forbes
Uploaded by
3 SLIDES
163 VUES
30LIKES

Predictive Analysis of Drug Response Using NSC and LDA Techniques in LCMS Data

DESCRIPTION

This study explores predictive analysis methods, combining NSC and LDA to classify drug responses in a training and testing set using LCMS raw spectra. Focused on Enbrel and Anakinra, we employed five-fold cross-validation to assess predictors among 972 unique features. Key methods include peak finding, feature extraction, and hierarchical clustering, enhanced by ROC and heatmap visualizations. Our findings reveal predictors of clinical response (CR) and pharmacological response (PR), enabling insights into personalized treatment strategies.

1 / 3

Télécharger la présentation

Predictive Analysis of Drug Response Using NSC and LDA Techniques in LCMS Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MASS-Conductor Prediction analysis (NSC, LDA, ROC) Cluster analysis Predictor discovery in training set Predictor confirmation in testing set Pattern analysis in all set 2 3 4 1 Training set (5 CR, 5 PR of Enbrel) Testing set (3 Anakinra CR) All set (5 Enbrel CR, 5 Enbrel PR, 3 Anakinra CR) LCMS raw spectra Peak finding peak alignment feature extraction NSC Classifier training LDA Class prediction 2d hierarchical clustering heatmap plotting Predictors of 4 ~ 972 features Normalization FIVE-fold Cross-validation Predict Anakinra response Predictors of 972 or 32 features 106246 unique features Classify CR PR of Enbrel FIGURE 1

  2. FIGURE 2 A Feature number Classification error% Values of thresholds Enbrel PR CR 972 feature panel 32 feature panel B Anakinra CR

  3. Enbrel PR CR 972 feature panel 27 feature panel B Enbrel Anakinra Anakinra CR PR CR CR Predictive probability

More Related