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An automated reasoning framework for translational research.

ABSTRACT: In this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools. In order to demonstrate the usefulness and power of the proposed framework, we present its application to Genome-Wide Association Studies, and we use it to reproduce a portion of the initial analysis performed on the well-known WTCCC dataset. Finally, we describe a computational system we are developing, aimed at assisting translational research. The system, based on the proposed model, will be able to automatically plan and perform knowledge discovery steps, to keep track of the inferences performed, and to explain the obtained results.

SUBMITTER: Riva A

PROVIDER: S-EPMC2878845 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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An automated reasoning framework for translational research.

Riva Alberto A Nuzzo Angelo A Stefanelli Mario M Bellazzi Riccardo R

Journal of biomedical informatics 20091118 3

In this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools. In order to demonstrate t ...[more]

PMID: 19931420

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Project description:BackgroundWith the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.MethodologyIn this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.Conclusion/significanceTo evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

Project description:BackgroundNormalisation Process Theory (NPT) is frequently used to inform qualitative research that aims to explain and evaluate processes that shape late-stage translation of innovations in the organisation and delivery of healthcare. A coding manual for qualitative researchers using NPT will facilitate transparent data analysis processes and will also reduce the cognitive and practical burden on researchers.Objectives(a) To simplify the theory for the user. (b) To describe the purposes, methods of development, and potential application of a coding manual that translates normalisation process theory (NPT) into an easily usable framework for qualitative analysis. (c) To present an NPT coding manual that is ready for use.MethodQualitative content analysis of papers and chapters that developed normalisation process theory, selection and structuring of theory constructs, and testing constructs against interview data and published empirical studies using NPT.ResultsA coding manual for NPT was developed. It consists of 12 primary NPT constructs and conforms to the Context-Mechanism-Outcome configuration of realist evaluation studies. Contexts are defined as settings in which implementation work is done, in which strategic intentions, adaptive execution, negotiating capability, and reframing organisational logics are enacted. Mechanisms are defined as the work that people do when they participate in implementation processes and include coherence-building, cognitive participation, collective action, and reflexive monitoring. Outcomes are defined as effects that make visible how things change as implementation processes proceed and include intervention mobilisation, normative restructuring, relational restructuring, and sustainment.ConclusionThe coding manual is ready to use and performs three important tasks. It consolidates several iterations of theory development, makes the application of NPT simpler for the user, and links NPT constructs to realist evaluation methods. The coding manual forms the core of a translational framework for implementation research and evaluation.

Project description:Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Designing effective individualized therapies for GBM requires quality fresh tissue specimens, and a comprehensive molecular profile of this highly heterogenous neoplasm. Novel neuro-surgical approaches, such as the automated resection NICO Myriad™ system, are increasingly used by neurosurgeons to better reach the invasive front of tumors. However, no information exists on how harvesting GBM tissue using this approach may impact the translational research value of the sample. Here, we set out to characterize matched specimens from 15 patients, where one tissue sample was obtained using traditional tumor de-bulking (herein referred to as "en bloc" sample), and the other sample was obtained using the MyriadTM System (herein referred to as "Myriad" sample). We investigated the fidelity of patient derived xenografts (PDXs) for each sample type to the corresponding human tissues and evaluated the added value of sequencing both samples for each patient. Matched en bloc and Myriad samples processed in parallel, were subjected to the following assays: cell viability, self-renewal, in vivo tumorigenicity using an orthotopic model of glioma, genomic sequencing, and pharmacological testing using PI3K-MTOR pathway inhibitors. Our results demonstrate that primary GBM cultures derived from matched specimens grew at similar rates (correlation coefficient R = 0.72), generated equivalent number of neurospheres, and had equivalent tumorigenic potential in vivo (mouse survival correlation coefficient R = 0.93). DNA Sequencing using the Illumina tumor panel amplicons revealed over 70% concordance in non-synonymous mutations between matched human GBM specimens. PDX genomic profiles were also highly concordant with the corresponding patient tissues (>70%). RNA sequencing of paired GBM samples revealed unique genomic variants and differential gene expression between the en bloc and Myriad specimens, with the former molecularly resembling the "tumor core" and the latter resembling the "invasive tumor front" signature. Functionally, we show that primary-derived GBM cells-obtained after fresh specimen's dissociation-are more effectively growth-inhibited by co-targeting non-overlapping mutations enriched in each sample type, suggesting that profiling both specimens more adequately capture the molecular heterogeneity of GBM and may enhance the design accuracy and efficacy of individualized therapies.

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An automated reasoning framework for translational research.

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An automated reasoning framework for translational research.

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