The United States of America, As Represented by the Secretary, Department of Health and Human Services

Agrovision Ab

Leland Stanford Junior University

Siemens Elema Ab

Pavilion Technologies, Inc.

Carsten Peterson

Javed Khan

Paul S Meltzer

Markus Ringner

Markus Ringnér

Peter Egelberg

Rickard Oste

Mattias Ohlsson

Stephen William Piche

Eric J Hartman

Richard Blankenbecler

Lars Edenbrandt

Patrik Soderlund

Lennart Sjostedt

Ralf Rittner

Eivor Svensson

Olle Mo Slashednsson

Bo Heden

Olle Pahlm

A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.

Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states

A method of diagnosing a disease that includes obtaining experimental data on gene selections. The gene selection functions to characterize a cancer when the expression of that gene selection is compared to the identical selection from a noncancerous cell or a different type of cancer cell. The invention also includes a method of targeting at least one product of a gene that includes administration of a therapeutic agent. The invention also includes the use of a gene selection for diagnosing a cancer.

Sets of probes and primers for the diagnosis of select cancers

Selections of genes and methods of using the same for diagnosis and for targeting the therapy of select cancers

This invention provides a method for protein structure alignment. More particularly, the present invention provides a method for identification, classification and prediction of protein structures. The present invention involves two key ingredients. First, an energy or cost function formulation of the problem simultaneously in terms of binary (Potts) assignment variables and real-valued atomic coordinates. Second, a minimization of the energy or cost function by an iterative method, where in each iteration (1) a mean field method is employed for the assignment variables and (2) exact rotation and/or translation of atomic coordinates is performed, weighted with the corresponding assignment variables.

Method for protein structure alignment

An optimization system is provided utilizing a Bayesian neural network calculation of a derivative wherein an output is optimized with respect to an input utilizing a stochastical method that averages over many regression models. This is done such that constraints from first principal models are incorporated in terms of prior art distributions.

Bayesian neural networks for optimization and control

In automatic evaluation of cereal kernels or like granular products handled in bulk, the kernels are conveyed on a vibrating conveyor belt (15). Owing to the vibrations, the kernels are spread and settled in grooves (14) in the belt so as to be oriented in essentially the same direction. A video camera (40) produces digital images of all the kernels on the belt. The kernels are identified in the images, and for each kernel input signals are produced and then sent to a neural network based on picture element values for the picture elements representing each kernel. A neural network then determines which of a plurality of predetermined classes that each kernel belongs.

Method and device for automatic evaluation of cereal grains and other

Methods and devices for automatic assessment of corn

An analysis apparatus such as an ECG apparatus has a control unit which includes an artificial neural network for discovering signal-recording electrodes which are erroneously attached to a patient. At least one artificial neural network is taught by being fed measurement signals from both correctly recorded measurements and from erroneously recorded measurements. The artificial neural network is then able to identify erroneous attachments with great accuracy from recorded measurement signals.

Medical apparatus for analyzing electrical signals from a patient

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Lund, Sweden

Carsten Peterson