Abstract
Breast cancer biomarkers have received a considerable attention for their key role in detecting and preventing the causes of breast cancer. In this paper, we study the impact of the published research related to the top genes most frequently mentioned in breast cancer articles. Our study helps governments and organizations by giving an idea about the number of studies that probably needs to be targeted in their support and funds. We perform time series analysis for the most frequently mentioned biomarkers in breast cancer articles. Constructing our time series dataset involves Information Retrieval (IR), Entity Recognition (ER) and Information Extraction (IE). We build a time series for the most frequently mentioned biomarkers in breast cancer articles by computing the number of published articles that mentioned these biomarkers over a periodic period of time. We use the autoregressive moving average (ARIMA) to build a model that helps in understanding and predicting a future number of articles in the time series of the breast cancer biomarkers.
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Jarada, T.N., Rokne, J., Alhajj, R. (2018). Time Series Analysis for the Most Frequently Mentioned Biomarkers in Breast Cancer Articles. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Highlighting the Importance of Big Data Management and Analysis for Various Applications. Studies in Big Data, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-60255-4_9
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DOI: https://doi.org/10.1007/978-3-319-60255-4_9
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