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Matrix Factorization-Based Data Fusion for Drug-Induced Liver Injury Prediction
Transcript of Matrix Factorization-Based Data Fusion for Drug-Induced Liver Injury Prediction
Data Fusion for Drug-Induced Liver
Marinka Zitnik and Blaz Zupan
University of Ljubljana, Slovenia
Data Fusion Configuration
14 types of objects
29 data sources
134,113,951 non-zero data entries
CUR matrix decomposition
Compound batch correction
Mahoney and Drineas, 2009.
U = C A R
Gene Ontology Annotations
Metadata on Arrays
AUC of 0.82 > 0.78 Standard ML
AUC of 0.82 > 0.66 CAMDA 2012
In vitro single
In vivo single
In vivo repeated
In vitro single
RBC, Neutrophil, Eosinophil,
Basophil, Monocyte, Lymphocyte
Terminal body weight
Relative liver weight
ALP, Cl, TC, Ca, TG, IP, PL, TP, TBIL, RALB, DBIL, A/G GLC, AST (GOT), BUN, ALT (GPT), CRE, LDH, Na, gamma-GTP, K
Input Data + Circumstantial Evidence = Gain in Accuracy
Replace animal studies with in vitro assays (AUC = 0.799)
Predict liver injury in humans from animal data (AUC = 0.811)
Blaz Zupan, PhD