Search alternatives:
practice optimization » practice utilization (Expand Search), reaction optimization (Expand Search), production optimization (Expand Search)
field optimization » lead optimization (Expand Search), guided optimization (Expand Search), linear optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based field » pulsed field (Expand Search)
practice optimization » practice utilization (Expand Search), reaction optimization (Expand Search), production optimization (Expand Search)
field optimization » lead optimization (Expand Search), guided optimization (Expand Search), linear optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based field » pulsed field (Expand Search)
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The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Parameter settings of the comparison algorithms.
Published 2024“…<div><p>Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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Datasets and their properties.
Published 2023“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”
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Parameter settings.
Published 2023“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”
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IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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IRBMO vs. feature selection algorithm boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Raw Data for the Thesis: "<i>Enhancing RNAi-Based Pest Control through Effective Target Gene Selection and Optimal dsRNA Design</i>"
Published 2025“…The results revealed moderate transferability (~50%) of highly effective targets from <i>T. castaneum</i>, which increased to approximately 80% when considering genes already validated in other leaf beetles. These findings are both conceptually important, in demonstrating partial but significant cross-species transferability of RNAi targets, and practically valuable for guiding the development of RNAi-based solutions against this important pest.…”
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An inflammation-associated ferroptosis signature can optimize the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma
Published 2023“…</p> <p>Methods: The train cohort from The Cancer Genome Atlas (TCGA) was clustered into three subtypes (C1, C2, and C3) based on the genes related to inflammation and ferroptosis. …”
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