Biomolecular Event Extraction using Natural Language Processing

Biomedical research and discoveries are communicated through scholarly publications and this literature is voluminous, rich in scientific text and growing exponentially by the day. Biomedical journals publish nearly three thousand research articles daily, making literature search a challenging proposition for researchers. Biomolecular events involve genes, proteins, metabolites, and enzymes that provide invaluable insights into biological processes and explain the physiological functional mechanisms. Text mining (TM) or extraction of such events automatically from big data is the only quick and viable solution to gather any useful information. Such events extracted from biological literature have a broad range of applications like database curation, ontology construction, semantic web search and interactive systems. However, automatic extraction has its challenges on account of ambiguity and the diverse nature of natural language and associated linguistic occurrences like speculations, negations etc., which commonly exist in biomedical texts and lead to erroneous elucidation. In the last decade, many strategies have been proposed in this field, using different paradigms like Biomedical natural language processing (BioNLP), machine learning and deep learning. Also, new parallel computing architectures like graphical processing units (GPU) have emerged as possible candidates to accelerate the event extraction pipeline. This paper reviews and provides a summarization of the key approaches in complex biomolecular big data event extraction tasks and recommends a balanced architecture in terms of accuracy, speed, computational cost, and memory usage towards developing a robust GPU-accelerated BioNLP system

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Not AvailableEleven cultivars were evaluated for fresh yield (10 environments), curing per cent, cucumin and dry yield (five environments) across India, four each in North and South India and two in North East India, ranging from 43 to 893 m above mean sea level. Combined analyses showed significant differences among cultivars, environments, and cultivar by environment interactions for yield, curing per cent and curcumin contents. A large proportion (70.8%) of variation on fresh yield was attributed to environments; however, for curing per cent, curcumin content and dry yield, genotype effect accounted for 31.2%, 17.7% and 15.7% of variation, respectively. Megha Turmeric was the most stable for fresh yield with above average yield per plant across all environments. Rajendra Sonia was performing well at specific locations as the fresh yield was high and was highly responsive to favorable environments. Results on curcumin and curing per cent showed that, IISR Kedaram performed consistently across five environments with regression values almost equal to one and non-significant deviation from regression was adjudged to be the most stable cultivar for curcumin production. High curcumin cultivar Narendra Tumeric-1 was least responsive at environments with regression values less than one and significant deviation from regression. Megha Turmeric, IISR Prathiba and IISR Kedaram showed high stability for dry yield across environments. Three varieties, Megha Turmeric, IISR Kedaram and IISR Prathiba could serve as a good genetic source for stability in breeding programs for high dry yield and curcumin content.Not Availabl