Opinion Mining and Sentiment Analysis of Online Drug Reviews as a Pharmacovigilance Technique

Pharmacovigilance is the science that focuses on identification and characterization of adverse effects of medications in populations when released to market. The focus of this paper is to study the prospects of exploiting drug related online reviews contributed by social media groups for finding the adverse effects of drugs using opinion mining and sentiment analysis. The experiences and opinions related to drug adverse reactions by patients or other contributors in these forums can be mined and analyzed as a facilitator for pharmacovigilance. This review paper highlights the usability of opinion mining and sentiment analysis as one of the approaches for pharmacovigilance. DOI: 10.17762/ijritcc2321-8169.150711

A Survey of Biological Entity Recognition Approaches

There has been growing interest in the task of Named Entity Recognition (NER) and a lot of research has been done in this direction in last two decades. Particularly, a lot of progress has been made in the biomedical domain with emphasis on identifying domain-specific entities and often the task being known as Biological Named Entity Recognition (BER). The task of biological entity recognition (BER) has been proved to be a challenging task due to several reasons as identified by many researchers. The recognition of biological entities in text and the extraction of relationships between them have paved the way for doing more complex text-mining tasks and building further applications. This paper looks at the challenges perceived by the researchers in BER task and investigates the works done in the domain of BER by using the multiple approaches available for the task

Ontology Building: An Integrative View of Methodologies

Ontologies are being developed and used in many disciplines now a day and they have become a key tool of data integration and knowledge representation in different domains of interest. The ontology building process identifies the stages through which the ontology should go through during its development. There is a certain set of activities to be performed in each stage of the ontology development process and different methodologies have been proposed by researchers for formalizing the different stages. The present paper investigates the most representative methodologies used in the ontology development to look at the different activities that are performed during the process of ontology development. The paper further attempts to provide an integrative view of the most representative methodologies used in the ontology development to look at the set of different activities that can be performed during the process of the ontology development. DOI: 10.17762/ijritcc2321-8169.15076

Text mining processing pipeline for semi structured data D3.3

Unstructured and semi-structured cohort data contain relevant information about the health condition of a patient, e.g., free text describing disease diagnoses, drugs, medication reasons, which are often not available in structured formats. One of the challenges posed by medical free texts is that there can be several ways of mentioning a concept. Therefore, encoding free text into unambiguous descriptors allows us to leverage the value of the cohort data, in particular, by facilitating its findability and interoperability across cohorts in the project.Named entity recognition and normalization enable the automatic conversion of free text into standard medical concepts. Given the volume of available data shared in the CINECA project, the WP3 text mining working group has developed named entity normalization techniques to obtain standard concepts from unstructured and semi-structured fields available in the cohorts. In this deliverable, we present the methodology used to develop the different text mining tools created by the dedicated SFU, UMCG, EBI, and HES-SO/SIB groups for specific CINECA cohorts

ProKinO: An Ontology for Integrative Analysis of Protein Kinases in Cancer

Protein kinases are a large and diverse family of enzymes that are genomically altered in many human cancers. Targeted cancer genome sequencing efforts have unveiled the mutational profiles of protein kinase genes from many different cancer types. While mutational data on protein kinases is currently catalogued in various databases, integration of mutation data with other forms of data on protein kinases such as sequence, structure, function and pathway is necessary to identify and characterize key cancer causing mutations. Integrative analysis of protein kinase data, however, is a challenge because of the disparate nature of protein kinase data sources and data formats., where the mutations are spread over 82 distinct kinases. We also provide examples of how ontology-based data analysis can be used to generate testable hypotheses regarding cancer mutations.

Muscle-Eye-Brain Disease; a Rare Form of Syndromic Congenital Muscular Dystrophy

Congenital muscular dystrophy (CMD) is a heterogeneous group of disorders characterized by muscular hypotonia since birth and the histologic features of muscular dystrophy. Syndromic congenital muscular dystrophies are clinically similar autosomal recessive disorders characterized by congenital muscular dystrophy, lissencephaly, and eye anomalies. We present a case of a rare form of syndromic congenital muscular dystrophy in an eight year old girl, born of first- degree consanguinity. She had: global developmental delay; a seizure disorder; hypotonia; progressive muscle contractures including bilateral symmetrical flexion contractures of hips, knees, equinus contracture and thoracolumbar scoliosis; diminished deep tendon reflexes: bilateral premature cataract; pseudophakia; and nystagmus. The patient was also highly myopic. Based on clinical features, muscle biopsy and MRI of the brain, a diagnosis of muscle- eye- brain disease was made. Identification of these patients may help to prevent this crippling disorder in the future siblings of probands by utilizing genetic counselling and mutation analysis

FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration

Food Ontology: FoodOn consortium efforts facilitate food traceability A well-defined food ontology can reduce the barrier of food data harmonization problems world wide. Currently the lack of standard digital vocabulary in food prevents food traceability throughout increasingly global food exchange networks. In this study, Damion Dooley and William Hsiao from the University of British Columbia lead a group of international scientists who give a detailed description of FoodOn, a-consortium-driven project to build a comprehensive food ontology that covers vocabulary from farm to fork. It addresses current gaps in food terminology and focuses on human and domesticated animal food description. It can be foreseen that employing FoodOn vocabulary will speed up traceability of contaminated foods, and ultimately lead to positive economic and human health outcomes