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US 5,845,049 AGranted
Neural network system with N-gram term weighting method for molecular sequence classification and motif identification
AssigneeBoard of Regents, The University of Texas System
InventorCathy H. Wu
Life of this patent
Estimating the term…
Abstract
A method for rapid and sensitive protein family identification is disclosed. The new designs include an n-gram term weighting algorithm for extracting local motif patterns, an enhanced n-gram method for extracting residues of long-range correlation, and integrated neural networks for combining global and motif sequence information.
Claims5
A method for training a neural network to predict membership in a family of linear sequences comprising;
- providing a training set of full length member sequences, a training set of full length non-member sequences and a training set of family motif sequences;
- deriving term weights for n-gram terms by dividing the number of occurrences of each n-gram term in the motif set by the number of occurrences in the full length member set;
- deriving a set of global vectors for the full length member set and a set of global vectors for the full length non-member set using an n-gram method;
- deriving a set of motif vectors for the full length member set and a set of motif vectors for the fall length non-member set by multiplying each term in the global vector set by its term weighting factor;
- providing a neural network with multiple output units to represent one family;
- using the global vector of the member sequence set to train the positive full length output unit;
- using the motif vector of the member set to train the positive motif output unit;
- using the global vector of the non-member set to train the fall length negative output unit;
- and
- using the motif vector of the non-member set to train the negative motif output unit of said neural network.
7 more elements
A method of obtaining term weighted n-grams for use in training a neural network comprising:
- providing a training set of full length member sequences and a training set of family motif sequences;
- and
- deriving term weights by applying the formula
- W.sub.k =.SIGMA.M.sub.ik /.SIGMA.F.sub.ik
- to said motif set and said full length set, where W.sub.k is the weight factor for the k-th n-gram term in the input vector, and F.sub.ik and M.sub.ik are total counts of the k-th n-gram term in the i-th sequence of the full-length sequence set and motif set, respectively.
2 more elements
Dependencies are inferred from the claim text.