Method and apparatus for multivariable analysis of biological measurements

Process for analyzing multivariable data sets including a plurality of measured variables, said process comprising:

• providing a neural network;
• applying signals representative of variables contained in said data sets to said neural network and processing said data in a sequence of iterations, starting at random guess for said neural network matrix of weight parameters, and repeatedly averaging until said matrix of weight parameters converge;
• and
• generating from said matrix of weight parameters an evaluation, indicating relationship between said variables.

Method comprising the steps of

• obtaining a data set comprising a plurality of input/output multivariable vectors representative of a biological process involving many interacting multifunctional components in at least one pathway,
• establishing a neural network comprised of single layer network operators,
• applying the data set to the neural network,
• training the neural network by a training algorithm to implement a transformation by a matrix of weights starting with a first random guess and iteratively modifying according to a learning rule until the weight matrix comes to convergence and produces a solution, then, using successive random guesses, repeating the training for each random guess until the weight matrix comes to a convergence solution for each random guess whereby a plurality of weight matrix solutions are obtained,
• averaging the weight matrix solutions to obtain an averaged weight matrix,
• determining from said matrix of weights the hierarchical structure between said components.

Method comprising the steps of

• obtaining a data set comprising a plurality of input multivariable vectors representative of a biological system involving many interacting multifunctional components in at least one pathway, that define a complex biological state (or condition),
• determining a corresponding output vector for each input vector that defines classes for the input vectors,
• establishing a neural network comprised of single layer network operators,
• applying the data set to the neural network,
• training the neural network by a training algorithm to implement a transformation by a matrix of weights starting with a first random guess and iteratively modifying according to a learning rule until the weight matrix comes to convergence and produces a solution, then, using successive random guesses, repeating the training for each random guess until the weight matrix comes to a convergence solution for each random guess whereby a plurality of weight matrix solutions are obtained,
• averaging the weight matrix solutions to obtain an averaged weight matrix,
• modifying the neural network to set its transformation of a matrix of weights to the averaged weight matrix, whereby the modified neural network can sort newly presented input vectors into the classes that were defined by the output set of vectors in the training.