Process for discriminating between biological states based on hidden patterns from biological data

Abstract

The invention describes a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirantas, semen, vaginal fluids, pre-ejaculate.), etc. which is analyzed to determine the biological state of the donor. The biological state can be a pathologic diagnosis, toxicity state, efficacy of a drug, prognosis of a disease, etc. Specifically, the invention concerns processes that discover hidden discriminatory biological data patterns (e.g., patterns of protein expression in a serum sample that classify the biological state of an organ) that describe biological states.

Description

[0001] This application claims benefit under 35 U.S.C. sec. 119(e)(1) of the priority of application Serial No. 60 / 232,909, filed Sep. 12, 2000, Serial No. 60 / 278,550, filed Mar. 23, 2001, Serial No. 60 / 219,067, filed Jul. 18, 2000, and U.S. Provisional Application titled “A Data Method Algorithm Reveals Disease with Protein Signal of Ovarian and Prostate Cancer in Serum,” (Serial. No. to be assigned), filed May 8, 2001 which is hereby incorporated by reference in its entirety.I. FIELD OF THE INVENTION [0002] The field of the invention concerns a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspir...

AI Technical Summary

Benefits of technology

"The invention is a method for detecting patterns in the expression of molecules in biological samples, which can be used for diagnostic purposes. The method involves using a learning algorithm to find a set of features that can discriminate between different biological states. The features are hidden in the data and can be found by performing a search using an optimal set of features. The method can be used with various statistical methods and is efficient in finding relevant patterns. The learning algorithm uses a combination of genetic algorithms and self-organizing adaptive pattern recognition systems. The method can be used with various types of data, such as mass spectral data or mRNA hybridization data. Overall, the invention provides a more effective and efficient way to detect patterns in molecular expression for diagnostic purposes."

Problems solved by technology

However, statistical methods have some limitations in that they are often linear, at least in their simple, well-known forms such as multivariate linear regressions.

Furthermore, statistical models tend not to be robust with respect to non-linear data.