Connecting human disease phenotype to genetic mutation and protein function: A modular data mining short course with an independent project sequence for lecture or lab

2018 ◽  
Author(s):  
Janet M. Murray ◽  
Heather E. Driscoll ◽  
Kara Pivarski
Keyword(s):  
Data Mining ◽  
Protein Function ◽  
Human Disease ◽  
Genetic Mutation ◽  
Disease Phenotype ◽  
Short Course ◽  
Modular Data

Reassessment of theTP53 mutation database in human disease by data mining with a library ofTP53 missense mutations

2004 ◽  
Vol 25 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Thierry Soussi ◽  
Shunsuke Kato ◽  
Pierre P. Levy ◽  
Chikashi Ishioka
Keyword(s):  
Data Mining ◽  
Human Disease ◽  
Missense Mutations ◽  
Mutation Database

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2011 ◽  
pp. 114-140
Author(s):  
Alex Freitas ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

scholarly journals Myopia disease mouse models: a missense point mutation (S673G) and a protein-truncating mutation of the Zfp644 mimic human disease phenotype

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Katarzyna I. Szczerkowska ◽  
Silvia Petrezselyova ◽  
Jiri Lindovsky ◽  
Marcela Palkova ◽  
Jan Dvorak ◽  
...  
Keyword(s):  
Point Mutation ◽  
Mouse Models ◽  
Human Disease ◽  
Disease Phenotype ◽  
Truncating Mutation

scholarly journals Mutations in the splicing regulator Prp31 lead to retinal degeneration in Drosophila

Biology Open ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. bio052332 ◽  
Author(s):  
Sarita Hebbar ◽  
Malte Lehmann ◽  
Sarah Behrens ◽  
Catrin Hälsig ◽  
Weihua Leng ◽  
...  
Keyword(s):  
Retinal Degeneration ◽  
Human Disease ◽  
Molecular Mechanisms ◽  
Autosomal Dominant ◽  
Light Stress ◽  
Mrna Levels ◽  
Disease Phenotype ◽  
Induced Mutations ◽  
Splicing Regulator ◽  
Splicing Machinery

ABSTRACTRetinitis pigmentosa (RP) is a clinically heterogeneous disease affecting 1.6 million people worldwide. The second-largest group of genes causing autosomal dominant RP in human encodes regulators of the splicing machinery. Yet, how defects in splicing factor genes are linked to the aetiology of the disease remains largely elusive. To explore possible mechanisms underlying retinal degeneration caused by mutations in regulators of the splicing machinery, we induced mutations in Drosophila Prp31, the orthologue of human PRPF31, mutations in which are associated with RP11. Flies heterozygous mutant for Prp31 are viable and develop normal eyes and retina. However, photoreceptors degenerate under light stress, thus resembling the human disease phenotype. Degeneration is associated with increased accumulation of the visual pigment rhodopsin 1 and increased mRNA levels of twinfilin, a gene associated with rhodopsin trafficking. Reducing rhodopsin levels by raising animals in a carotenoid-free medium not only attenuates rhodopsin accumulation, but also retinal degeneration. Given a similar importance of proper rhodopsin trafficking for photoreceptor homeostasis in human, results obtained in flies presented here will also contribute to further unravel molecular mechanisms underlying the human disease.This paper has an associated First Person interview with the co-first authors of the article.

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2007 ◽  
pp. 175-208 ◽  
Author(s):  
Alex Freitas ◽  
André Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2008 ◽  
pp. 119-145
Author(s):  
Alex Freitas ◽  
Andre´ C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

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