Predicting Influenza A Viral Host Using PSSM and Word Embeddings

Xu, Yanhua ORCID: 0000-0003-1028-9023 and Wojtczak, Dominik ORCID: 0000-0001-5560-0546 (2021) Predicting Influenza A Viral Host Using PSSM and Word Embeddings. In: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2021-10-13 - 2021-10-15.

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Official URL: http://dx.doi.org/10.1109/CIBCB49929.2021.9562959

Abstract

The rapid mutation of the influenza virus threatens public health. Reassortment among viruses with different hosts can lead to a fatal pandemic. However, it is difficult to detect the original host of the virus during or after an outbreak as influenza viruses can circulate between different species. Therefore, early and rapid detection of the viral host would help reduce the further spread of the virus. We use various machine learning models with features derived from the position-specific scoring matrix (PSSM) and features learned from word embedding and word encoding to infer the origin host of viruses. The results show that the performance of the PSSM-based model reaches the MCC around 95%, and the F1 around 96%. The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%.

Item Type:	Conference or Workshop Item (Unspecified)
Additional Information:	Accepted for publication at CIBCB 2021. V1: accepted version + minor correction to table 1; V2: corrected a minor typo; V3: update the formula of error rate; V4: replacing 'nested cv' with 'nested k-fold cv' for better clarity
Uncontrolled Keywords:	Influenza, Machine Learning, Deep Learning, Position-specific Scoring Matrix, Word Embedding, Support Vector Machine, Ensemble Model, Convolutional Neural Network
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	26 Jan 2022 09:21
Last Modified:	15 Mar 2024 00:25
DOI:	10.1109/CIBCB49929.2021.9562959
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3147597