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While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

Diacylglycerol acyltransferase (DGAT) activity is an essential enzymatic step in the formation of neutral lipid i.e., triacylglycerol in all living cells capable of accumulating storage lipid. Previously, we characterized an oleaginous yeast Candida tropicalis SY005 that yields storage lipid up to 58% under a specific nitrogen-stress condition, when the DGAT-specific transcript is drastically up-regulated. Here we report the identification, differential expression and function of two DGAT2 gene homologues--CtDGAT2a and CtDGAT2b of this C. tropicalis. Two protein isoforms are unique with respect to the presence of five additional stretches of amino acids, besides possessing three highly conserved motifs known in other reported DGAT2 enzymes. Moreover, the CtDGAT2a and CtDGAT2b are characteristically different in amino acid sequences and predicted protein structures. The CtDGAT2b isozyme was found to be catalytically 12.5% more efficient than CtDGAT2a for triacylglycerol production in a heterologous yeast system i.e., Saccharomyces cerevisiae quadruple mutant strain H1246 that is inherently defective in neutral lipid biosynthesis. The CtDGAT2b activity rescued the growth of transformed S. cerevisiae mutant cells, which are usually non-viable in the medium containing free fatty acids by incorporating them into triacylglycerol, and displayed preferential specificity towards saturated acyl species as substrate. Furthermore, we document that the efficiency of triacylglycerol production by CtDGAT2b is differentially affected by deletion, insertion or replacement of amino acids in five regions exclusively present in two CtDGAT2 isozymes. Taken together, our study characterizes two structurally novel DGAT2 isozymes, which are accountable for the enhanced production of storage lipid enriched with saturated fatty acids inherently in C. tropicalis SY005 strain as well as in transformed S. cerevisiae neutral lipid-deficient mutant cells. These two genes certainly will be useful for further investigation on the novel structure-function relationship of DGAT repertoire, and also in metabolic engineering for the enhanced production of lipid feedstock in other organisms.

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

The biomedical research community has been one of the earliest adopters of Semantic Web technologies and Linked Data principles. Several databases and knowledge bases are published and linked on the Web using these technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. During this age of data and knowledge explosion in biomedicine, Semantic Web technologies and the LSLOD cloud may provide a unique opportunity toward the integration of disparate biomedical data and knowledge stored in isolated repositories. However, in the current state of the LSLOD cloud, it is still very difficult for most biomedical researchers to query and integrate data and knowledge from multiple sources simultaneously. The semantic heterogeneity across the LSLOD cloud makes the task of serendipitously discovering implicit associations illusive.I hypothesize that a Semantic Web pattern-based query federation framework can aid in the integration of multiple disparate, heterogeneous biomedical data and knowledge sources for discovering novel implicit associations in biomedicine serendipitously. I detect and quantify the manifestations of semantic heterogeneity across biomedical ontologies and linked data sources in the LSLOD cloud. I develop the PhLeGrA (Linked Graph Analytics in Pharmacology) framework for heterogeneous biomedical data and knowledge integration, and association discovery. I demonstrate the utility of the PhLeGrA framework to generate a systems pharmacology network composed of drugs, proteins, pathways and phenotypes. In conjunction with this systems pharmacology network, PhLeGrA mines clinical data in spontaneous reporting systems and electronic health records for detecting adverse drug reactions that manifest due to multiple drug intake, and provides explanations on the underlying biological mechanisms.The findings presented in this research will make Semantic Web developers and publishers more aware of the architectural issues associated with mining the LSLOD cloud. The methods that I have developed should enable biomedical researchers to query and integrate data and knowledge from multiple, heterogeneous LSLOD sources for solving complex biomedical problems.

Diacylglycerol acyltransferase (DGAT) activity is an essential enzymatic step in the formation of neutral lipid i.e., triacylglycerol in all living cells capable of accumulating storage lipid. Previously, we characterized an oleaginous yeast Candida tropicalis SY005 that yields storage lipid up to 58% under a specific nitrogen-stress condition, when the DGAT-specific transcript is drastically up-regulated. Here we report the identification, differential expression and function of two DGAT2 gene homologues- CtDGAT2a and CtDGAT2b of this C. tropicalis. Two protein isoforms are unique with respect to the presence of five additional stretches of amino acids, besides possessing three highly conserved motifs known in other reported DGAT2 enzymes. Moreover, the CtDGAT2a and CtDGAT2b are characteristically different in amino acid sequences and predicted protein structures. The CtDGAT2b isozyme was found to be catalytically 12.5% more efficient than CtDGAT2a for triacylglycerol production in a heterologous yeast system i.e., Saccharomyces cerevisiae quadruple mutant strain H1246 that is inherently defective in neutral lipid biosynthesis. The CtDGAT2b activity rescued the growth of transformed S. cerevisiae mutant cells, which are usually non-viable in the medium containing free fatty acids by incorporating them into triacylglycerol, and displayed preferential specificity towards saturated acyl species as substrate. Furthermore, we document that the efficiency of triacylglycerol production by CtDGAT2b is differentially affected by deletion, insertion or replacement of amino acids in five regions exclusively present in two CtDGAT2 isozymes. Taken together, our study characterizes two structurally novel DGAT2 isozymes, which are accountable for the enhanced production of storage lipid enriched with saturated fatty acids inherently in C. tropicalis SY005 strain as well as in transformed S. cerevisiae neutral lipid-deficient mutant cells. These two genes certainly will be useful for further investigation on the novel structure-function relationship of DGAT repertoire, and also in metabolic engineering for the enhanced production of lipid feedstock in other organisms.

