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Search Results: 1 - 10 of 297741 matches for " Wild J. "
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Grand challenges for cheminformatics
David J Wild
Journal of Cheminformatics , 2009, DOI: 10.1186/1758-2946-1-1
Abstract: But therein lies a problem. We have impacted a diverse group of people and domains that defy a single categorization. We have done so with a small, scattered academic presence in very different environments (current cheminformaticians sit with various levels of comfort in Information Studies, Informatics, Computer Science, Chemistry, and Pharmacy departments among others). We only settled on a name for the field less than a decade ago, and we still struggle with its spelling. Much research has been carried out in pharmaceutical companies or with industry support, so is not as visible or accessible as comparable research in related fields such as bioinformatics. Unlike biology, academic chemistry traditionally has a much different scope than industrial and pharmaceutical chemistry.So are we all about Library & Information Science or Computational Drug Discovery? Is our work theoretical or pragmatic? Are our "customers" biologists or chemists? Do we have an academic or industry focus? Historically, the answer to these questions is "both". Those of us who care about the future of this field need to honestly ask whether cheminformatics is a bona-fide discipline in its own right, or whether it is a loose collection of practitioners without a single, definable focus. How we answer this question will determine the approach we should take to research, funding, publication, and education in the future.We firmly believe that Cheminformatics needs to be centered and cemented as a true, distinct discipline, with explicit funding support, dedicated journals and conferences, more visibility in the academic community, strengthened ties to related fields, and a mechanism to educate the next generation of researchers. To maximize our chances of this happening, we need to be clear about what we can contribute to the world in the future, we need to apply ourselves to some of the grand challenges of the 21st century, and we need to find our place in relation to our sister field of bioi
A Matrix Formulation of Einstein's Vacuum Field Equations
Walter J. Wild
Physics , 1998,
Abstract: We develop a correspondence between arbitrary tensors and matrices based on the use of Kronecker products and associated identities. Utilizing the rules of matrix differentiation we derive the vacuum Einstein field equations as a differential-matrix equation. This formulation may facilitate their efficient use in numerical relativistic models.
On locating statistics in the world of finding out
Chris J. Wild
Statistics , 2015,
Abstract: This paper attempts to situate statistics in relation to qualitative research methods and other means of "finding out". It compares and contrasts aspects of qualitative research methods and statistical inquiry and attempts to answer the question of whether and how elements of qualitative research methods should be included in statistics teaching.
PubChemSR: A search and retrieval tool for PubChem
Junguk Hur, David J Wild
Chemistry Central Journal , 2008, DOI: 10.1186/1752-153x-2-11
Abstract: PubChemSR (Search and Retrieve) is a freely available desktop application written for Windows using Microsoft .NET that is designed to assist scientists in search, retrieval and organization of chemical and biological data from the PubChem database. It employs SOAP web services made available by NCBI for extraction of information from PubChem.The program supports a wide range of searching techniques, including queries based on assay or compound keywords and chemical substructures. Results can be examined individually or downloaded and exported in batch for use in other programs such as Microsoft Excel. We believe that PubChemSR makes it straightforward for researchers to utilize the chemical, biological and screening data available in PubChem. We present several examples of how it can be used.Recent years have seen an explosion in the amount of chemical and related biological information in freely-accessible databases [1,2] The most widely known of these is PubChem [3], a repository of over 40 million chemical substances (at the time of writing) with associated property, literature reference and biological activity information. In addition to being a resource of information about compounds, this database is the primary repository for High Throughput Screening results generated by the Molecular Libraries Screening Centers Network (MLSCN) [4], part of the NIH Roadmap.While PubChem has a straightforward web-based user interface for searching, it is quite limited in its facilities for download and processing of search results. For example, one can download data for a particular PubChem entry in XML [5] and a few other formats, but it is not possible to download aggregate search results in a manner that is straightforward for a non-computational scientist. Yet the greatest utility of this information is clearly in aggregate: with structural information for compounds tested in a particular bioassay, one can create a QSAR model; by comparing compounds active in one assay w
Fast rule-based bioactivity prediction using associative classification mining
Yu Pulan,Wild David J
Journal of Cheminformatics , 2012, DOI: 10.1186/1758-2946-4-29
Abstract: Relating chemical features to bioactivities is critical in molecular design and is used extensively in the lead discovery and optimization process. A variety of techniques from statistics, data mining and machine learning have been applied to this process. In this study, we utilize a collection of methods, called associative classification mining (ACM), which are popular in the data mining community, but so far have not been applied widely in cheminformatics. More specifically, classification based on predictive association rules (CPAR), classification based on multiple association rules (CMAR) and classification based on association rules (CBA) are employed on three datasets using various descriptor sets. Experimental evaluations on anti-tuberculosis (antiTB), mutagenicity and hERG (the human Ether-a-go-go-Related Gene) blocker datasets show that these three methods are computationally scalable and appropriate for high speed mining. Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM) methods, and produce highly interpretable models.
