Syracuse University

Jonathan David Adelman

Michael Marciano

William Ryon McKay

Jacquelyn Lillis

Laura C Haarer

Katherine Lawson

A system configured to characterize a number of contributors to a DNA mixture within a sample, the system comprising: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a number of contributors determination module comprising a machine-learning algorithm configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the number of contributors to the DNA mixture within the sample; and an output device configured to receive the determined number of contributors from the processor, and further configured output information about the received determined number of contributors.

Methods and systems for determination of the number of contributors to a DNA mixture

A system for evaluating a DNA sample and determining whether the sample contains related individuals and/or unrelated individuals with high levels of alleles sharing. Trained and pre-validated machine learning algorithms are to rapidly and probabilistically assess the presence of relatives in a DNA mixture. To make a probabilistic determination, the system evaluates aspect of the sample that have not be considered before, such as peak heights, peak height ratios, maximum peak heights, minimum peak heights, ratios of allele heights to one another, number of contributors using maximum allele count method, and quantitative measures of the amount of DNA contributed by the male and female organisms. The system identifies whether a DNA sample has contributors that are not readily identifiable based on the data and can thus improve downstream analysis.

Hierarchical optimized detection of relatives

Methods and systems for characterizing two or more nucleic acids in a sample. The method can include the steps of providing a hybrid machine learning approach that enables rapid and automated deconvolution of DNA mixtures of multiple contributors. The input is analyzed by an expert system which is implemented in the form of a rule set. The rule set establishes requirements based on expectations on the biology and methods used. The methods and systems also include a machine learning algorithm that is either incorporated into the expert system, or utilizes the output of the expert system for analysis. The machine learning algorithm can be any of a variety of different algorithms or combinations of algorithms used to perform classification in a complex data environment.

System and method for inter-species DNA mixture interpretation

A system configured to characterize a ratio of contributors to a DNA mixture within a sample, the system including: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a ratio of contributors determination module configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the ratio of contributors to the DNA mixture within the sample; and an output device configured to receive the determined ratio of contributors from the processor, and further configured to output information about the received determined ratio of contributors.

Methods and systems for prediction of a DNA profile mixture ratio

The present invention relates to a method and system for classifying high-resolution melt ('HRM') curves, and, more specifically, to a method and system for classifying HRM curves by genotype where the curves are represented by a mathematical function with varying coefficient values.

High-resolution melt curve classification using neural networks

A method and system for geolocation. The method and system determines the geographic history of a target of interest (i.e., the probability that a sample of interest passed though a defined location) given a combination of spatially attributable input data and weighing distributions, and modifying the probability for each possible location using the equation where P(x) is the probability that the sample of interest passes through a given geographic location X, nis the total number of possible legal travel routes for the sample of interest, nis the number of legal travel routes including location X, k is the travel route's cardinality, and w(k) is the assigned weight for cardinality k from the user-defined weighting function.

Combinatorics method for probabilistic geolocation

A method for pollen-based geolocation. The method determines the probability P that a given location is part of the travel history of a given sample. Using simulated datasets and Monte Carlo simulation, the model parameters can be precisely associated with P, thereby allowing the algorithm to operate on real-life samples of interest.

Growing community of inventors

Mexico, NY, United States of America

Jonathan David Adelman