عنوان انگلیسی مقاله:
Identifying mutation positions in all segments of influenza genome enables better differentiation between pandemic and seasonal strains
ترجمه فارسی عنوان مقاله:
شناسايي موقعيت جهش در تمامي بخش هاي ژنوم آنفلوانزا باعث تمايز بهتر ميان سويه ها و بيماري هاي فصلي مي شود
Sciencedirect - Elsevier - Gene, 697 (2019) 78-85: doi:10:1016/j:gene:2019:01:014
Fatemeh Kargarfarda,b, Ashkan Samib, Farhid Hemmatzadehc, Esmaeil Ebrahimiec,d,e,f,⁎
Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research,
most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase).
However, new findings suggest that all internal and external proteins of influenza viruses can contribute in
pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic
and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a
unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from
seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying
mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and
seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining
techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains.
CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to
extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate
pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy
for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2.
The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If
sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was
100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I,
and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on
internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.
Keywords: Association rule mining | CBA | Expert system | Hot spots | Ripper algorithm | Pandemic influenza