Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain.

Objectives: Clinicians can rapidly and accurately diagnose disease, learn from experience, and explain their reasoning. Computational Bayesian medical decision-making might replicate this expertise. This paper assesses a computer system for diagnosing cardiac chest pain in the emergency department (ED) that decides whether to admit or discharge a patient.

Validating TrueAllele Interpretation of DNA Mixtures Containing up to Ten Unknown Contributors.

Most DNA evidence is a mixture of two or more people. Cybergenetics TrueAllele system uses Bayesian computing to separate genotypes from mixture data and compare genotypes to calculate likelihood ratio (LR) match statistics. This validation study examined the reliability of TrueAllele computing on laboratory-generated DNA mixtures containing up to ten unknown contributors.

Efficient construction of match strength distributions for uncertain multi-locus genotypes.

Natural variation in biological evidence leads to uncertain genotypes. Forensic comparison of a probabilistic genotype with a person's reference gives a numerical strength of DNA association. The distribution of match strength for all possible references usefully represents a genotype's potential information.

Inclusion probability for DNA mixtures is a subjective one-sided match statistic unrelated to identification information.

Background: DNA mixtures of two or more people are a common type of forensic crime scene evidence. A match statistic that connects the evidence to a criminal defendant is usually needed for court. Jurors rely on this strength of match to help decide guilt or innocence.

TrueAllele(®) Genotype Identification on DNA Mixtures Containing up to Five Unknown Contributors.

Computer methods have been developed for mathematically interpreting mixed and low-template DNA. The genotype modeling approach computationally separates out the contributors to a mixture, with uncertainty represented through probability. Comparison of inferred genotypes calculates a likelihood ratio (LR), which measures identification information.

TrueAllele casework on Virginia DNA mixture evidence: computer and manual interpretation in 72 reported criminal cases.

Mixtures are a commonly encountered form of biological evidence that contain DNA from two or more contributors. Laboratory analysis of mixtures produces data signals that usually cannot be separated into distinct contributor genotypes. Computer modeling can resolve the genotypes up to probability, reflecting the uncertainty inherent in the data.

New York State TrueAllele ® casework validation study.

DNA evidence can pose interpretation challenges, particularly with low-level or mixed samples. It would be desirable to make full use of the quantitative data, consider every genotype possibility, and objectively produce accurate and reproducible DNA match results. Probabilistic genotype computing is designed to achieve these goals.

DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: combining quantitative data for greater identification information.

Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this.

Validating TrueAllele® DNA mixture interpretation.

DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all-or-none events and assigning allele pairs equal likelihood.

An information gap in DNA evidence interpretation.

Forensic DNA evidence often contains mixtures of multiple contributors, or is present in low template amounts. The resulting data signals may appear to be relatively uninformative when interpreted using qualitative inclusion-based methods. However, these same data can yield greater identification information when interpreted by computer using quantitative data-modeling methods.

Determining sequence length or content in zero, one, and two dimensions.

High-throughput assays are essential for the practical application of mutation detection in medicine and research. Moreover, such assays should produce informative data of high quality that have a low-error rate and a low cost. Unfortunately, this is not currently the case.