BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…The BIMSSA model implements a pipelined feature selection method to effectively handle high-dimensional microarray data. …”
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Search for polymorphic variants of candidate genes contributing to individual radiosensitivity by D. S. Isubakova, N. V. Litviakov, O. S. Tsymbal, T. V. Usova, M. Yu. Tsyplenkova, I. V. Milto, R. M. Takhauov

“…DNA was genotyped using 257 SNPs of cyclin genes and neighboring intergenic regions using DNA microarrays from the high-density CytoScan HD Array (Affymetrix, USA).Results. …”
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Ensemble Algorithm Based on Gene Selection, Data Augmentation, and Boosting Approaches for Ovarian Cancer Classification by Zne-Jung Lee, Jing-Xun Cai, Liang-Hung Wang, Ming-Ren Yang

“…<b>Background:</b> Ovarian cancer is a difficult and lethal illness that requires early detection and precise classification for effective therapy. Microarray technology has permitted the simultaneous assessment of hundreds of genes’ expression levels, yielding important insights into the molecular pathways driving ovarian cancer. …”
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Role of transcriptomics in the study of oral cancer by Prabhu Manickam Natarajan, Vidhya Rekha Umapathy

“…We delve into RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), long non-coding RNA sequencing (lncRNA-seq), microarray analysis, and small RNA profiling, showcasing their unique contributions to unraveling the complexities of oral cancer. …”
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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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Rapid Whole-Genome Sequencing in Critically Ill Infants and Children with Suspected, Undiagnosed Genetic Diseases: Evolution to a First-Tier Clinical Laboratory Test in the Era of... by Rina Kansal

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Structure preserving t-SNE of matrix framed data by Soohyun Ahn, Johan Lim, Wei Jiang, Sungim Lee, Xinlei Wang

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Genetic testing for diagnosing neurodevelopmental disorders and epilepsy: a systematic review and meta-analysis by Yu-Ming Chang, Yen-Ta Huang, Pei-Chun Lai

“…Abstract Background Identifying the genetic causes of neurodevelopmental disorders (NDDs) and epilepsy is crucial for effective treatment and genetic counseling. Our objective was to determine the diagnostic yield of chromosomal microarray (CMA) and next-generation sequencing (NGS) methods—including targeted sequencing (TS), whole-exome sequencing (WES), and whole-genome sequencing (WGS)—in individuals with NDDs or epilepsy. …”
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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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Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification by Shahad Alkamli, Hala Alshamlan

“…This complexity necessitates advanced optimization methods for effective gene selection. <b>Methods</b>: This study introduces and evaluates the Nuclear Reaction Optimization (NRO)—drawing inspiration from nuclear fission and fusion—for identifying informative gene subsets in six benchmark cancer microarray datasets. …”
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A hybrid of an automated multi-filter with a spatial bound particle swarm optimization for gene selection and cancer classification by Anas Arram, Masri Ayob, Musatafa Abbas Abbood Albadr, Dheeb Albashish, Alaa Sulaiman

“…Therefore, early detection of cancer is critical for effective treatments. However, the main challenge in disease identification and classification, such as cancer microarray dataset, is the large number of genes. …”
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Identification of potential biomarkers and pathways related to major depressive disorder by integrated bioinformatic analysis and experimental validation by Ying Zeng, Lu-Qi Peng, Mei Zhang, Rong Zhong, Ke-Chao Nie, Wei Huang

“…Objective: To identify promising biomarkers for the pathogenesis of major depressive disorder (MDD). Methods: Microarray chips of MDD patients, including the GSE98793, GSE52790, and GSE39653 datasets, were obtained from the Gene Expression Omnibus database. …”
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A Network Analysis Approach to Detect and Differentiate Usher Syndrome Types Using miRNA Expression Profiles: A Pilot Study by Rama Krishna Thelagathoti, Wesley A. Tom, Chao Jiang, Dinesh S. Chandel, Gary Krzyzanowski, Appolinaire Olou, Rohan M. Fernando

“…<b>Methods:</b> We collected microarray miRNA-expression data from 17 samples, representing four patient-derived USH cell lines and a non-USH control. …”
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Assessment of the carcinogenic potential of particulate matter generated from 3D printing devices in Balb/c 3T3-1-1 cells by CheolHong Lim, DongSeok Seo

“…Various assays, such as the comet assay, cell transformation assays, microarray analysis, and glucose consumption measurement, were employed. …”
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Using effective subnetworks to predict selected properties of gene networks. by Gemunu H Gunaratne, Preethi H Gunaratne, Lars Seemann, Andrei Török

“…Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. …”
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The Gene Expression Profile of Milk Somatic Cells of Small Ruminant Lentivirus-Seropositive and -Seronegative Dairy Goats (<i>Capra hircus</i>) During Their First Lactation by Joanna Pławińska-Czarnak, Alicja Majewska, Joanna Magdalena Zarzyńska, Jarosław Kaba, Emilia Bagnicka

“…Statistical analysis was performed in GeneSpring 12 software. Results: Microarrays showed reduced expression of <i>DUSP26</i>, <i>PRLR</i>, <i>SCARA3</i>, <i>APBB2</i>, and <i>OR4F4</i> genes in SRLV-SP goats. …”
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Decreased mitochondrial‐related gene expression in adipose tissue after acute sprint exercise in humans: A pilot study by Mona Esbjörnsson, Håkan C. Rundqvist, Barbara Norman, Ted Österlund, Eric Rullman, Jens Bülow, Eva Jansson

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A decade of change – lessons learned from prenatal diagnostics in Central Denmark region in 2008–2018 by Dorte Launholt Lildballe, Naja Becher, Else Marie Vestergaard, Rikke Christensen, Stina Lou, Puk Sandager, Lars Henning Pedersen, Kasper Gadsbøll, Olav Bjørn Petersen, Ida Vogel

“…This retrospective study summarizes 11 years of using chromosomal microarray in invasive prenatal testing and presents the effect on diagnostic yield and turnaround time. …”
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Genetic etiology and pregnancy outcomes of fetal hyperechoic kidneys: a retrospective analysis by Meiying Cai, Na Lin, Ziheng Xiao, Ziheng Xiao, Hailong Huang, Lin Zheng, Liangpu Xu

“…Chromosome karyotyping and chromosomal microarray analysis (CMA) were performed on fetuses displaying this phenotype on prenatal ultrasound. …”
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Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma by Liam Burrows, Declan Sculthorpe, Hongrun Zhang, Obaid Rehman, Abhik Mukherjee, Ke Chen

“…This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. …”
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