Methods and system for predicting multi-variable outcomes

Abstract

Systems methods and recordable media for predicting multi-variable outcomes based on multi-variable inputs. Additionally, the models described can be used to predict the multi-variable inputs themselves, based on the multi-variable inputs, providing a smoothing function, acting as a noise filter. Both multi-variable inputs and multi-variable outputs may be simultaneously predicted, based upon the multi-variable inputs. The models find a critical subset of data points, or “tent poles” to optimally model all outcome variables simultaneously to leverage communalities among outcomes.

Description

CROSS-REFERENCE [0001] This application claims the benefit of U.S. Provisional Application No. 60 / 368,586, filed Mar. 29, 2002, which application is incorporated herein, in its entirety, by reference thereto.FIELD OF THE INVENTION [0002] The present invention relates to software, methods, and devices for evaluating correlations between observed phenomena and one or more factors having putative statistical relationships with such observed phenomena. More particularly, the software, methods, and devices described herein relate to the prediction of the suitability of new compounds for drug development, including predictions for diagnosis, efficacy, toxicity, and compound similarity among others. The present invention may also be applicable in making predictions relating to other complex, multivariate fields, including earthquake predictions, economic predictions, and others. For example the transmission of seismic signals through a particular fault may exhibit significant changes in pr...

AI Technical Summary

Benefits of technology

[0019] According to the present invention, the dimensions of the data can be reduced to a lower dimension as defined only by necessary critical components to represent the phenomenon being modeled. Hence, in general, the present invention is valuable to help researchers “see” the high-dimensional patterns from limited noisy data on complex phenomenon that can involve multiple inputs and multiple consequential outputs (e.g., outcomes or responses).

[0022] The present invention finds a critical subset of data points to optimally model all outcome variables simultaneously to leverage both communalities among outcomes and uniqueness properties of each outcome. The method relates measured variables associated with a complex phenomenon using a simple direct functional process that eliminates artifactual inferences even if the data is sparse or limited and the variable space is high dimensional. The present invention can also be layered to model higher-ordered features, e.g., output of a GSMILES network can be input to a second GSMILES network. Such GSMILES networks may include feedback loops. If profiles include one or more ordered indices such as “time,” GSMILES networks can incorporate the ordering of such indices (i.e., “time” series). GSMILES also provides statistical evaluations and diagnostics of the analysis, both retrospective and prospective scenarios. GSMILES reduces random noise by combining data from replicate and nearby adjacent information (i.e., pseudo-replicates).

Problems solved by technology

The most widely used statistical methods currently used in the medical and drug discovery fields are generally limited to conventional regression methods which relate clinical variables obtained from patients being treated for a disease with the probable treatment outcomes for those patients, based upon data relating to the particular drug, drugs or treatment methodology being performed on that patient.

The nonlinearity of the parameters in the logistic probability function, coupled with the use of the maximum likelihood estimation procedure, makes logistic regression methods complicated.

Thus, such methods are often ineffective for complex models in which interactions among the various clinical variables being studied are present, or where multivariable characterizations of the outcomes are desired, Such as when characterizing all experimental drug.

In addition, the coupling of logistic and maximum likelihood methods limits the validation of logistic models to retrospective predictions that can overestimate the model's true abilities.

However, these combined models are effective only for limited degrees of interactions among clinical variables and thus are inadequate for many applications.

SMILES fails, however, to provide a means to effectively handle multiple outcome variables or outcomes of different types.

It becomes difficult to perform analysis with separate independent models.

Nuisance and noise factors complicate this task even further.

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