A processor-implemented method, computer program product and system are provided for predicting drug-drug interactions based on clinical side effects. The method includes constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs. The method also includes constructing side effect features for each of the drugs from side effects associated with the drugs. The method further includes building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the drugs. The method additionally includes for each of the side effects, building a two-by-two table using the side effect features, and performing a Fisher's exact test using the two-by-two table to determine whether a given one of side effects is differentially shown between positive predicted drug-drug interactions and negative predicted drug-drug interactions.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A processor-implemented method for predicting drug-drug interactions based on clinical side effects, comprising: constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs; constructing side effect features for each of the plurality of drugs from side effects associated with the plurality of drugs; building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the plurality of drugs; and for each of the side effects, building a two-by-two table using the side effect features, and performing a Fisher's exact test using the two-by-two table to determine whether a given one of side effects is differentially shown between positive predicted drug-drug interactions and negative predicted drug-drug interactions. 2. The method of claim 1, wherein the multiple data sources comprise at least one of a drug bank, drug label information, an adverse event reporting system, and text mining from scientific documents. 3. The method of claim 1, wherein the side effects, from which the side effect features are constructed, are determined from at least one of clinical trials, drug development information, empirical information, drug labels, and an adverse event reporting system. 4. The method of claim 1, wherein each of the side effects is designated as a respective one of the side effect features, and each of the drug pairs is represented as a vector of side effect features having either a first value, a second value, or a third value respectively indicating that the drugs forming a particular drug pair have zero side effects, only one of the drugs has side effects, and both of the drugs have side effects. 5. The method of claim 1, wherein the side effect features used for building the drug-drug interactions classifier comprise at least one of drug clinical side effect keywords, and drug properties. 6. The method of claim 5, wherein the drug properties comprise at least one of chemical structures and protein targets. 7. The method of claim 1, wherein building the drug-drug interactions classifier comprises using a label propagation method. 8. The method of claim 7, wherein label propagation method propagates drug-drug interactions between different ones of the plurality of drugs on a basis that if a first drug has an interaction with a second drug and the second drug is similar to a third drug, then the first drug will be considered to have also the interaction with the third drug. 9. The method of claim 7, wherein the label propagation method is performed using a similarity graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, each of the plurality of nodes being associated with a respective drug from the plurality of drugs and including drug-drug interactions information for the drug pairs that include the respective drug, and each of the plurality of edges for propagating the drug-drug interactions information between the plurality of nodes. 10. The method of claim 9, wherein the similarity graph is constructed to have, from among the plurality of nodes, a central node, a plurality of directly neighboring nodes with respect to the central node, and a plurality of indirectly neighboring nodes with respect to the central node, and wherein the label propagation′ method comprises iteratively propagating the drug-drug interactions information outwardly from the central node to the plurality of directly neighboring nodes and thereafter to the plurality of indirectly neighboring nodes until convergence of a classifying objective function. 11. The method of claim 1, wherein the drug-drug interactions classifier uses a classifying objective function having a smoothness constraint and a fitting constraint to render the predicted adverse drug-drug interactions. 12. A computer program product for predicting drug-drug interactions based on clinical side effects, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising: constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs; constructing side effect features for each of the plurality of drugs from side effects associated with the plurality of drugs; building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the plurality of drugs; and for each of the side effects, building a two-by-two table using the side effect features, and performing a Fisher's exact test using the two-by-two table to determine whether a given one of side effects is differentially shown between positive predicted drug-drug interactions and negative predicted drug-drug interactions. 13. The computer program product of claim 12, wherein each of the side effects is designated as a respective one of the side effect features, and each of the drug pairs is represented as a vector of side effect features having either a first value, a second value, or a third value respectively indicating that the drugs forming a particular drug pair have zero side effects, only one of the drugs has side effects, and both of the drugs have side effects. 14. The computer program product of claim 12, wherein the side effect features used for building the drug-drug interactions classifier comprise at least one of drug clinical side effect keywords, and drug properties. 15. The computer program product of claim 12, wherein building the drug-drug interactions classifier comprises using a label propagation method. 16. The computer program product of claim 15, wherein label propagation method propagates drug-drug interactions between different ones of the plurality of drugs on a basis that if a first drug has an interaction with a second drug and the second drug is similar to a third drug, then the first drug will be considered to have also the interaction with the third drug. 17. The computer program product of claim 15, wherein the label propagation method is performed using a similarity graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, each of the plurality of nodes being associated with a respective drug from the plurality of drugs and including drug-drug interactions information for the drug pairs that include the respective drug, and each of the plurality of edges for propagating the drug-drug interactions information between the plurality of nodes. 18. The computer program product of claim 17, wherein the similarity graph is constructed to have, from among the plurality of nodes, a central node, a plurality of directly neighboring nodes with respect to the central node, and a plurality of indirectly neighboring nodes with respect to the central node, and wherein the label propagation method comprises iteratively propagating the drug-drug interactions information outwardly from the central node to the plurality of directly neighboring nodes and thereafter to the plurality of indirectly neighboring nodes until convergence of a classifying objective function. 19. The computer program product of claim 12, wherein the drug-drug interactions classifier uses a classifying objective function having a smoothness constraint and a fitting constraint to render the predicted adverse drug-drug interactions. 20. A system for predicting drug-drug interactions based on clinical side effects, comprising: a drug-drug interactions training dataset constructor for constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs; a side effect features constructor for constructing side effect features for each of the plurality of drugs from side effects associated with the plurality of drugs; a drug-drug interactions classifier builder for building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the plurality of drugs; a two-by-two table builder for building, for each of the side effects, a two-by-two table using the side effect features; and a Fisher's exact tester having a processor for performing a Fisher's exact test using the two-by-two table to determine whether a given one of side effects is differentially shown between positive predicted drug-drug interactions and negative predicted drug-drug interactions.