Top models based on analysis of training and testing set samples using a suite of ML algorithms with sequential feature exclusion based on feature predictive importance. by Cathal Meehan (14129680)

Filtering process for the removal of somatic SNV/indel noise generated by treatment with the HyperPlus kit. by Norio Tanaka (4371805)

Outline of computational workflow for selecting stock barcodes. by Sylvain Blois (663852)

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis by Lichuang Huang (10679028)

“…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …”

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis by Lichuang Huang (10679028)

“…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …”

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis by Lichuang Huang (10679028)

“…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …”

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC–MS/MS Data Analysis by Lichuang Huang (10679028)

“…This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify “features” (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. …”

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start by Yifan Wang (380120)

“…More sustainable chemical processes require the selection of suitable molecules, which can be supported by computer-aided molecular design (CAMD). …”

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start by Yifan Wang (380120)

“…More sustainable chemical processes require the selection of suitable molecules, which can be supported by computer-aided molecular design (CAMD). …”

CMRSegTools workspace. by William A. Romero R. (13783210)

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

Data Sheet 7_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 2_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 9_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.xlsx by Xuehui Fan (10762662)

Data Sheet 5_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 8_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 6_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 3_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)

Data Sheet 4_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.docx by Xuehui Fan (10762662)