Literature DB >> 14696046

Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era.

Douglas B Kell1, Stephen G Oliver.   

Abstract

It is considered in some quarters that hypothesis-driven methods are the only valuable, reliable or significant means of scientific advance. Data-driven or 'inductive' advances in scientific knowledge are then seen as marginal, irrelevant, insecure or wrong-headed, while the development of technology--which is not of itself 'hypothesis-led' (beyond the recognition that such tools might be of value)--must be seen as equally irrelevant to the hypothetico-deductive scientific agenda. We argue here that data- and technology-driven programmes are not alternatives to hypothesis-led studies in scientific knowledge discovery but are complementary and iterative partners with them. Many fields are data-rich but hypothesis-poor. Here, computational methods of data analysis, which may be automated, provide the means of generating novel hypotheses, especially in the post-genomic era. Copyright 2003 Wiley Periodicals, Inc.

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Year:  2004        PMID: 14696046     DOI: 10.1002/bies.10385

Source DB:  PubMed          Journal:  Bioessays        ISSN: 0265-9247            Impact factor:   4.345


  121 in total

1.  Identification of putative stage-specific grapevine berry biomarkers and omics data integration into networks.

Authors:  Anita Zamboni; Mariasole Di Carli; Flavia Guzzo; Matteo Stocchero; Sara Zenoni; Alberto Ferrarini; Paola Tononi; Ketti Toffali; Angiola Desiderio; Kathryn S Lilley; M Enrico Pè; Eugenio Benvenuto; Massimo Delledonne; Mario Pezzotti
Journal:  Plant Physiol       Date:  2010-09-08       Impact factor: 8.340

2.  Systems biology: Metabolites do social networking.

Authors:  Douglas B Kell
Journal:  Nat Chem Biol       Date:  2011-01       Impact factor: 15.040

3.  Neural networks in the future of neuroscience research.

Authors:  Mikail Rubinov
Journal:  Nat Rev Neurosci       Date:  2015-10-21       Impact factor: 34.870

Review 4.  Clashing Diagnostic Approaches: DSM-ICD Versus RDoC.

Authors:  Scott O Lilienfeld; Michael T Treadway
Journal:  Annu Rev Clin Psychol       Date:  2016-02-03       Impact factor: 18.561

Review 5.  Genetic design automation: engineering fantasy or scientific renewal?

Authors:  Matthew W Lux; Brian W Bramlett; David A Ball; Jean Peccoud
Journal:  Trends Biotechnol       Date:  2011-10-14       Impact factor: 19.536

Review 6.  New hyphenated methodologies in high-sensitivity glycoprotein analysis.

Authors:  Milos V Novotny; Yehia Mechref
Journal:  J Sep Sci       Date:  2005-10       Impact factor: 3.645

Review 7.  Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets.

Authors:  Mariët J van der Werf; Renger H Jellema; Thomas Hankemeier
Journal:  J Ind Microbiol Biotechnol       Date:  2005-05-14       Impact factor: 3.346

Review 8.  Proteomics of the human placenta: promises and realities.

Authors:  J M Robinson; W E Ackerman; D A Kniss; T Takizawa; D D Vandré
Journal:  Placenta       Date:  2008-01-28       Impact factor: 3.481

9.  Identification and evaluation of cycling yeast metabolites in two-dimensional comprehensive gas chromatography-time-of-flight-mass spectrometry data.

Authors:  Rachel E Mohler; Benjamin P Tu; Kenneth M Dombek; Jamin C Hoggard; Elton T Young; Robert E Synovec
Journal:  J Chromatogr A       Date:  2007-10-25       Impact factor: 4.759

10.  Placental proteomics: a shortcut to biological insight.

Authors:  J M Robinson; D D Vandré; W E Ackerman
Journal:  Placenta       Date:  2008-12-13       Impact factor: 3.481

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