Automated splicing mutation analysis by information theory

2008 ◽  
Vol 29 (9) ◽  
pp. 1168-1168 ◽  
Author(s):  
Vijay K. Nalla ◽  
Peter K. Rogan
Keyword(s):  
Information Theory ◽  
Mutation Analysis ◽  
Splicing Mutation

Automated splicing mutation analysis by information theory

2005 ◽  
Vol 25 (4) ◽  
pp. 334-342 ◽  
Author(s):  
Vijay K. Nalla ◽  
Peter K. Rogan
Keyword(s):  
Information Theory ◽  
Mutation Analysis ◽  
Splicing Mutation

Determination of haemophilia A carrier status by mutation analysis

Haemophilia ◽  
2001 ◽  
Vol 7 (1) ◽  
pp. 20-25 ◽  
Author(s):  
S. Oranwiroon ◽  
V. Akkarapatumwong ◽  
P. Pung-Amritt ◽  
A. Treesucon ◽  
G. Veerakul ◽  
...  
Keyword(s):  
Mutation Analysis ◽  
Carrier Status ◽  
Haemophilia A

homeodomain mutation analysis in patients with Müllerian system anomalies

1998 ◽  
Vol 5 (1) ◽  
pp. 140A-140A ◽  
Author(s):  
J STELLING ◽  
B BHAGAVATH ◽  
M GRAY ◽  
R REINDOLLAR
Keyword(s):  
Mutation Analysis

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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