Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics

2010 ◽  
pp. 215-226 ◽  
Author(s):  
Joshua L. Payne ◽  
Casey S. Greene ◽  
Douglas P. Hill ◽  
Jason H. Moore
Keyword(s):  
Human Genetics ◽  
Expert Knowledge ◽  
Evolution System ◽  
Epistasis Analysis

Lay understandings of the relationship between race and genetics: Development of a collectivized knowledge through shared discourse

2002 ◽  
Vol 11 (4) ◽  
pp. 373-387 ◽  
Author(s):  
Celeste Michelle Condit ◽  
Roxanne Parrott ◽  
Tina M. Harris
Keyword(s):  
Focus Group ◽  
Human Genetics ◽  
Expert Knowledge ◽  
Social Process ◽  
The United States ◽  
Theoretical Concept ◽  
Past Century ◽  
Cultural Groups ◽  
Focus Group Research ◽  
The Relationship

Throughout the past century, research into human genetics revealed the relationships between biochemistry and various human characteristics in increasing detail. At each step of this path of discovery, social critics warned that knowledge of genetics, and especially social attention to genetics, might heighten racist attitudes. In light of these warnings and the recent sequencing of the Human Genome, it is important to inquire into the interpretations laypersons might hold of the relationship between race and genetics. A variety of recent efforts have described the insufficiency of public opinion polls for arriving at sophisticated understandings of such complex attitudinal structures. Therefore, this essay offers a sketch of some lay understandings of race and genetics in the United States based on a series of focus group sessions. In order to interpret the responses, the analysis employs a novel template for interpreting focus group research based on the theoretical concept of rhetorical formations. This approach reveals the way in which the knowledge of individual members is brought to bear upon collective decision-making through the social process of discussion to produce a pool of information that is similar to expert knowledge, although phrased in a popular vocabulary. Differences in the ways in which cultural groups negotiate this knowledge are discussed.

Solving Complex Problems in Human Genetics using Nature-Inspired Algorithms Requires Strategies which Exploit Domain-Specific Knowledge

2010 ◽  
pp. 166-180
Author(s):  
Casey S. Greene ◽  
Jason H. Moore
Keyword(s):  
Dna Sequence ◽  
Classification Accuracy ◽  
Human Genetics ◽  
Expert Knowledge ◽  
Fitness Function ◽  
Building Blocks ◽  
Complex Problems ◽  
Sequence Variations ◽  
Or Gene ◽  
Nature Inspired Algorithms

In human genetics the availability of chip-based technology facilitates the measurement of thousands of DNA sequence variations from across the human genome. The informatics challenge is to identify combinations of interacting DNA sequence variations that predict common diseases. The authors review three nature-inspired methods that have been developed and evaluated in this domain. The two approaches this chapter focuses on in detail are genetic programming (GP) and a complex-system inspired GP-like computational evolution system (CES). The authors also discuss a third nature-inspired approach known as ant colony optimization (ACO). The GP and ACO techniques are designed to select relevant attributes, while the CES addresses both the selection of relevant attributes and the modeling of disease risk. Specifically, they examine these methods in the context of epistasis or gene-gene interactions. For the work discussed here we focus solely on the situation where there is an epistatic effect but no detectable main effect. In this domain, early studies show that nature-inspired algorithms perform no better than a simple random search when classification accuracy is used as the fitness function. Thus, the challenge for applying these search algorithms to this problem is that when using classification accuracy there are no building blocks. The goal then is to use outside knowledge or pre-processing of the dataset to provide these building blocks in a manner that enables the population, in a nature-inspired framework, to discover an optimal model. The authors examine one pre-processing strategy for revealing building blocks in this domain and three different methods to exploit these building blocks as part of a knowledge-aware nature-inspired strategy. They also discuss potential sources of building blocks and modifications to the described methods which may improve our ability to solve complex problems in human genetics. Here it is argued that both the methods using expert knowledge and the sources of expert knowledge drawn upon will be critical to improving our ability to detect and characterize epistatic interactions in these large scale biomedical studies.

