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

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

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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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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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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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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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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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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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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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