Big Data Trends in Bioinformatics The amount of biological data available for both the academic community and industry has increased due to the rise of high throughput omics technologies, biotechnology, and health monitoring. This scenario demands efficient storage and analysis of the massive amount of data involved. This work aimed to investigate the scientific literature to map topics related to the use of Big Data in Bioinformatics. Due to the large number of relevant papers to inspect, we employed a threestep datadriven systematic approach. We performed a literature search and selected works related to the theme in three search bases (Scopus, ACM, and Web of Science). Then, we proceeded to a text mining step to analyze terms frequently used in the documents and prepare the documents to be inspected. Afterward, we performed a topic modeling using the LDA (Latent Dirichlet Allocation) algorithm. Twenty groups of topics were obtained and drawn in twenty trend topics, which show a revealing stateoftheart of the Big Data application in Bioinformatics.

A Big Data Approach for Health Data Information Retrieval In this paper we address the problem of analyzing biomedical data collection with the purpose of searching for semantic similarity among textual documents. In details, we leverage Word Embeddings models obtained by word2vec algorithm and a specific Big Data architecture for their management, defining an approach able to permit the retrieving of semantic similar texts among a huge biomedical text corpus. The proposed architecture has been developed with the purpose of improving a previous implementation, lowering the computational time and allowing in this way the use of the whole PubMed library as dataset, proving also the usability of this methodology in a real context.