A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data
<italic>Goal:</italic> Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the multiple testing problem. A four-step framew...
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2021
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oai:doaj.org-article:37424223ba2649ab913818a0b0e64cf52021-11-24T00:04:05ZA Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data2644-127610.1109/OJEMB.2021.3100416https://doaj.org/article/37424223ba2649ab913818a0b0e64cf52021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9497700/https://doaj.org/toc/2644-1276<italic>Goal:</italic> Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the multiple testing problem. A four-step framework is proposed for the efficient identification of n-Way interactions. <italic>Methods:</italic> The framework was applied on a Multiple Sclerosis dataset with 725 subjects and 147 tagging SNPs. The first two steps of the framework are quality control and feature selection. The next step uses clustering and binary encodes the features. The final step performs the n-Way interaction testing. <italic>Results:</italic> The feature space was reduced to 7 SNPs and using the proposed binary encoding, more 2-SNP and 3-SNP interactions were identified compared to using the initial encoding. <italic>Conclusions:</italic> The framework selects informative features and with the proposed binary encoding it is able to identify more n-way interactions by increasing the power of the statistical analysis.Aristos AristodimouAthos AntoniadesEfthimios DardiotisEleni LoizidouGeorge SpyrouChristina VotsiChristodoulou KyproulaMarios PantzarisNikolaos GrigoriadisGeorgios HadjigeorgiouTheodoros KyriakidesConstantinos PattichiIEEEarticleClusteringEpistasisFeature SelectionInteraction TestingMachine LearningComputer applications to medicine. Medical informaticsR858-859.7Medical technologyR855-855.5ENIEEE Open Journal of Engineering in Medicine and Biology, Vol 2, Pp 256-262 (2021) |
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DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Clustering Epistasis Feature Selection Interaction Testing Machine Learning Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5 |
| spellingShingle |
Clustering Epistasis Feature Selection Interaction Testing Machine Learning Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5 Aristos Aristodimou Athos Antoniades Efthimios Dardiotis Eleni Loizidou George Spyrou Christina Votsi Christodoulou Kyproula Marios Pantzaris Nikolaos Grigoriadis Georgios Hadjigeorgiou Theodoros Kyriakides Constantinos Pattichi A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data |
| description |
<italic>Goal:</italic> Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the multiple testing problem. A four-step framework is proposed for the efficient identification of n-Way interactions. <italic>Methods:</italic> The framework was applied on a Multiple Sclerosis dataset with 725 subjects and 147 tagging SNPs. The first two steps of the framework are quality control and feature selection. The next step uses clustering and binary encodes the features. The final step performs the n-Way interaction testing. <italic>Results:</italic> The feature space was reduced to 7 SNPs and using the proposed binary encoding, more 2-SNP and 3-SNP interactions were identified compared to using the initial encoding. <italic>Conclusions:</italic> The framework selects informative features and with the proposed binary encoding it is able to identify more n-way interactions by increasing the power of the statistical analysis. |
| format |
article |
| author |
Aristos Aristodimou Athos Antoniades Efthimios Dardiotis Eleni Loizidou George Spyrou Christina Votsi Christodoulou Kyproula Marios Pantzaris Nikolaos Grigoriadis Georgios Hadjigeorgiou Theodoros Kyriakides Constantinos Pattichi |
| author_facet |
Aristos Aristodimou Athos Antoniades Efthimios Dardiotis Eleni Loizidou George Spyrou Christina Votsi Christodoulou Kyproula Marios Pantzaris Nikolaos Grigoriadis Georgios Hadjigeorgiou Theodoros Kyriakides Constantinos Pattichi |
| author_sort |
Aristos Aristodimou |
| title |
A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data |
| title_short |
A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data |
| title_full |
A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data |
| title_fullStr |
A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data |
| title_full_unstemmed |
A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data |
| title_sort |
framework for efficient n-way interaction testing in case/control studies with categorical data |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/37424223ba2649ab913818a0b0e64cf5 |
| work_keys_str_mv |
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