Method and apparatus for multivariable analysis of biological measurements

• US 20030158829A1
• Filed: 11/26/2002
• Published: 08/21/2003
• Est. Priority Date: 07/01/1999
• Status: Active Grant

• Current Assignee: Yeda Research and Development Co. Ltd. (Weizmann Institute of Science)
• Original Assignee: Yeda Research and Development Co. Ltd. (Weizmann Institute of Science)
• Inventors: Kam, Zvi
• Granted Patent: US 7,225,172 B2
• Time in Patent Office: Days
• US Class Current: 706/15
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/30 Dynamic-time models
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y10S 706/924 Medical

Abstract

In a method and apparatus for analyzing multivariable data sets, a general computerized platform is provided for evaluating the relationship between large number of measurements of sets of variables characterizing components of complex states of a system under induced stimulation or controlled conditions. The linked responses of variables and their temporal relations tell about the network of interactions and their hierarchy. Processing of data sets by a simple neural network gives a matrix of weight parameters, that allow to identify fingerprints of complex states characterized by patterns of measured variable and estimate the interactions between the components characterized by the measured variables. The results are provided numerically and by color-coded presentation indicating dominating relations between variables and strongly responding variables. When applied to dynamic responses of a system, the analysis can construct a schematic hierarchical architecture of the network of interaction between the components of the studied system. Applications in biology include analysis of measurements characterizing responses of molecular components in cells under changes induced by stimuli (e.g. drugs, growth factors, hormones, mutations or forced expression of a proteins), and identification of complex cellular states (e.g. proliferation, differentiation, transformation, starvation, necrosis, apoptosis, and the time dependencies of the above effects).

Claims

1. Apparatus for analyzing multivariable data sets including a plurality of measured variables, said apparatus comprising:

a neural network capable of receiving signals contained in said data sets and processing said signals according to an artificial intelligence program; and

means for obtaining a matrix of weight parameters for said neural network and said data sets through a sequence of iterations, starting at random guess, and repeatedly averaging for many initial guesses until said matrix of weight parameters converge; and

means of evaluation of the relationship between said variables from said weight parameters.

2. Apparatus according to claim 1 wherein said data sets comprise experimentally determined data which characterize variations in measurable components of a biological process.

3. Apparatus according to claim 2 for fingerprinting complex cellular states by analysis of cell responses to external stimuli, comprising:

means for collecting data sets which includes a plurality of induced and measured variables which characterize stimuli applied to cells and responses of said cells to said stimuli;

a neural network capable of receiving signals contained in said data sets and to process said signals according to an artificial intelligence program; and

means for obtaining a matrix of weight parameters from said neural network, said weight parameters allowing identification of fingerprints of complex cellular states.

4. Apparatus according to claim 3 wherein said external stimulus may be for example a drug, growth factor, hormone, a mutated proteins or forced expression of cellular component, and said complex cellular state is starvation, appoptosis, cell differentiation, mitogenicity of proliferating cells or cell cycle arrest.

5. Apparatus according to claim 2 for construction of hierarchical architecture of interaction network between said components of said biological process by analysis of cell responses to external stimuli, comprising:

means for collecting time dependent data sets which includes a plurality of changing variables which characterize responses of said cells to said stimuli;

a neural network capable of receiving signals contained in said data sets and to process said signals according to an artificial intelligence program; and

means for obtaining a matrix of weight parameters from said neural network, said weight parameters allowing the construction of hierarchical architecture of said interaction network.

6. Apparatus according to claim 5 wherein said network is a signaling pathway in cells.

7. Apparatus according to claim 1 wherein said neural network matrix of weight parameters is represented by a color coded image, in which dominating positive and negative weight parameters are easily identified.

8. Apparatus according to claim 1 wherein said neural network comprises a matrix of weight parameters W_ji which operate on input variables I(k)_j, through a monotonic transfer function to generate output variables O(k)_i, according to:

9. 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.

10. Process according to claim 9 wherein said neural network comprises a matrix of weight parameters W_ji which operate on input variables I(k)_j, through a monotonic transfer function to generate output variables O(k)_i, according to:

11. 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.

12. The method of claim 11 wherein the hierarchical structure is portrayed by drawing the vectors between the variables as determined by the magnitude of the matrix of weights.

13. The method of claim 12 wherein weights smaller than a preselected threshold are ignored in the portrayal.

14. The method of claim 11 wherein the data set of input/output multivariable vectors constitutes time dependent data.

15. The method of claim 14 wherein the data set includes an input set including a measured set of variables at consecutive times, and an output set comprised of the same data as the input set, but shifted forward in time to a later time, so that the neural network learns to take in data at one time and give out data at the later time.

16. 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.

17. The method of claim 16 wherein a multitude of random guesses are used and a multitude of weight matrix solutions are obtained which are averaged to obtain the averaged weight matrix.

18. Apparatus for processing input vectors representative of a biological process involving many interacting multifunctional components in at least one pathway comprising,

a neural network composed of single layer network operators trained by a training algorithm to implement a transformation by a matrix of weights, the training including 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, and then averaging the weight matrix solutions to obtain an averaged weight matrix, said neural network having been modified so that the neural network is set to its transformation of a matrix of weights to perform its function based on the averaged weight matrix, so that the modified neural network will give a predetermined output vector for a predetermined input vector that presents a prescribed pattern of values, and

a mechanism for inputting vectors into the modified neural network, and to receive output vectors from the modified neural network.

19. The apparatus of claim 18 including a device for recognizing a predetermined input vector based on the output vectors received by the mechanism.

20. The apparatus of claim 18 wherein the input vectors are sorted according to predetermined criteria for the output vectors.