Spearman correlation coefficient results for classification algorithms (predicted probabilities) and regression algorithms (predicted IC50 values). by Sofia P. Miranda (11418930)

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SUNRISE hybrids obtained from 36 maintainer lines (females, rows) crossed to 36 restorer lines (males, columns). by Brigitte Mangin (336874)

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”

Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

“…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”

Most informative genes for predicting cell-line responses for Gefitinib. by Sofia P. Miranda (11418930)

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DE algorithm flow. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

Test results of different algorithms. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

Classification results for all subsampling scenarios and algorithms for Gefitinib. by Sofia P. Miranda (11418930)

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Regression results for all combinations of subsampling scenarios and algorithms for Gefitinib. by Sofia P. Miranda (11418930)

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Average RMSE rank for all combinations of subsampling scenarios and algorithms. by Sofia P. Miranda (11418930)

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Predicting patient drug response from cell-line methylation profiles for Temozolomide (n = 85). by Sofia P. Miranda (11418930)

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Line graph for determining K value based on elbow method. by Bing Li (43671)

Summary of AUC values across all combinations of subsampling scenario and algorithm. by Sofia P. Miranda (11418930)

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Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction by E. Tejera (7470644)

“…<div><p>The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. …”

Example medication days generated for patients P4 and P6, medication times (red dots), patient’s state—TRS score (blue line) with bounds for the states (red lines) and optimal stat... by Tomasz Gutowski (17200042)

Descriptions of the algorithms we tested and hyperparameters that we evaluated via nested cross validation. by Sofia P. Miranda (11418930)

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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

Minimum, mean and maximum RMSE value for each drug and algorithm combination, averaged across all subsampling scenarios. by Sofia P. Miranda (11418930)

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Minimum, mean and maximum AUC value for each combination of drug and algorithm, averaged across all subsampling scenarios. by Sofia P. Miranda (11418930)

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