Solving Complex Problems in Human Genetics Using Nature-Inspired Algorithms Requires Strategies which Exploit Domain-Specific Knowledge

2012 ◽  
pp. 1867-1881
Author(s):  
Casey S. Greene ◽  
Jason H. Moore
Keyword(s):  
Dna Sequence ◽  
Classification Accuracy ◽  
Human Genetics ◽  
Expert Knowledge ◽  
Fitness Function ◽  
Building Blocks ◽  
Complex Problems ◽  
Sequence Variations ◽  
Or Gene ◽  
Nature Inspired Algorithms

In human genetics the availability of chip-based technology facilitates the measurement of thousands of DNA sequence variations from across the human genome. The informatics challenge is to identify combinations of interacting DNA sequence variations that predict common diseases. The authors review three nature-inspired methods that have been developed and evaluated in this domain. The two approaches this chapter focuses on in detail are genetic programming (GP) and a complex-system inspired GP-like computational evolution system (CES). The authors also discuss a third nature-inspired approach known as ant colony optimization (ACO). The GP and ACO techniques are designed to select relevant attributes, while the CES addresses both the selection of relevant attributes and the modeling of disease risk. Specifically, they examine these methods in the context of epistasis or gene-gene interactions. For the work discussed here we focus solely on the situation where there is an epistatic effect but no detectable main effect. In this domain, early studies show that nature-inspired algorithms perform no better than a simple random search when classification accuracy is used as the fitness function. Thus, the challenge for applying these search algorithms to this problem is that when using classification accuracy there are no building blocks. The goal then is to use outside knowledge or pre-processing of the dataset to provide these building blocks in a manner that enables the population, in a nature-inspired framework, to discover an optimal model. The authors examine one pre-processing strategy for revealing building blocks in this domain and three different methods to exploit these building blocks as part of a knowledge-aware nature-inspired strategy. They also discuss potential sources of building blocks and modifications to the described methods which may improve our ability to solve complex problems in human genetics. Here it is argued that both the methods using expert knowledge and the sources of expert knowledge drawn upon will be critical to improving our ability to detect and characterize epistatic interactions in these large scale biomedical studies.

Pathway Analysis and Its Applications

2015 ◽  
pp. 215-239
Author(s):  
Ravi Mathur ◽  
Alison Motsinger-Reif
Keyword(s):  
Functional Annotation ◽  
Human Genetics ◽  
Expert Knowledge ◽  
Biological Traits ◽  
Domain Expert ◽  
Advantages And Disadvantages ◽  
Proteomic Data ◽  
Pathway Analyses ◽  
Key Resources ◽  
Potential Biomarkers

As the scale of genetic, genomic, metabolomics, and proteomic data increases with advancing technology, new approaches leveraging domain expert knowledge, and other sources of functional annotation have been developed to aid in the analysis and interpretation of such data. Pathway and network analysis approaches have become popular in association analysis – connecting genetic markers or measures of gene product with phenotypes or diseases of interest. These approaches aim to leverage big data to better understand the complex etiologies of these traits. Findings from such analyses can help reveal interesting biological traits and/or help identify potential biomarkers of disease. In the current chapter, the authors review broad categories of pathway analyses and review advantages and disadvantages of each. They discuss both the analytical methods to detect phenotype-associated pathways and review the key resources in the field of human genetics that are available to investigators wanting to perform such analyses.

scholarly journals Accelerating epistasis analysis in human genetics with consumer graphics hardware

2009 ◽  
Vol 2 (1) ◽  
pp. 149 ◽  
Author(s):  
Nicholas A Sinnott-Armstrong ◽  
Casey S Greene ◽  
Fabio Cancare ◽  
Jason H Moore
Keyword(s):  
Human Genetics ◽  
Graphics Hardware ◽  
Epistasis Analysis

A survey of human genetics in the dental school curriculum

1977 ◽  
Vol 41 (9) ◽  
pp. 563-566
Author(s):  
RG Sanger ◽  
RE Stewart
Keyword(s):  
Human Genetics ◽  
School Curriculum ◽  
Dental School
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