Li, Meilong Shi, Yang Wang, Penghao Li, Xiaoyi Yin, Guoxiao Zhang, Xiaochao Kang, Huan Wang, Suizhi Gao, Kailian Zheng, Xiaohan Shi, Xiongfei Xu, Yukun Zhou, Hui Jiang, Wei Jing, Shiwei Guo, Gang Jin

“…We utilized a tissue microarray to assess the spatial intensity of α-SMA expression within the tumor microenvironment. …”
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A Sensitive and Fast microRNA Detection Platform Based on CRlSPR-Cas12a Coupled with Hybridization Chain Reaction and Photonic Crystal Microarray by Bingjie Xue, Bokang Qiao, Lixin Jia, Jimei Chi, Meng Su, Yanlin Song, Jie Du

“…By using photonic crystal microarrays with a stopband-matched emission spectrum of the fluorescent-quencher modified reporter, the fluorescence signal was moderately enhanced to increase the sensitivity. …”
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Exposing Optimal Feature Sets for Enhancing Machine Learning Performance by Hiba Mohammed Al-Marwai, Ghaleb H. Al-Gaphari, Mohammed Mohammed Zayed

“…The majority of high dimensional gene expression data contain a significant amount of redundant genes, posing challenges for machine learning algorithms due to their high dimensionality. Feature selection has shown to be a successful method for improving classification algorithms performance by addressing two primary objectives: reducing the number of features and improving classification accuracy. …”
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Successful strategy of comprehensive pre-implantation genetic testing for Duchenne muscular dystrophy and chromosome balance using karyomapping and fluorescent PCR by Suchada Mongkolchaipak, Sirivipa Piyamongkol, Chutithep Teekaput, Rungthiwa Sirapat, Wanwisa Suriya, Tawiwan Pantasri, Theera Tongsong, Wirawit Piyamongkol

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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Gene-expression analysis can be an alternative, where the different machine learning and artificial intelligence techniques can be developed to learn microarray RNA details for breast cancer detection. …”
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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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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

“…The exact aetiology and pathogenesis of vitiligo remain incompletely understood.MethodsFirst, a microarray dataset of blood samples from multiple patients with vitiligo was collected from GEO database.The limma package was used to analyze the microarray data and identify significant differentially expressed genes (DEGs). …”
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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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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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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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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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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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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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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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Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS by Iwona Stelniec‐Klotz, Stefan Legewie, Oleg Tchernitsa, Franziska Witzel, Bertram Klinger, Christine Sers, Hanspeter Herzel, Nils Blüthgen, Reinhold Schäfer

“…We measured mRNA and protein levels in manipulated cells by microarray, RT–PCR and western blot analysis, respectively. …”
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