Surromed, Inc.

Ppd Biomarker Discovery Sciences, LLC

Permissionbit

Curtis A Hastings

Christopher Hank Becker

Scott M Norton

Thomas Andrew Shaler

Hua Lin

Sushmita Mimi Roy

Markus Anderle

Ronnie Mainieri

Weixun Wang

Haihong Zhou

Praveen Kumar

A method for encoding computer processes for malicious program detection. The method includes the steps of (a) randomly sampling a trace of system calls collected over a predetermined interval, each system call including context information and memory addresses for the function being monitored; (b) computing system address differences from the trace of system calls and retaining the computed values; (c) forming a group of n-grams (words) of retained differences of system addresses from the trace of system calls; (d) forming a series of process snippets, each process snippet including context information and the retained differences of system addresses; (e) transforming each process snippet to form a compact representation (process dot) comprising a pair of elementsc, a, wherein c includes the context information and a is a sparse vector that encodes information derived from the group of n-grams; (f) forming clusters of compact representations; (g) obtaining clusters of compact representations from one or more malicious program-free computers; and (h) comparing the clusters formed in step (f) to those obtained in step (g) and determining the presence of malicious program from the comparison.

Methods and systems for encoding computer processes for malware detection

High-intensity, spiked noise is reduced in chromatography-mass spectrometry data by applying a nonlinear filter such as a moving median filter to the data. The filter is applied to individual mass chromatograms, plots of ion abundance versus retention time for each detected mass-to-charge ratio, and the filtered chromatograms are combined to form a filtered total ion current chromatogram. Standard linear filters are not effective for reducing noise in liquid chromatography-mass spectrometry (LC-MS) data because they assume a normal distribution of noise. LC-MS noise, however, is not normally distributed.

Median filter for liquid chromatography-mass spectrometry data

An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Peak selection in multidimensional data

Relative quantitative information about components of chemical or biological samples can be obtained from mass spectra by normalizing the spectra to yield peak intensity values that accurately reflect concentrations of the responsible species. A normalization factor is computed from peak intensities of those inherent components whose concentration remains constant across a series of samples. Relative concentrations of a component occurring in different samples can be estimated from the normalized peak intensities. Unlike conventional methods, internal standards or additional reagents are not required. The methods are particularly useful for differential phenotyping in proteomics and metabolomics research, in which molecules varying in concentration across samples are identified. These identified species may serve as biological markers for disease or response to therapy.

Mass spectrometric quantification of chemical mixture components

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San Francisco, CA, United States of America

Curtis A Hastings