Gefitinib regression results across four metrics. by Sofia P. Miranda (11418930)

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Graphs for Temozolomide regression analysis. by Sofia P. Miranda (11418930)

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Graphs for Doxorubicin regression analysis. by Sofia P. Miranda (11418930)

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Graphs for Doxorubicin classification analysis. by Sofia P. Miranda (11418930)

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Graphs for Gemcitabine classification analysis. by Sofia P. Miranda (11418930)

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Graphs for Etoposide regression analysis. by Sofia P. Miranda (11418930)

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Graphs for Docetaxel regression analysis. by Sofia P. Miranda (11418930)

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Histograms for each drug based on drug response (IC₅₀ values) for the GDSC dataset. by Sofia P. Miranda (11418930)

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Graphs for Cisplatin classification analysis. by Sofia P. Miranda (11418930)

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Combination performance of each single caller and combination strategy in detecting DELs and INSs using the ‘neighboring SV method’ and ‘directly merge method’. by De-Min Duan (20685513)

Inspection of Line Defects in Transition Metal Dichalcogenides Using a Microscopic Hyperspectral Imaging Technique by Xiangmin Hu (1612399)

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

“…Benchmark datasets of aqueous solubility (log <i>S</i>), lipophilicity (log <i>D</i>), and membrane permeability measured using the Caco-2 cell line (log <i>P</i>_app) were constructed by merging and calibrating experimental data from diverse articles and databases. …”

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

“…Benchmark datasets of aqueous solubility (log <i>S</i>), lipophilicity (log <i>D</i>), and membrane permeability measured using the Caco-2 cell line (log <i>P</i>_app) were constructed by merging and calibrating experimental data from diverse articles and databases. …”

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

“…Benchmark datasets of aqueous solubility (log <i>S</i>), lipophilicity (log <i>D</i>), and membrane permeability measured using the Caco-2 cell line (log <i>P</i>_app) were constructed by merging and calibrating experimental data from diverse articles and databases. …”

Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines by Ophélie Thomas - - Chemin (19451533)

“…All of the FCs were then classified using machine learning tools with a statistical approach based on a fuzzy logic algorithm, trained to discriminate between nonmalignant and cancerous cells (training base, up to 120 cells/cell line). …”

Generation of in silico dataset and calibration pipeline optimization. by Janice Yang (5747246)

Algorithm 1: Cap inhibitory densities to number of neurons. by Dimitri Rodarie (10148838)

Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

“…The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. …”

Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

“…The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. …”

Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

“…The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. …”