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  • Review Article
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Data integration: challenges for drug discovery

Nature Reviews Drug Discovery volume 4, pages 45–58 (2005)Cite this article

Key Points

  • The integration of data from many sources, especially new 'omic' platforms, is increasingly challenging not just because of increasing volume of data but because these data are highly diverse, expecially in a drug discovery enterprise that inherently draws on many disparate types of data.

  • The rise of the 'omics' disciplines means that data-driven research will need to be integrated with hypothesis-driven methods, as well as experimental with observational studies, and in general data integration will have cultural consequences, because it combines results from different scientific disciplines.

  • Even seemingly homogeneous data create integration challenges, beginning with cross-platform normalization, meta-analysis methods, multiple testing issues and new logical and statistical complexities that only increase with greater data heterogeneity.

  • The drug discovery enterprise, extending from molecules to human populations, depends on the combination of much more heterogenous data as well, and this carries its own challenges in terms of bridging widely varying scales and contexts.

  • Integration increases the dimensionality of data, both in terms of its arity or number of attributes, and in terms of its degree of connectivity; both are known to place stresses on data management, visualization and algorithmic analysis.

  • Scientific data integration entails making choices about the representation of data (for example, tables, matrices or graphs), each of which affords different integration and analysis techniques, and also requires careful attention to the syntax (format) and semantics (meaning) of the data; the latter can require sophisticated knowledge representation tools called ontologies.

  • Business data integration places more emphasis on decision support and on processes surrounding portfolio progression, which can be captured in a form of semantics called business rules.

  • Data mining is a form of integrative query that is highly exploratory and stresses pattern recognition and clue generation from heterogenous data sources, often presented on the Web.

Abstract

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies — challenges that could require significant methodological and even cultural changes in our approach to data.

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Figure 1: Data domains.
Figure 2: Data integration schemata.
Figure 3: Data normalization.
Figure 4: Data dimensionality.
Figure 5: Integration views.

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Acknowledgements

The author thanks M.Hurle, D. Crowther, S. Dear, J. Aaronson, P. Agarwal, M. Lutz and N. Odendahl for valuable contributions to this review.

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Authors and Affiliations

  1. Bioinformatics Division, Genetics Research, GlaxoSmithKline Pharmaceuticals, 709 Swedeland Road, P.O. Box 1539, King of Prussia, 19406, Pennsylvania, USA

    David B. Searls

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FURTHER INFORMATION

BioWisdom, Ltd.

Business Process Execution Language (BPEL)

CellSpace, Cellomics, Inc.

COG (Clusters of Orthologous Genes)

DAVID

Ensembl

EnsMart

FatiGO

GeneCards

GeneGo, Inc.

GeneLynx

GenMAPP

GoMiner

Integromics, SL

IPA, Ingenuity, Inc.

Life Sciences Identifier (LSID)

Open Biological Ontologies

OrthoMCL

PathwayAssist, StrataGene, Inc.

PubGene, Inc.

PubMatrix

RefSeq

RESOURCERER

SpotFire, Inc.

TOGA (TIGR Orthologous Gene Alignments)

UCSC Genome Browser

Glossary

MOORE'S LAW

A prediction made by engineer Gordon Moore in 1965 that the number of transistors per integrated circuit would increase exponentially with time. This has proven accurate, and moreover has been achieved at roughly constant cost per chip.

PRIMARY DATA

Data close to the experimental source; 'raw', unprocessed data.

DERIVED DATA

The result of processing, refining or interpreting data in any way.

META-ANALYSIS

The combination of results from several studies or experiments that bear on the same or similar questions or hypotheses. It entails the application of a variety of principles, methods and statistical techniques that are designed to ensure the validity of conclusions that might go beyond those supported by the individual studies.

DATA PROVENANCE

Information about the source and history of particular data items or sets, which is generally necessary to ensure their integrity, currency and reliability.

DECISION-SUPPORT SYSTEMS

A broadly defined term for computational systems and methods that aid humans in any decision-making process, generally for unstructured or semi-structured tasks.

