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

Subjects: “…Biomarker detection…”
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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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Xq12q13.2 duplication detected in a child in selective exome screening by A. A. Dokshukina, Je. Shubina, D. N. Maslennikov, I. O. Sadelov, E. R. Tolmacheva, S. V. Ionushene, T. A. Bairova, L. V. Rychkova, D. Yu. Trofimov, D. N. Degtyarev

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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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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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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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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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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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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

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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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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization by Sumet Mehta, Fei Han, Muhammad Sohail, Bhekisipho Twala, Asad Ullah, Fasee Ullah, Arfat Ahmad Khan, Qinghua Ling

Subjects: “…Microarray gene selection…”
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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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RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

“…Abstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). …”
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DurdusTools—An Online Genetic Distance Calculation Tool for Efficient Variety Testing in Durum Wheat (<i>Triticum turgidum</i> L. subsp. <i>durum</i> (Desf.) Husn.) by Alexandra Ribarits, Svenja Bomers, Tadej Zerak, Oliver Alber, Jakob Seereiter, Antonio Escolano García, Ana Lázaro Somoza, Anna Pia Maria Giulini, Ferenc Somogyi, Sándor Kőrösi, Jutta Taferner-Kriegl

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Monitoring of Representational Difference Analysis Subtraction Procedures by Global Microarrays by T. Andersson, P. Unneberg, P. Nilsson, J. Odeberg, J. Quackenbush, J. Lundeberg

“…The compromise between focusing on only the important genes in certain cellular processes and achieving a complete picture is critical for the selection of strategy. We demonstrate how global microarray technology can be used for the exploration of the differentially expressed genes extracted through representational difference analysis (RDA). …”
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The Comparison of Three Measures in Feature Selection by SONG Zhi-chao, KANG Jian, SUN Guang-lu, HE Yong-jun

“…Three representative linear or nonlinear measures，linear correlation coefficient，symmetrical uncertainty，and mutual information are selected. By combining them with the fast correlation-based filter ( FCBF) feature selection method，we make the comparison of selected feature subset from 8 gene microarray and image datasets. …”
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Application of Chromosome Microarray in Diagnosis of Amniotic Fluid in Older Pregnant Women by Guangting Lu, Weiwu Liu, Chao Ou

“…Background: To improve the detection rate of chromosome abnormalities in fetuses and to reduce the birth defects rate in elderly pregnant women using chromosome karyotype analysis combined with the chromosome microarray analysis (CMA) technique. …”
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Consensus PCR and Microarray for Diagnosis of the Genus Staphylococcus, Species, and Methicillin Resistance by S. Hamels, J.-L. Gala, S. Dufour, P. Vannuffel, N. Zammatteo, J. Remacle

“…Products were then identified on a glass array. The microarray contained five selective DNA capture probes for the simultaneous and differential identification of the five most clinically relevant staphylococcal species (S. aureus, S. epidermidis, S. haemolyticus, S. hominis, and S. saprophyticus), while a consensus capture probe could detect all femAsequences, allowing the identification of the genus Staphylococcus. …”
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