Teaching the Hardy-Weinberg Equilibrium: A 5E Lesson Plan

2017 ◽  
Vol 79 (4) ◽  
pp. 288-293
Author(s):  
Mike U. Smith
Keyword(s):  
Young People ◽  
Lesson Plan ◽  
Original Data ◽  
Good Teaching ◽  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium ◽  
Basic Concepts ◽  
The Cost

In an earlier paper (Smith & Baldwin, 2015), we explained the basic concepts of the Hardy-Weinberg equilibrium (HWeq) principle needed for meaningful understanding and for good teaching, emphasizing distinctions that are sometimes ignored at the cost of coherent understanding, and identifying nine shortcomings of most available Hardy-Weinberg activities and problem sets. In the present paper, we provide a 5E lesson plan based on that analysis and designed to avoid the shortcomings identified, including providing original data and focusing on understanding and topics that are interesting and meaningful to young people.

Significance of Hardy-Weinberg Equilibrium in Case Control

2004 ◽  
Vol 73 (1) ◽  
pp. 95-95
Author(s):  
Hemant Kumar Bid ◽  
Rama D. Mittal
Keyword(s):  
Case Control ◽  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium

scholarly journals Underuse of Hardy-Weinberg equilibrium

2004 ◽  
Vol 66 (4) ◽  
pp. 1711 ◽  
Author(s):  
Gere Sunder-Plassmann ◽  
Manuela Födinger
Keyword(s):  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium

scholarly journals Embedding Undersampling Rotation Forest for Imbalanced Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Huaping Guo ◽  
Xiaoyu Diao ◽  
Hongbing Liu
Keyword(s):  
Imbalanced Data ◽  
Feature Space ◽  
Original Data ◽  
Training Set ◽  
Data Set ◽  
Minority Class ◽  
Rotation Forest ◽  
Novel Method ◽  
Individual Classifier ◽  
The Cost

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.

scholarly journals Allele Frequencies of 15 AmpFISTR Identifiler loci in the Nepalese population

2012 ◽  
Vol 10 (10) ◽  
pp. 20-23 ◽  
Author(s):  
Dinesh Kumar Jha ◽  
Luis Javier Martinez Gonzalez ◽  
Jiwan Prasad Rijal ◽  
Bhinu Shova Tuladhar ◽  
Nirajan Thapa Chhetri
Keyword(s):  
Allele Frequencies ◽  
Statistical Parameters ◽  
Weinberg Equilibrium ◽  
Allelic Frequencies ◽  
Str Loci ◽  
Scientific World ◽  
Hardy Weinberg Equilibrium

Allele frequencies for the 15 STR loci in the AmpFlSTR® Identifiler® and statistical parameters were estimated from a sample of 233 unrelated individuals from different area of Nepal. A total of 161 alleles were found, with corresponding allelic frequencies ranging from 0.0021 to 0.4077. The MP, PD, PIC, PE, TPI, Ho and He ranged from 0.0282 to 0.1511, 0.8489 to 0.9717, 0.6478 to 0.8700, 0.4546 to 0.7631, 1.7651 to 4.3148, 0.7167 to 0.8841 and 0.7009 to 0.8833 respectively. Deviations from the Hardy–Weinberg Equilibrium were observed for D16S539, D18S51, D21S11 and TPOX Markers. One new allele has been detected in the process. Scientific World, Vol. 10, No. 10, July 2012 p20-23 DOI: http://dx.doi.org/10.3126/sw.v10i10.6856

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