US20030172043A1
Expired / Lapsed
US
⏱️ 0.0 years remaining
Methods of identifying patterns in biological systems and uses thereof
Assignee: BIOWULF TECHNOLOGIES LLC
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Filing: 2002-01-24
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Granted: —
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Expires: 2022-01-24
Abstract
Abstract The methods, systems and devices of the present invention comprise use of Support Vector Machines and RFE (Recursive Feature Elimination) for the identification of patterns that are useful for medical diagnosis, prognosis and treatment. SVM-RFE can be used with varied data sets.
Inventors
- 👤 Isabelle Guyon
- 👤 Jason Weston
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 20:32
CPC Classifications
G; G06; G06N; G06N20/00; G06F; G06F18/00; G06F18/20; G06F18/21; G06F18/211; G06F18/2115; G06F18/24; G06F18/241; G06F18/2411; G06N20/10; G16; G16B; G16B25/00; G16B25/10; G16B40/00; G16B40/20; G16B40/30; Y; Y02; Y02A; Y02A90/00; Y02A90/10
Claims Analysis
Total Claims: 11
Claim 1
Independent Claim
138 words
1 . A computer-implemented method for identifying patterns in data, the method comprising: (a) inputting into a classifier a training set having known outcomes, the classifier comprising a decision function having a plurality of weights, each having a weight value, wherein the training set comprises features corresponding to the data and wherein each feature has a corresponding weight; (b) optimizing the plurality of weights so that classifier error is minimized; (c) computing ranking criteria using the optimized plurality of weights; (d) eliminating at least one feature corresponding to the smallest ranking criterion; (e) repeating steps (a) through (d) for a plurality of iterations until a subset of features of pre-determined size remains; and (f) inputting into the classifier a live set of data wherein the features within the live set are selected according to the subset of features.
Claim 2
Dependent Claim
138 words
1 . A computer-implemented method for identifying patterns in data, the method comprising: (a) inputting into a classifier a training set having known outcomes, the classifier comprising a decision function having a plurality of weights, each having a weight value, wherein the training set comprises features corresponding to the data and wherein each feature has a corresponding weight; (b) optimizing the plurality of weights so that classifier error is minimized; (c) computing ranking criteria using the optimized plurality of weights; (d) eliminating at least one feature corresponding to the smallest ranking criterion; (e) repeating steps (a) through (d) for a plurality of iterations until a subset of features of pre-determined size remains; and (f) inputting into the classifier a live set of data wherein the features within the live set are selected according to the subset of features.
Claim 3
Dependent Claim
16 words
2 . The method of claim 1 , wherein the classifier is a support vector machine.
Claim 4
Dependent Claim
18 words
3 . The method of claim 1 , wherein the classifier is a soft margin support vector machine.
Claim 5
Dependent Claim
27 words
4 . The method of claim 1 , wherein the ranking criterion corresponding to a feature is calculated by squaring the optimized weight for the corresponding feature.
Claim 6
Dependent Claim
16 words
5 . The method of claim 1 , wherein the decision function is a quadratic function.
Claim 7
Dependent Claim
32 words
6 . The method of claim 1 , wherein step (d) comprises eliminating a plurality of features corresponding to the smallest ranking criteria in a single iteration of steps (a) through (d).
Claim 8
Dependent Claim
44 words
7 . The method of claim 1 , wherein step (d) comprises eliminating a plurality of features corresponding to the smallest ranking criteria in at least the first iteration of steps (a) through (d) and in later iterations, eliminating one feature for each iteration.
Claim 9
Dependent Claim
39 words
8 . The method of claim 1 , wherein step (d) comprises eliminating a plurality of features corresponding to the smallest ranking criteria so that the number of features is reduced by a factor of two for each iteration.
Claim 10
Dependent Claim
25 words
9 . The method of claim 1 , wherein the training set and the live set each comprise gene expression data obtained from DNA micro-arrays.
Claim 11
Dependent Claim
25 words
10 . The method of claim 1 , further comprising pre-processing the training set and the live set so that the features are comparably scaled.
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