NORMALIZATION

In data analysis, the processing of data that arises from distinct sources or experiments so as to allow their standardized evaluation and direct comparison, and which involves various transformations, corrections and weightings to accommodate systematic differences. In database design, the logical grouping of data into separate, well-factored tables according to rules that avoid known pitfalls of data redundancy and development of inconsistencies.

RESEARCH SYNTHESIS

The application of techniques ranging from systematic literature search to statistical meta-analysis compiling and summarizing evidence relating to a specific scientific question.

MULTIPLE TESTING

The testing of many independent null hypotheses in the same experiment, such that more stringent significance thresholds must be used to account for the multiple 'opportunities' for a chance false rejection. The simplest such adjustment is the classic Bonferroni correction.

ALEATORY UNCERTAINTY

Uncertainty arising from or associated with the inherent, irreducible, natural randomness of a system or process. The focus is therefore on the actual physical state of the system or process.

FREQUENTIST

Describing a conception of probability as being applicable solely to the (measurable) relative frequencies of random events. Also describes something/one that adheres to this approach.

EPISTEMIC UNCERTAINTY

Uncertainty associated with a model of a system or process and its parameters that arises from limitations on the data available or on causal understanding. The focus is on our state of knowledge of the system or process, or beliefs about it.

BAYESIAN

Describing an alternative to the frequentist approach that allows probabilities to apply to a broader range of propositions and beliefs. Its mathematical basis is Bayes' Rule, which supports initial assessments of the relative plausibility of hypotheses and their incremental refinement through additional observations. Also describes something/one that adheres to this approach.

DEMPSTER–SHAFER THEORY

A system for combining lines of evidence that deals in belief functions, which assign probabilities to sets of possibilities rather than single events, and provides formal rules for their combination.

ARITY

In mathematics, the number of arguments to a function or relation. In databases, therefore, the number of columns in a data table, or the number of attributes associated with an entity.

CURSE OF DIMENSIONALITY

The fanciful name given to statistical and algorithmic challenges posed by very high-dimensional spaces necessary to map data with many features, in which the resulting exponential growth in hypervolumes means that the data inevitably will be distributed ever more sparsely.

PRINCIPAL COMPONENTS ANALYSIS

(PCA). A mathematical technique to reduce a high-dimensional space to just a few orthogonal axes called principal components, which are defined in terms of weighted combinations of variables that maximize the variance of the data along those axes. PCA is typically used for clustering data and making its visualization easier.

CHEMOGENOMICS

The integration of genomics with chemistry — for instance, by using genomic readouts of compound action on a biological system, or adopting gene-family-centred approaches to drug discovery.

BAYESIAN NETWORK

A data structure that consists of a graph or network in which the nodes represent random variables and the connections describe dependencies between them. Such networks support various forms of probabilistic and causal inference based on several sources of data.

SYNTAX

The rules that govern the valid arrangements of symbols in some formal representation system. Examples include the grammar of a language, allowable structures of logical expressions or specifications of data formats.

SEMANTICS

The significance or meaning of symbols or arrangements of symbols in some formal representation system. Examples include the denotation of a sentence, the action specified by a statement in a computer program or the real-world referent of a data entity or attribute.

CONTROLLED VOCABULARY

A set of terms (such as gene names) that are unambiguous and non-redundant, and which are accepted as standards for the purpose of consistent database representation. They are often associated with synonym tables that can be used as an adjunct to database query or integration.

ONTOLOGY

In philosophy, the study of the nature of existence. In computer science, a systematically ordered representation of knowledge about a domain, in terms of its objects, concepts and other entities, as well as the variety of relationships among them.

BUSINESS RULES

The implementation in software and databases of the policies, practices, procedures and decision processes of an enterprise, including constraints on data and its legitimate operational uses. Also known as business logic.

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Searls, D. Data integration: challenges for drug discovery. Nat Rev Drug Discov 4, 45–58 (2005). https://doi.org/10.1038/nrd1608

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