Gibiansky
Andrew Leonidovich Gibiansky, Claremont, CA US
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20160140289 | VARIANT CALLER - Processes and systems for reading variants from a genome sample relative to a reference genomic sequence are provided. An exemplary process includes collecting a set reads and generating a k-mer graph from the reads. For example, the k-mer graph can be constructed to represent all possible substrings of the collected reads. The k-mer graph may be reduced to a contiguous graph, and a set of possible haplotypes generated from the contiguous graph. The process may further generate, the error table providing a filter for common sequencer errors. The process may then generate a set of diplotypes based on the set of haplotypes and the generated error table and score the set of diplotypes to identify variants from the reference genome. Scoring the diplotypes may include determining a posterior probability for each of the diplotypes, with the highest scoring diplotype(s) reported as the result. | 05-19-2016 |
Ekaterina Gibiansky, North Potomac, MD US
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20090221532 | Methods Of Dosing Propofol Prodrugs For Inducing Mild To Moderate Levels Of Sedation - A dosage of a propofol prodrug needed for inducing mild to moderate sedation levels in a patient is calculated based on a patient's lean body mass. It has been discovered dosages based on gross body mass may result in overdosing, particularly for obese patients. In another aspect, a dosage suitable for inducing mild to moderate sedation levels in a patient who is at least 60 years of age is determined. A weight-appropriate dosage for the patient is determined and then adjusted by an age-based factor. For example, the dosage needed to produce a sedated state or other effect in a patient who is 60 years of age or older may be about 0.6-0.8 times the dosage needed to produce a corresponding effect in a younger patient of the same weight. | 09-03-2009 |
Leonid Gibiansky, N. Potomac, MD US
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20090313087 | METHOD FOR OPTIMIZING NET PRESENT VALUE OF A CROSS-SELLING MARKETING CAMPAIGN - The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list. | 12-17-2009 |
Leonid Gibiansky, North Potomac, MD US
Patent application number | Description | Published |
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20090221532 | Methods Of Dosing Propofol Prodrugs For Inducing Mild To Moderate Levels Of Sedation - A dosage of a propofol prodrug needed for inducing mild to moderate sedation levels in a patient is calculated based on a patient's lean body mass. It has been discovered dosages based on gross body mass may result in overdosing, particularly for obese patients. In another aspect, a dosage suitable for inducing mild to moderate sedation levels in a patient who is at least 60 years of age is determined. A weight-appropriate dosage for the patient is determined and then adjusted by an age-based factor. For example, the dosage needed to produce a sedated state or other effect in a patient who is 60 years of age or older may be about 0.6-0.8 times the dosage needed to produce a corresponding effect in a younger patient of the same weight. | 09-03-2009 |
Maxsim Gibiansky, Sunnyvale, CA US
Patent application number | Description | Published |
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20160110657 | Configurable Machine Learning Method Selection and Parameter Optimization System and Method - A system and method for selecting a machine learning method and optimizing the parameters that control its behavior including receiving data; determining, using one or more processors, a first candidate machine learning method; tuning, using one or more processors, one or more parameters of the first candidate machine learning method; determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method. | 04-21-2016 |