Screening of candidate genes via protein–protein interaction (PPI) network analysis and subsequent correlation analysis. A. A PPI network was constructed to investigate the interac... حسب Xiaoyu Shen (4715430)

Oryza australiensis species-specific gene and protein candidates حسب Sabrina Morrison (19846869)

"…</p><p dir="ltr">The protein sequence data for both <i>O. australiensis</i> and <i>O. sativa</i> (Osativa323v7 protein file Phytozome). were filtered for the longest isomer and then analysed for orthologous and unique protein clusters within the O. australiensis genome using OrthoVenn3 (parameters: OrthoFinder algorithm, E-value: 1e-2, Inflation value:1.50) (Sun et al., 2023, Emms and Kelly, 2019). …"

Data Sheet 1_Machine learning potential predictor of idiopathic pulmonary fibrosis.docx حسب Chenchun Ding (19231043)

Pseudo Code of RBMO. حسب Chenyi Zhu (9383370)

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P-value on CEC-2017(Dim = 30). حسب Chenyi Zhu (9383370)

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Memory storage behavior. حسب Chenyi Zhu (9383370)

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Elite search behavior. حسب Chenyi Zhu (9383370)

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Description of the datasets. حسب Chenyi Zhu (9383370)

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Collaborative hunting behavior. حسب Chenyi Zhu (9383370)

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Friedman average rank sum test results. حسب Chenyi Zhu (9383370)

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IRBMO vs. variant comparison adaptation data. حسب Chenyi Zhu (9383370)

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Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation حسب Zhen Chen (129176)

"…PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. …"

Table 2_Harnessing serum VOCs and machine learning for the early detection of MAFLD.xlsx حسب Xin Li (51274)

"…Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS). Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. …"

Image 2_Harnessing serum VOCs and machine learning for the early detection of MAFLD.jpeg حسب Xin Li (51274)

"…Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS). Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. …"

Image 1_Harnessing serum VOCs and machine learning for the early detection of MAFLD.jpeg حسب Xin Li (51274)

"…Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS). Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. …"

Table 1_Harnessing serum VOCs and machine learning for the early detection of MAFLD.xlsx حسب Xin Li (51274)

"…Volatile organic compounds were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS). Machine learning algorithms, including random forest, were applied to construct diagnostic models and identify key discriminatory metabolites. …"

The GA iteration result. حسب Jiaqing Huang (5018492)

"…The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. …"

GA crossover and mutation process. حسب Jiaqing Huang (5018492)

"…The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. …"

The framework for the proposed model and GA-FNN. حسب Jiaqing Huang (5018492)

"…The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. …"

The structure for the FNN. حسب Jiaqing Huang (5018492)

"…The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. …"