GPU accelerated Chemical Text mining for relationship identification between chemical entities in heterogeneous environment
Mita A. Landge, K. Raja Rajeswari · 2016
Chemical Text mining techniques have been extensively used to reveal interesting patterns and relationships between proteins, genes, disease and drug from biomedical and chemical literature. Various chemical Text mining tools were proposed for mining chemical data from various chemical databases like PubMed, Drug-Bank, etc. The exponential increase in the generation and collection of data has led to the new era of information extraction and data analysis. Mining the huge and massive literature data on conventional general purpose systems can be a tedious task which is unable to meet high computational requirements of text mining. To accelerate this process parallel Text mining operations can be performed using Graphical Processing Unit (GPU). This paper provides the literature on various Chemical text mining tools and techniques. This paper also presents a scalable framework for high performance GPU accelerated Chemical Text mining for relationship identification between chemical entities on heterogeneous platform providing a method of balancing the workload between CPUs and GPUs for Maximum utilization of diverse commodity hardware.