Patent ID: 11923047
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A system for training a machine learning classifier to determine interaction sites between biosequences, the system comprising:
at least one processor; and
a non-transitory computer-readable memory having stored thereon program instructions executable by the at least one processor for:
obtaining, by the processor, a dataset of contact data for a plurality of biomolecule pairs from a database, each one of the biomolecule pairs composed of a biomolecule ri from a first biosequence and a biomolecule rj from a second biosequence, the contact data corresponding to a separation distance between ri and rj;
transforming, by the processor, the contact data to create statistical weights, the transforming including:
accounting, by the processor, a frequency of occurrence of every biomolecule pair (ri, rj) in the contact data to obtain a frequency table;
determining, by the processor, a statistical weight for each frequency of occurrence as a function of a deviation of the frequency of occurrence from an expected frequency to obtain a statistical residual frequency matrix, wherein the statistical residual frequency matrix is considered as a statistical residual vector space such that each row is a vector having coordinates that are statistical residuals of a given biomolecule interaction with other biomolecules corresponding to a column of the statistical residual vector space;
decomposing, by the processor, the statistical residual vector space through principal component decomposition to obtain vector projections on principal components to reveal orthogonal interacting functionality through a strength of corresponding coordinates as reflected by a variance of principal components;
re-projecting, by the processor, the vector projections on the principal components to re-projected statistical residual vector spaces with new vector positions; and
deriving, by the processor, the statistical weights from the re-projected residual vector spaces for each biomolecule pair (ri, rj);
generating, by the processor, a k-dimensional feature vector for each biomolecule pair (ri, rj) and its nearest neighbors, for classifier training, using at least the statistical weights;
creating, by the processor, a training set comprising one or more of the feature vectors for each biomolecule pair (ri, rj);

training, by the processor, a classifier to classify a likelihood of an interaction site, using the training set;
wherein the classifier is operable to classify feature vectors constructed from an input biosequence pair to determine a likelihood that the input biosequence pair is interacting at an interaction site.