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.

1 - 65 . (canceled)  
     
     
         66 . A method of determining whether a biological sample taken from a subject indicates that the subject has a disease by analyzing a data stream that is obtained by performing an analysis of the biological sample, wherein the data stream has been abstracted to produce a sample vector that characterizes the data stream in a predetermined vector space containing a diagnostic cluster, the diagnostic cluster being a disease cluster, and the disease cluster corresponding to the presence of the disease, comprising the steps of: 
 determining whether the sample vector rests within the disease cluster; and    if the sample vector rests within the disease cluster, providing an indication that the subject has the disease.    
     
     
         67 . The method of  claim 66 , wherein the data stream is data describing an expression of molecules in the biological sample.  
     
     
         68 . The method of  claim 67 , wherein the molecules are proteins.  
     
     
         69 . The method of  claim 67 , wherein the molecules are selected from the group consisting of proteins, peptides, phospholipids, DNA, and RNA.  
     
     
         70 . The method of  claim 66 , wherein the data stream is formed by any high throughput data generation method.  
     
     
         71 . The method of  claim 66 , wherein the data stream is based on data associated with a time of flight mass spectrum.  
     
     
         72 . The method of  claim 71 , wherein the time of flight mass spectrum is generated by surface-enhanced laser desorption time-of-flight mass spectroscopy.  
     
     
         73 . The method of  claim 71 , wherein the time of flight mass spectrum is generated by matrix assisted laser desorption ionization time of flight.  
     
     
         74 . The method of  claim 66 , wherein the data stream is based on data associated with a spectrum.  
     
     
         75 . The method of  claim 66 , wherein the data stream is based on data associated with a mass spectrum.  
     
     
         76 . The method of  claim 66 , the vector space contains a healthy cluster, the healthy cluster corresponding to an absence of the disease, further comprising: 
 determining whether the sample vector rests within the healthy cluster; and    if the sample vector rests within the healthy cluster, providing an indication that the subject does not have the disease.    
     
     
         77 . A method of determining whether a biological sample taken from a subject indicates that the subject does not have a disease by analyzing a data stream that is obtained by performing an analysis of the biological sample, wherein the data stream has been abstracted to produce a sample vector that characterizes the data stream in a predetermined vector space containing a diagnostic cluster, the diagnostic cluster being a healthy cluster, and the healthy cluster corresponding to the absence of the disease, comprising the steps of: 
 determining whether the sample vector rests within the healthy cluster; and    if the sample vector rests within the healthy cluster, providing an indication that the subject does not have the disease.    
     
     
         78 . The method of  claim 77 , wherein the data stream is data describing an expression of molecules in the biological sample.  
     
     
         79 . The method of  claim 78 , wherein the molecules are proteins.  
     
     
         80 . The method of  claim 78 , wherein the molecules are selected from the group consisting of proteins, peptides, phospholipids, DNA, and RNA.  
     
     
         81 . The method of  claim 77 , wherein the data stream is formed by any high throughput data generation method.  
     
     
         82 . The method of  claim 77 , wherein the data stream is based on data associated with a time of flight mass spectrum.  
     
     
         83 . The method of  claim 82 , wherein the time of flight mass spectrum is generated by surface-enhanced laser desorption time-of-flight mass spectroscopy.  
     
     
         84 . The method of  claim 82 , wherein the time of flight mass spectrum is generated by matrix assisted laser desorption ionization time of flight.  
     
     
         85 . The method of  claim 77 , wherein the data stream is based on data associated with a spectrum.  
     
     
         86 . The method of  claim 77 , wherein the data stream is based on data associated with a mass spectrum.  
     
     
         87 . The method of  claim 77 , wherein the vector space contains a disease cluster, the disease cluster corresponding to the presence of the disease, further comprising: 
 determining whether the sample vector rests within the disease cluster; and    if the sample vector rests within the disease cluster, providing an indication that the subject has the disease.    
     
     
         88 . A method of determining whether a sample is of a first state or a second state by analyzing a data stream that is obtained by performing an analysis of the sample, comprising: 
 abstracting the data stream to produce a sample vector that characterizes the data stream in a predetermined vector space containing a cluster, the cluster being associated with the first state;    determining whether the sample vector rests within the cluster; and    if the sample vector rests within the cluster, identifying the sample as being of the first state and displaying the result.    
     
     
         89 . The method of  claim 88 , wherein the sample is a biological sample.  
     
     
         90 . The method of  claim 88 , wherein the sample is a biological sample taken from a human subject.  
     
     
         91 . The method of  claim 88 , wherein the first state is a disease state.  
     
     
         92 . The method of  claim 88 , wherein the data stream is data describing an expression of molecules in the sample.  
     
     
         93 . The method of  claim 92 , wherein the molecules are selected from the group consisting of proteins, peptides, phospholipids, DNA, and RNA.  
     
     
         94 . The method of  claim 88 , wherein the data stream is based on data associated with a time of flight mass spectrum.  
     
     
         95 . The method of  claim 88 , wherein the data stream is based on data associated with a spectrum.  
     
     
         96 . The method of  claim 88 , wherein the data stream is based on data associated with a mass spectrum.  
     
     
         97 . The method of  claim 88 , wherein the cluster is a first cluster, the vector space contains a second cluster, the second cluster is associated with the second state, further comprising: 
 determining whether the sample vector rests within the second cluster; and    if the sample vector rests within the second cluster, identifying the sample as being of the second state and displaying the result.