BACKGROUND: LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. RESULTS: Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. CONCLUSIONS: The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-Computer Interaction (HCI) and for the oversight of app-based public health surveillance.

MOTIVATION: Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. RESULTS: Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists. AVAILABILITY AND IMPLEMENTATION: Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. RESULTS: In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks. CONCLUSIONS: We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

BACKGROUND: Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download. RESULTS: We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis. CONCLUSION: The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.

We discuss the Data Science program at Indiana University, which is offered in both traditional residential and online formats. We describe Data Science, our chosen curriculum and its motivation. We describe experience in online delivery for both traditional lectures and online programming laboratories, and discuss implications for the technology used.

Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simultaneously. In this paper, we focus on studying the collective classification problem in heterogeneous networks, which involves multiple types of data objects interconnected by multiple types of links. Intuitively, two objects are correlated if they are linked by many paths in the network. By considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects. We introduce the concept of meta-path based dependencies among objects, where a meta path is a path consisting a certain sequence of linke types. We show that the quality of collective classification results strongly depends upon the meta paths used. To accommodate the large network size, a novel solution, called HCC (meta-path based Heterogenous Collective Classification), is developed to effectively assign labels to a group of instances that are interconnected through different meta-paths. The proposed HCC model can capture different types of dependencies among objects with respect to different meta paths. Empirical studies on real-world networks demonstrate that effectiveness of the proposed meta path-based collective classification approach.

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.

MOTIVATION: Networks to predict protein pharmacology can be created using ligand similarity or using known bioassay response profiles of ligands. Recent publications indicate that similarity methods can be highly accurate, but it has been unclear how similarity methods compare to methods that use bioassay response data directly. RESULTS: We created protein networks based on ligand similarity (Similarity Ensemble Approach or SEA) and ligand bioassay response-data (BARD) using 155 Pfizer internal BioPrint assays. Both SEA and BARD successfully cluster together proteins with known relationships, and predict some non-obvious relationships. Although the approaches assess target relations from different perspectives, their networks overlap considerably (40% overlap of the top 2% of correlated edges). They can thus be considered as comparable methods, with a distinct advantage of the similarity methods that they only require simple computations (similarity of compound) as opposed to extensive experimental data. CONTACTS: [email protected]; [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating information pertaining to new chemical compounds, effectively creating evidence paths relating the compounds to genes, diseases and so on. In this paper we examine the utility of automatically inferring new compound-disease associations (and thus new links in the network) based on semantically marked-up versions of these evidence paths, rule-sets and inference engines. RESULTS: Through the implementation of a semantic inference algorithm, rule set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have created a new tool called Chemogenomic Explorer that uses networks of ontologically annotated RDF statements along with deductive reasoning tools to infer new associations between the query structure and genes and diseases from WENDI results. The tool then permits interactive clustering and filtering of these evidence paths. CONCLUSIONS: We present a new aggregate approach to inferring links between chemical compounds and diseases using semantic inference. This approach allows multiple evidence paths between compounds and diseases to be identified using a rule-set and semantically annotated data, and for these evidence paths to be clustered to show overall evidence linking the compound to a disease. We believe this is a powerful approach, because it allows compound-disease relationships to be ranked by the amount of evidence supporting them.

SUMMARY Visualization of large‐scale high dimensional data is highly valuable for data analysis facilitating scientific discovery in many fields. We present PubChemBrowse, a customized visualization tool for cheminformatics research. It provides a novel 3D data point browser that displays complex properties of massive data on commodity clients. As in Geographic Information System browsers for Earth and Environment data, chemical compounds with similar properties are nearby in the browser. PubChemBrowse is built around in‐house high performance parallel Multi‐dimensional scaling and Generative topographic mapping services and supports fast interaction with an external property database. These properties can be overlaid on 3D mapped compound space or queried for individual points. We prototype the integration with Chem2Bio2RDF system using SPARQL endpoint to access over 20 publicly accessible bioinformatics databases. We describe our design and implementation of the integrated PubChemBrowse application and outline its use in drug discovery. The same core technologies are generally applicable to develop high performance scientific data browsing systems for other applications. Copyright © 2011 John Wiley & Sons, Ltd.

