Methods for classifying subjects and analyzing the relation between subject classification and multiple features of the subjects are provided. Some embodiments of the disclosure provide processes that use clustering analysis as an unsupervised machine learning technique to classify subjects based on multiple features. Some embodiments may associate features of the subjects with a categorical variable on which subject groups are based. This association between the features and the categorical variable of interest (or subject groups) can be obtained by finding featured-based clusters that have similar members as the subject groups. Systems and computer program products implementing the methods are also disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, implemented at a computer system that includes one or more processors and system memory, for subject classification, the method comprising: (a) providing, on the computer system, a dataset comprising a plurality of feature vectors for a plurality of subjects in a pair of subject groups, wherein each feature vector comprises data for one or more features for one subject, each feature comprising a unit of measurement data; (b) splitting, by the computer system, the plurality of subjects into a pair of clusters by performing a cluster analysis for each feature, thereby obtaining a plurality of cluster pairs; (c) scoring, by the computer system, each feature's ability to discriminate between the pair of subject groups; (d) selecting, by the computer system, one or more features that score higher than other features; and (e) combining, by the computer system, the selected features to form a pair-wise discriminator between the pair of subject groups. 2. The method of claim 1, further comprising: (f) repeating (a)-(e) for additional pairs of subject groups, thereby forming pair-wise discriminators for multiple pairs of subject groups. 3. The method of claim 2, wherein the selected features comprise two or more features. 4. The method of claim 2, further comprising: (g) classifying which subject group a test subject belongs to by applying the pair-wise discriminators of one or more pairs of subject groups. 5. The method of claim 4, wherein (g) involves determining which one of the two groups in a pair of subject groups is a nearest neighbor of the test subject for each pair of the subject groups, and classifying the subject as a member of the subject group that is most frequently determined to be the nearest neighbor across all pairs of subject groups under consideration. 6. The method of claim 4, further comprising obtaining a p value of the classifying of (g), which p value indicates the probability of random selection yielding the same results as the classifying of (g). 7-8. (canceled) 9. The method of claim 1, wherein the scoring of (c) is based on membership in the subject groups and the clusters. 10. The method of claim 1, wherein the cluster analysis comprises a K-means clustering. 11-12. (canceled) 13. The method of claim 1, wherein (e) comprises splitting, by the computer system, at least some of the plurality of subjects into two multidimensional clusters by performing a K-means clustering for the combined features, and using information of the two multidimensional clusters to form the pair-wise discriminator. 14. The method of claim 13, further comprises cross-validating the pair-wise discriminator using data of subjects complementary to the subjects used to form the pair-wise discriminator. 15. The method of claim 1, wherein the clustering in (b) is performed on a training set comprising a randomly selected subset of the dataset, the method further comprising cross-validating the pair-wise discriminator using a test set complementary to the training set. 16. The method of claim 1, wherein (c) comprises calculating a score based on (i) the count of the more common subject group for each cluster, and (ii) the count of the more common cluster for each subject group. 17. The method of claim 1, wherein at least some feature vectors comprise behavioral measurements. 18. The method of claim 1, wherein the subjects are rodents, mammals, or humans. 19-21. (canceled) 22. The method of claim 1, wherein the subject groups are defined by genotype, drug treatment, diet, treatment with a hormone, normal mice versus humanized mouse models of a disease of interest, normal versus high fat diet. 23-27. (canceled) 28. The method of claim 22, wherein the hormone is corticosterone. 29. The method of claim 1, wherein the dataset comprises behavioral data collected from an animal cage that monitors animal behavioral. 30. (canceled) 31. The method of claim 1, wherein the subject groups are defined by disease states and the features are clinical features, the method further comprising identifying clinical features as disease susceptibility factors. 32-37. (canceled) 38. An unsupervised classification system for classifying subjects into two or more groups of interest, the system comprising: one or more memories configured to store feature vectors; and logic configured to: (a) provide a feature vector for each of a plurality of subjects in a pair of subject groups, wherein each feature vector comprises data for a plurality of features for one subject, each feature corresponding to a behavior, property or characteristic of the subject; (b) split the plurality of subjects into two clusters by performing a cluster analysis for each feature; (c) score each feature by comparing subjects in the two clusters for each feature and subjects in the pair of subject groups; (d) select one or more features that score higher than other features; and (e) combine the selected features to form a pair-wise discriminator between the pair of subject groups. 39-43. (canceled) 44. A method, implemented at a computer system that includes one or more processors and system memory, for entity classification, the method comprising: (a) providing, on the computer system, a dataset comprising a plurality of feature vectors for a plurality of entities in a pair of entity groups, wherein each feature vector comprises data for one or more features for one subject, each feature comprising a unit of measurement data; (b) splitting, by the computer system, the plurality of entities into a pair of clusters by performing a cluster analysis for each feature, thereby obtaining a plurality of cluster pairs; (c) scoring, by the computer system, each feature's ability to discriminate between the pair of entity groups; (d) selecting, by the computer system, two or more features that score higher than other features; and (e) combining, by the computer system, the selected features to form a pair-wise discriminator between the pair of entity groups. 45-46. (canceled)