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3037results about "Biological models" patented technology

Server-originated differential caching

The invention provides a method and system for sending relatively identical web pages, when requested by subsequent users, with substantial reduction of bandwidth. The server determines a “template web page” corresponding to the actual information on the web page, and having a set of insertion points, at which changed data can be inserted by the client. The server sends a JavaScript program corresponding to the template web page, which makes reference to the template web page and the changed data, along with sending the actual changed data itself. A first user requesting the web page receives the entire web page, while a second user requesting the web page (or the first user re-requesting the web page at a later time) receives the template information plus only the changed data. The server re-determines the template web page from time to time, such as when a ratio of changed data to template web page data exceeds a selected threshold. The server identifies the particular template web page to the client using a unique identifier (an “E-tag”) for the particular data sent in response to the request. Since the E-tag refers to the template, not the underlying web page, when the standard client makes its conditional request for the web page “if not changed”, the server responds that the web page is “not changed” even if it really is, but embeds the changed data in a cookie it sends to the client with the server response to the client request.
Owner:DIGITAL RIVER INC

Binary prediction tree modeling with many predictors and its uses in clinical and genomic applications

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.
Owner:DUKE UNIV

Meta-content analysis and annotation of email and other electronic documents

Meta-content analysis and annotation upon the body of email documents, and other electronic documents, and to create a displayable index of these instances of meta-content, which is sorted and annotated by type are provided. In addition, the electronic document is enhanced by providing links for the semantic foci to external documents containing related information. An electronic document adapted for delivery to one or more recipients, the electronic document including a header and a body, is processed by:performing meta-content extraction of semantic foci within said header and said body, the semantic foci comprising a plurality of type of information including one or more of email addresses, URLs, dates, currency values, organization names, names of people, names of places, and phone numbers;creating a meta-content index the document based upon said extracted semantic foci;arranging the meta-index according to said plurality of types;combining said meta-content index with said header and said body to provide an enhanced document; andsending said enhanced document to said one or more recipients via a communication network.The process includes converting the electronic mail document to a markup language format, and wherein said meta-content index comprises one or more objects expressed in said markup language adapted for presentation with body in said enhanced document.
Owner:SAP AMERICA
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