COMET: A Machine-Learning Framework Integrating Ligand-Based and Target-Based Algorithms for Elucidating Drug Targets by Haojie Wang (3072141)

“…We have developed a computational target-fishing method, termed COMET, which integrates ligand-based similarity scores with target-based binding scores into a random forest algorithm for target ranking. …”

COMET: A Machine-Learning Framework Integrating Ligand-Based and Target-Based Algorithms for Elucidating Drug Targets by Haojie Wang (3072141)

“…We have developed a computational target-fishing method, termed COMET, which integrates ligand-based similarity scores with target-based binding scores into a random forest algorithm for target ranking. …”

Distribution of articles by augmentation method. by Rodrigo Gutiérrez Benítez (19746443)

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AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…By integrating a heuristic search mechanism, the algorithm can quickly converge to potential high utility patterns and effectively reduce redundant computational processes. …”

K-means clustering method based on PCA algorithm. by Elmira Bagheri (20553110)

Accuracy of the proposed method using SVM combined with the uniform window. by Abeer Aljohani (18497914)

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Image Clarity Processing Flow. by Cheng-jie Chen (22272090)

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Computation time as a function of the sample size on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

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Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

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Examples of ground-truth graph structures with (<i>p</i>, <i>n</i>_≠0) = (10, 10). by Tomokaze Shiratori (9635271)

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Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

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Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Computation time as a function of the number of variables on the chain graph dataset. by Tomokaze Shiratori (9635271)

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Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

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F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

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