Results 281 to 290 of about 191,482 (312)
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2013 IEEE Congress on Evolutionary Computation, 2013
Evolutionary algorithms (EAs) have been successfully used in many studies for evolving both the structure and parameters of biological networks including gene regulatory networks that demonstrate different functionalities. However, most of these studies have used only mutation as the genetic operator in the evolutionary framework, perhaps due to the ...
Dhammika S. Hettiarachchi +2 more
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Evolutionary algorithms (EAs) have been successfully used in many studies for evolving both the structure and parameters of biological networks including gene regulatory networks that demonstrate different functionalities. However, most of these studies have used only mutation as the genetic operator in the evolutionary framework, perhaps due to the ...
Dhammika S. Hettiarachchi +2 more
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Cancer Research, 2015
Abstract Introduction RNA sequencing (RNA-seq) has rapidly become one of the main methods to study transcriptome. It has become an important and popular issue to identify biomarkers based on their differential expression patterns in NGS data. Currently, more than 10 different algorithms can be used to detect differentially
Chin-Ting Wu +4 more
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Abstract Introduction RNA sequencing (RNA-seq) has rapidly become one of the main methods to study transcriptome. It has become an important and popular issue to identify biomarkers based on their differential expression patterns in NGS data. Currently, more than 10 different algorithms can be used to detect differentially
Chin-Ting Wu +4 more
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Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS 2025)
Cancer driver genes (CDGs) play a central role in tumorigenesis and represent important targets for diagnosis and therapy. In this study, we evaluate the impact of hyperparameter optimization on the predictive performance of traditional machine learning algorithms using multi-omics data. We perform systematic searches across different configurations to
Ana Laura Schardosim +3 more
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Cancer driver genes (CDGs) play a central role in tumorigenesis and represent important targets for diagnosis and therapy. In this study, we evaluate the impact of hyperparameter optimization on the predictive performance of traditional machine learning algorithms using multi-omics data. We perform systematic searches across different configurations to
Ana Laura Schardosim +3 more
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2023
Abstract In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based ...
Wei Zhang +7 more
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Abstract In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based ...
Wei Zhang +7 more
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2012 International Conference on Advances in Computing and Communications, 2012
Reliable predictive model build using semi supervised learning utilising classification algorithm has evolved rapidly in successful cancer treatment. In order to optimise the data integration problem, such as hypergraph based learning to integrate microarray gene expressions and protein interactions for predicting cancer outcome, novice optimization ...
Seena Mary Augusty, Sminu Izudheen
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Reliable predictive model build using semi supervised learning utilising classification algorithm has evolved rapidly in successful cancer treatment. In order to optimise the data integration problem, such as hypergraph based learning to integrate microarray gene expressions and protein interactions for predicting cancer outcome, novice optimization ...
Seena Mary Augusty, Sminu Izudheen
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The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is ...
Jahangheer S. Shaik, Mohammed Yeasin
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This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is ...
Jahangheer S. Shaik, Mohammed Yeasin
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International Journal of Data Mining and Bioinformatics, 2016
Class retrieval in gene expression microarray data analysis is highly challenging task. Because of high class imbalance, highly dimensional feature space and small number of samples most of the algorithms fail to capture real complex structures in data 'golden standard'.
Milan Vukicevic +3 more
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Class retrieval in gene expression microarray data analysis is highly challenging task. Because of high class imbalance, highly dimensional feature space and small number of samples most of the algorithms fail to capture real complex structures in data 'golden standard'.
Milan Vukicevic +3 more
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2021 17th International Conference on Mobility, Sensing and Networking (MSN), 2021
Shunbao Li +2 more
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Shunbao Li +2 more
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Gene expression programming algorithm based on multi-threading evaluator
Journal of Computer Applications, 2013Sheng-qiao NI +3 more
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