The metadata about scientific experiments published in online repositories have been shown to suffer from a high degree of representational heterogeneity---there are often many ways to represent the same type of information, such as a geographical location via its latitude and longitude. To harness the potential that metadata have for discovering scientific data, it is crucial that they be represented in a uniform way that can be queried effectively. One step toward uniformly-represented metadata is to normalize the multiple, distinct field names used in metadata (e.g., lat lon, lat and long) to describe the same type of value. To that end, we present a new method based on clustering and embeddings (i.e., vector representations of words) to align metadata field names with ontology terms. We apply our method to biomedical metadata by generating embeddings for terms in biomedical ontologies from the BioPortal repository. We carried out a comparative study between our method and the NCBO Annotator, which revealed that our method yields more and substantially better alignments between metadata and ontology terms.

While the biomedical community has published several \"open data\" sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 publicly available biomedical linked data graphs into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

Objective Although social and environmental factors are central to provider patient interactions, the data that reflect these factors can be incomplete, vague, and subjective. We sought to create a conceptual framework to describe and classify data about presence, the domain of interpersonal connection in medicine. Methods Our top down approach for ontology development based on the concept of relationality included 1) broad survey of social sciences literature and systematic literature review of more than 20,000 articles around interpersonal connection in medicine, 3) relational ethnography of clinical encounters (5 pilot, 27 full) and 4) interviews about relational work with 40 medical and nonmedical professionals. We formalized the model using the Web Ontology Language in the Protege ontology editor. We iteratively evaluated and refined the Presence Ontology through manual expert review and automated annotation of literature. Results and Discussion The Presence Ontology facilitates the naming and classification of concepts that would otherwise be vague. Our model categorizes contributors to healthcare encounters and factors such as Communication, Emotions, Tools, and Environment. Ontology evaluation indicated that Cognitive Models (both patients explanatory models and providers caregiving approaches) influenced encounters and were subsequently incorporated. We show how ethnographic methods based in relationality can aid the representation of experiential concepts (e.g., empathy, trust). Our ontology could support informatics applications to improve healthcare such annotation of videotaped encounters, clinical instruments to measure presence, or EHR based reminders for providers. Conclusion The Presence Ontology provides a model for using ethnographic approaches to classify interpersonal data.

The United States is in the midst of an opioid epidemic with recent estimates indicating that more than 130 people die every day due to drug overdose. The over-prescription and addiction to opioid painkillers, heroin, and synthetic opioids, has led to a public health crisis and created a huge social and economic burden. Statistical learning methods that use data from multiple clinical centers across the US to detect opioid over-prescribing trends and predict possible opioid misuse are required. However, the semantic heterogeneity in the representation of clinical data across different centers makes the development and evaluation of such methods difficult and non-trivial. We create the Opioid Drug Knowledge Graph (ODKG) -- a network of opioid-related drugs, active ingredients, formulations, combinations, and brand names. We use the ODKG to normalize drug strings in a clinical data warehouse consisting of patient data from over 400 healthcare facilities in 42 different states. We showcase the use of ODKG to generate summary statistics of opioid prescription trends across US regions. These methods and resources can aid the development of advanced and scalable models to monitor the opioid epidemic and to detect illicit opioid misuse behavior. Our work is relevant to policymakers and pain researchers who wish to systematically assess factors that contribute to opioid over-prescribing and iatrogenic opioid addiction in the US.

Clinicians face several significant barriers to search and synthesize accurate, succinct, updated, and trustworthy medical information from several literature sources during the practice of medicine and patient care. In this talk, we will be presenting our research behind the development of a Focused Clinical Search Service, powered by a Healthcare Knowledge Graph, to interpret the query intent behind clinical search queries and retrieve relevant medical snippets from a diverse corpus of medical literature.