Table4_Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.docx by Ling-Fang Ye (14578280)

“…We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. …”

Image1_Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.TIF by Ling-Fang Ye (14578280)

“…We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. …”

Table3_Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.docx by Ling-Fang Ye (14578280)

“…We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. …”

Table5_Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.docx by Ling-Fang Ye (14578280)

“…We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. …”

Ranking of features selected by scatter search and fast correlation base filter algorithms. by Pedro Manuel Martínez-García (10005013)

The used test functions in the evaluation. by Xuan Chen (158617)

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Load curves of residential and IT sector loads with CS algorithm based DSM. by Manoj Kumar V. (18196645)

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Algorithm flow of the CNN + LSTM model. by Zhongzhong Guo (12133402)

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Performances of ImpAO, AO, SMA, MFO and ABC algorithms for minimization of different error-based cost functions. by Davut Izci (15927452)

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If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

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An Algorithmic Approach Based on Data Trees and Genetic Algorithms to Understanding Charged and Neutral Metal Nanocluster Growth by Chelsea M. Mueller (8351745)

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Multi-objective fuzzy optimization results based on PSCACO algorithm. by Tianrui Zhang (2294542)

Constructing the dynamical law. by Robert Marsland (8616483)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Greedy optimization algorithm. by Alexis Bénichou (19832361)

Boxplots obtained by testing rat575 for 20 times by each algorithm. by Jin Zhang (53297)

Boxplots obtained by testing ch150 for 50 times by each algorithm. by Jin Zhang (53297)

Table of relationships between algorithms and five strategies for two experimental groups. by Jin Zhang (53297)

Boxplots obtained by testing fl1400 for 10 times by each algorithm. by Jin Zhang (53297)