Discovering Associations in Biomedical Datasets by Link-based Associative Classifier (LAC)
Pulan Yu, David J. Wild
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0051018
Abstract: Associative classification mining (ACM) can be used to provide predictive models with high accuracy as well as interpretability. However, traditional ACM ignores the difference of significances among the features used for mining. Although weighted associative classification mining (WACM) addresses this issue by assigning different weights to features, most implementations can only be utilized when pre-assigned weights are available. In this paper, we propose a link-based approach to automatically derive weight information from a dataset using link-based models which treat the dataset as a bipartite model. By combining this link-based feature weighting method with a traditional ACM method–classification based on associations (CBA), a Link-based Associative Classifier (LAC) is developed. We then demonstrate the application of LAC to biomedical datasets for association discovery between chemical compounds and bioactivities or diseases. The results indicate that the novel link-based weighting method is comparable to support vector machine (SVM) and RELIEF method, and is capable of capturing significant features. Additionally, LAC is shown to produce models with high accuracies and discover interesting associations which may otherwise remain unrevealed by traditional ACM.
Assessing Drug Target Association Using Semantic Linked Data
Bin Chen,Ying Ding,David J. Wild
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002574
Abstract: 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 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.
Improving integrative searching of systems chemical biology data using semantic annotation
Bin Chen, Ying Ding, David J Wild
Journal of Cheminformatics , 2012, DOI: 10.1186/1758-2946-4-6
Abstract: We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology.Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl webcite. The document is available at http://chem2bio2owl.wikispaces.com webcite.Recent efforts [1-3] in the Semantic web have involved conversion of various chemical and biological data sources into semantic formats (e.g., RDF, OWL) and linked them into very large networks. The number of bubbles in Linked Open Data (LOD) [4] has expanded rapidly from 12 in 2007 to 203 in 2010. This richly linked data allows answering of complex scientific questions using the SPARQL query language [5], finding paths among objects [6], and ranking associations of different entities [7,8]. Our previous work on Chem2Bio2RDF [3] offers a framework to data mine systems chemical biology and chemogenomics data, as exemplified by the examples given in our paper: compound selection in polypharmacology, multiple pathway inhibitor identification and adverse drug reaction - pathway mapping. However, without an ontology and associated annotation, the utility of the resource is semantically very limited - for example results cannot be refined based on criteria of the type of relationship between entities (e.g., activation or inhibition between compound and protein). Even when it is possible to create a SPARQL query, the lack of ontology increases the complexity of the qu
CUTLASS HF radar observations of high-latitude azimuthally propagating vortical currents in the nightside ionosphere during magnetospheric substorms
J. A. Wild,T. K. Yeoman
Annales Geophysicae (ANGEO) , 2003,
Abstract: High-time resolution CUTLASS observations and ground-based magnetometers have been employed to study the occurrence of vortical flow structures propagating through the high-latitude ionosphere during magnetospheric substorms. Fast-moving flow vortices (~800 m s-1) associated with Hall currents flowing around upward directed field-aligned currents are frequently observed propagating at high speed (~1 km s-1) azimuthally away from the region of the ionosphere associated with the location of the substorm expansion phase onset. Furthermore, a statistical analysis drawn from over 1000 h of high-time resolution, nightside radar data has enabled the characterisation of the bulk properties of these vortical flow systems. Their occurrence with respect to substorm phase has been investigated and a possible generation mechanism has been suggested. Key words: Ionosphere (auroral ionosphere; electric fields and currents) · Magnetospheric physics (storms and substorms)
The Patchwork Text Assessment – An Integral Component of Constructive Alignment Curriculum Methodology to Support Healthcare Leadership Development
Leigh J. A.,Rutherford J.,Wild J.,Cappleman J.
Journal of Education and Training Studies , 2013, DOI: 10.11114/jets.v1i1.83
Abstract: Background: A responsive and innovative postgraduate programme curriculum that produces an effective and competent multi professional healthcare leader whom can lead within the United Kingdom (UK) and international healthcare context offers a promising approach to contributing towards the challenging global healthcare agenda Aims: The aim of the study is to evaluate the impact of utilising constructive alignment curricular methodology incorporating the Patchwork Text Assessment on the healthcare leadership development of UK and international postgraduate students Design: Case study design, incorporating Kirkpatrick's Five Levels of Evaluation Model Settings and Participants: 12 post graduate students (multi-professional, UK and international) studying on a healthcare leadership and management programme at one UK University in the North West of England. Methods: Rretrieval of the critical commentary produced and submitted by students as part of the Patchwork Text Assessment process Data Analysis: Thematic content analysis approach Results: Four key themes emerged demonstrating how the success of constructive alignment and the Patchwork Text Assessment in promoting deep learning for UK and international postgraduate healthcare leadership students is underpinned by principles of good practice and these include: a) Curriculum planners incorporating work based learning activities into the generated learning activities b) Curriculum planners creating the best learning environment so the student can achieve the learning activities c) Providing the learning activities that reflect the real world of healthcare leadership d) Providing students with opportunities to contextualise theory and practice through relevant patchwork activity and learning activities e) Equipping students with the transferable postgraduate skills (through learning activities and patch working) to embark on a journey of lifelong learning and continuous professional development f) Targeting the postgraduate programme /module intended learning outcomes and assessment patches against contemporary leadership qualities frameworks g) Providing students with opportunities to reflect in multi- professional groups that remain constant in terms of facilitator and supervisor h) Creating the learning opportunities for students to apply their learning to their own healthcare organisation
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