Consistency and Stability in Feature Selection for High-Dimensional Microarray Survival Data in Diffuse Large B-Cell Lymphoma Cancer by Kazeem A. Dauda, Rasheed K. Lamidi

“…High-dimensional survival data, such as microarray datasets, present significant challenges in variable selection and model performance due to their complexity and dimensionality. …”
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Optimization based tumor classification from microarray gene expression data. by Onur Dagliyan, Fadime Uney-Yuksektepe, I Halil Kavakli, Metin Turkay

“…<h4>Background</h4>An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. …”
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Phenotypic Profiling of Selected Cellulolytic Strains to Develop a Crop Residue-Decomposing Bacterial Consortium by Arman Shamshitov, Egidija Satkevičiūtė, Francesca Decorosi, Carlo Viti, Skaidrė Supronienė

“…Therefore, this study aimed to address these limitations by assessing the metabolic profiles of five previously identified cellulolytic bacterial strains, including <i>Bacillus pumilus</i> 1G17, <i>Micromonospora chalcea</i> 1G49, <i>Bacillus mobilis</i> 5G17, <i>Streptomyces canus</i> 1TG5, and <i>Streptomyces achromogenes</i> 3TG21 using Biolog Phenotype Microarray analysis. Moreover, this study evaluated the impact of wheat straw inoculation with single strains and a bacterial consortium on soil organic carbon and nitrogen content in a pot experiment. …”
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Effective detection of 148 cases chromosomal mosaicism by karyotyping, chromosomal microarray analysis and QF-PCR in 32,967 prenatal diagnoses by Yi Deng, Lan Zeng, Zhiling Wu, Jin Wang, Mengling Ye, Chun Chen, Ping Wei, Danni Wang, Guangming Deng, Shuyao Zhu

“…Methods A total of 148 fetuses diagnosed with chromosomal mosaicism by karyotyping with copy number variant sequencing (CNV-seq)/ chromosomal microarray analysis (CMA) and quantitative fluorescent polymerase chain reaction (QF-PCR) were selected, and the results from three the methods were compared and further analyzed. …”
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CRISPR screens and lectin microarrays identify high mannose N-glycan regulators by C. Kimberly Tsui, Nicholas Twells, Jenni Durieux, Emma Doan, Jacqueline Woo, Noosha Khosrojerdi, Janiya Brooks, Ayodeji Kulepa, Brant Webster, Lara K. Mahal, Andrew Dillin

“…We used CRISPR screens to uncover the expanded network of genes controlling high mannose levels, followed by lectin microarrays to fully measure the complex effect of select regulators on glycosylation globally. …”
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Machine learning identification of molecular targets for medulloblastoma subgroups using microarray gene fingerprint analysis by Alicia Reveles-Espinoza, Ulises Villela, Edgar Hernandez-Martinez, Isaac Chairez, Sergio Juárez-Méndez, J. Casanova-Moreno, Ma. del Pilar Eguía-Aguilar, Luis Figueroa-Yáñez, Adriana Vallejo-Cardona, Iván Salgado

“…The classification achieved an average accuracy of 96%, demonstrating the effectiveness of the proposed approach. Feature selection using the Kruskal–Wallis and χ2 tests revealed statistically relevant genes contributing to subgroup discrimination. …”
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Microarray analysis of virulence gene profiles in Salmonella serovars from food/food animal environment by Wen Zou, Sufian F Al-Khaldi, William S Branham, Tao Han, James C Fuscoe, Jing Han, Steven L Foley, Joshua Xu, Hong Fang, Carl E Cerniglia, Rajesh Nayak

“…Conclusions: This hybridization array presents an accurate and cost-effective method for evaluating the disease-causing potential of Salmonella in outbreak investigations by targeting a selective set of Salmonella-associated virulence genes.  …”
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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

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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

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Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification by Shahad Alkamli, Hala Alshamlan

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FEATURE SELECTION IN THE TASK OF MEDICAL DIAGNOSTICS ON MICROARRAY DATA by N. G. Zagoruiko, O. A. Kutnenko, I. A. Borisova, V. V. Dyubanov, D. A. Levanov, O. A. Zyranov

“…The high efficiency of the algorithm is illustrated by results of solving the task of disease recognition on a microarray dataset.…”
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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

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Silver coated porous silicon microarray SERS platform for detecting aflatoxin B1 fumonisin B1 and ochratoxin A by Rohit Kumar Singh, Narsingh R. Nirala, Sudharsan Sadhasivam, Divagar Muthukumar, Edward Sionov, Giorgi Shtenberg

“…Herein, we present a newly developed nanostructured microarray based on silver-coated porous silicon (Ag-pSi) used as a surface-enhanced Raman scattering (SERS) transducer. …”
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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

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Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications by Fangling Wang, Azlan Mohd Zain, Yanjie Ren, Mahadi Bahari, Azurah A. Samah, Zuraini Binti Ali Shah, Norfadzlan Bin Yusup, Rozita Abdul Jalil, Azizah Mohamad, Nurulhuda Firdaus Mohd Azmi

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Comprehensive Review of Hybrid Feature Selection Methods for Microarray-Based Cancer Detection by Azka Khoirunnisa, Didit Adytia, Mustafa Mat Deris, Adiwijaya

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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fathima Fajila, Yuhanis Yusof

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Identification and characterization of gene networks and key genes related to the high-yield production of milk in high-yield cows using meta-analysis of microarray data by Mahdi Rahmatzadeh, Reza Shokri-Gharelo, Morteza Derakhti-Dizaji, Asghar Bazzaz, Bizhan Mahmoudi

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Identification and validation of a novel autoantibody biomarker panel for differential diagnosis of pancreatic ductal adenocarcinoma by Metoboroghene O. Mowoe, Metoboroghene O. Mowoe, Hisham Allam, Joshua Nqada, Marc Bernon, Karan Gandhi, Sean Burmeister, Urda Kotze, Miriam Kahn, Christo Kloppers, Suba Dharshanan, Zafirah Azween, Pamela Maimela, Paul Townsend, Eduard Jonas, Jonathan M. Blackburn, Jonathan M. Blackburn

“…Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).ResultsCombinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

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