Visualization of large-scale high dimensional data tool is highly valuable for scientific discovery in many fields. We presentPubChemBrowse, acustomizedvisualizationtoolfor cheminformatics research. It provides a novel 3D data point browser that displays complex properties of massive data on commodity clients. As in GIS browsers for Earth and Environment data, chemical compounds with similar properties are nearby in the browser. PubChemBrowse is built around in-househighperformanceparallel MDS(Multi-Dimensional Scaling) and GTM (Generative Topographic Mapping) services andsupports fast interaction with anexternalproperty database. These properties can be overlaid on 3D mapped compound space or queried for individual points. We prototype use with Chem2Bio2RDF system using SPARQLquery language to access over 20 publicly accessible bioinformatics databases. We describe our design and implementation of the integrated PubChemBrowse application and outline its use in drug discovery. The same core technologies can be used to develop similar high dimensional browsers in other scientific areas.

The Chem2Bio2RDF portal is a Linked Open Data (LOD) portal for systems chemical biology aiming for facilitating drug discovery. It converts around 25 different datasets on genes, compounds, drugs, pathways, side effects, diseases, and MEDLINE/PubMed documents into RDF triples and links them to other LOD bubbles, such as Bio2RDF, LODD and DBPedia. The portal is based on D2R server and provides a SPARQL endpoint, but adds on several unique features such as RDF faceted browser, user-friendly SPARQL query generator, MEDLINE/PubMed cross validation service, and Cytoscape visualization plugin. Three use cases demonstrate the functionality and usability of this portal. The portal is available at http://chem2bio2rdf.org.

Recent years have seen a huge increase in the amount of publicly-available information relevant to drug discovery, including online databases of compound and bioassay information; scholarly publications linking compounds with genes, targets and diseases; and predictive models that can suggest new links between compounds, genes, targets and diseases. However, there is a lack of tools and methods to integrate this information, and in particular to look for pertinent knowledge and relationships across multiple sources. At Indiana University we are tackling this problem by applying aggregative data mining tools and semantic web technologies including using an extensive web service infrastructure, RDF networks and inference engines, ontologies, and automated extraction of information from scholarly literature.

BACKGROUND: Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited RESULTS: We have created a single repository called Chem2Bio2RDF by aggregating data from multiple chemogenomics repositories that is cross-linked into Bio2RDF and LODD. We have also created a linked-path generation tool to facilitate SPARQL query generation, and have created extended SPARQL functions to address specific chemical/biological search needs. We demonstrate the utility of Chem2Bio2RDF in investigating polypharmacology, identification of potential multiple pathway inhibitors, and the association of pathways with adverse drug reactions. CONCLUSIONS: We have created a new semantic systems chemical biology resource, and have demonstrated its potential usefulness in specific examples of polypharmacology, multiple pathway inhibition and adverse drug reaction--pathway mapping. We have also demonstrated the usefulness of extending SPARQL with cheminformatics and bioinformatics functionality.

Recent trends in life science and the pharmaceutical industry indicate that the drug discovery process could benefit greatly from both web service and semantic web technology. While web services provide a platform to allow different applications to interact with each other programmatically, semantic web developments are aiming at making web content machine-understandable and meta-level exploitation for data and computation possible. In this paper we demonstrate an automatic drug discovery workflow generation tool using semantic web service technologies. More specifically, we extend standard web services with semantic annotations specified in the domain ontology to enable semantic level interoperability. We also implement a service composition algorithm using linear planning approach, and using such an algorithm meaningful workflows for drug discovery could be generated automatically.

BACKGROUND: The web has seen an explosion of chemistry and biology related resources in the last 15 years: thousands of scientific journals, databases, wikis, blogs and resources are available with a wide variety of types of information. There is a huge need to aggregate and organise this information. However, the sheer number of resources makes it unrealistic to link them all in a centralised manner. Instead, search engines to find information in those resources flourish, and formal languages like Resource Description Framework and Web Ontology Language are increasingly used to allow linking of resources. A recent development is the use of userscripts to change the appearance of web pages, by on-the-fly modification of the web content. This opens possibilities to aggregate information and computational results from different web resources into the web page of one of those resources. RESULTS: Several userscripts are presented that enrich biology and chemistry related web resources by incorporating or linking to other computational or data sources on the web. The scripts make use of Greasemonkey-like plugins for web browsers and are written in JavaScript. Information from third-party resources are extracted using open Application Programming Interfaces, while common Universal Resource Locator schemes are used to make deep links to related information in that external resource. The userscripts presented here use a variety of techniques and resources, and show the potential of such scripts. CONCLUSION: This paper discusses a number of userscripts that aggregate information from two or more web resources. Examples are shown that enrich web pages with information from other resources, and show how information from web pages can be used to link to, search, and process information in other resources. Due to the nature of userscripts, scientists are able to select those scripts they find useful on a daily basis, as the scripts run directly in their own web browser rather than on the web server. This flexibility allows the scientists to tune the features of web resources to optimise their productivity.