A method that analyzes mass spectra using a digital computer is disclosed. The method includes entering into a digital computer a data set obtained from mass spectra from a plurality of samples. Each sample is, or is to be assigned to a class within a class set having two or more classes and each class is characterized by a different biological status. A classification model is then formed. The classification model discriminates between the classes in the class set.

Method for analyzing mass spectra

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.

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

http://www.hpl.hp.com/techreports/90/HPL-90-161.pdf

MSnet: A Neural Network which Classifies Mass Spectra

Methods are provided for predicting the presence, subtype and stage of ovarian cancer, as well as for assessing the therapeutic efficacy of a cancer treatment and determining whether a subject potentially is developing cancer. Associated test kits, computer and analytical systems as well as software and diagnostic models are also provided.

Predictive markers for ovarian cancer

The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

Heuristic method of classification

http://link.springer.com/content/pdf/10.1007/978-1-4684-1312-0_11.pdf

Approaches to Pyrolysis/Mass Spectrometry Data Analysis of Biological Materials

An apparatus and method for the electrodeposition of microorganisms on an electrode using a weak, direct current voltage. The electrodeposition of the microorganisms occurs within a relatively short period of time thereby reducing the requirement for culturing the microorganisms. The electrode can be used as an electrode for a Curie point pyrolysis mass spectrometer to determine the specific microorganism electrodeposited on the electrode.

Apparatus and method for concentrating microorganisms from a liquid medium on an electrode by electrodeposition

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Approaches to Pyrolysis/Mass Spectrometry Data Analysis of Biological Materials

Apparatus and method for concentrating microorganisms from a liquid medium on an electrode by electrodeposition