Abstract
In advanced methods of delineation and mapping of scientific fields, hybrid methods open a promising path to the capitalisation of advantages of approaches based on words and citations. One way to validate the hybrid approaches is to work in cooperation with experts of the fields under scrutiny. We report here an experiment in the field of genomics, where a corpus of documents has been built by a hybrid citation-lexical method, and then clustered into research themes. Experts of the field were associated in the various stages of the process: lexical queries for building the initial set of documents, the seed; citation-based extension aiming at reducing silence; final clustering to identify noise and allow discussion on border areas. The analysis of experts’ advices show a high level of validation of the process, which combines a high-precision and low-recall seed, obtained by journal and lexical queries, and a citation-based extension enhancing the recall. This findings on the genomics field suggest that hybrid methods can efficiently retrieve a corpus of relevant literature, even in complex and emerging fields.
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Notes
Consortium: ADIS (Université Paris-Sud), Lereco (INRA), OST.
Probabilistic index = observed cell population c(ij)/expected cell population where expected cell population = c(i.)c(.j)/c(..).
AKM is implemented in the commercial software Neuronav by Diatopie (S. Aubin, www.diatopie.com). It is enhanced it with a basic but robust and efficient term-extracting sequence.
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Aknowledgments
This work is part of the CSTG project launched by Antoine Schoen and Bertrand Bellon, a project supported by ANR. The authors are indebted to Sylvain Aubin (DIATOPIE) for term extraction and clustering. They also thank the panel of experts for their commitment: P. Bessières (INRA), A. Lecharny (CNRS-Evry), M. Pinto (Université Paris XI), MM JP Rousset and M Dubow (IGMORS-Université Paris XI), P. Wincler (Genoscope). We also benefited from the experience in the field of Bérangère Virlon (OST). The authors are solely responsible for the views expressed in this article.
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Appendices
Annex I List of core journals
ANNUAL REVIEW OF GENOMICS AND HUMAN GENETICS | GENOME |
BIOINFORMATICS | GENOME BIOLOGY |
BMC BIOINFORMATICS | GENOME RESEARCH |
BMC GENOMICS | GENOMICS |
BRIEFINGS IN BIOINFORMATICS | JOURNAL OF PROTEOME RESEARCH |
COMPARATIVE AND FUNCTIONAL GENOMICS | MAMMALIAN GENOME |
CURRENT GENOMICS | MOLECULAR & CELLULAR PROTEOMICS |
CYTOGENETIC AND GENOME RESEARCH | MOLECULAR GENETICS AND GENOMICS |
DNA REPAIR | PHARMACOGENETICS AND GENOMICS |
DNA RESEARCH | PHARMACOGENOMICS |
DNA SEQUENCE | PHARMACOGENOMICS JOURNAL |
EXPERT REVIEW OF PROTEOMICS | PHYSIOLOGICAL GENOMICS |
GENES CHROMOSOMES & CANCER | PROTEOMICS |
Annex II List of themes
The clusters were labelled arbitrarly from M1 to M50 during the clustering process. The most central terms related to each cluster are shown to point out its thematic content
Core genomics
Extension share: high | |
M18/Population_genomics | M32/Marker/RAPD/AFLP/Polymorphism |
M20/Resistance/Resistance_genes/Plant_&_Trout_resistance | M40/QTL/Trait/Mapping/Polymorphism |
M31/LOH/Tumor_suppressor/Genome_&_Cancer | M47/Species/Phylogeny/Evolutionary_genomics |
Extension share: average | |
M 3/Plant_genomics/Transgenic_plants | M25/Patient/Disease_genomics/Biomarkers/Pharmacogenomics |
M 4/DNA_sequence/Satellite | M27/Evolution/Evolutionary_genomics |
M 5/Strain/Microbial_genomics | M28/Cancer/Genome_&_cancer |
M 6/Cell_identity_&_Gene_expression | M35/C-DNA/Transcription/C-DNA_library |
M 8/Alignment/Bioinformatics | M36/Polymorphism |
M12/Network/Biological_networks/Model | M43/Mouse/Murine_genomics |
M14/Locus/Microsatellite_locus/Polymorphism | M44/Expression/Cell_identity_&_Gene_expression |
M15/Cell_line/Tumor/Genome_&_Cancer | M45/LOD/Linkage_analysis/Polymorphism |
M16/Spectrometry/Proteomics | M46/Human/Primate/Gene_annotation/Comparative_genomics |
M22/Human/C-DNA/Gene_annotation | M48/C57BL/Congenic_strains/Murine_genomics |
M23/Exon/Genomic_organization/Gene_annotation | |
Extension share: low | |
M 1/Human_genome/Human_genome_project | M17/Map/Linkage_maps/Polymorphism |
M 9/Genome/ | M24/System/Systems_biology/Bioinformatics |
M10/Comparative_genomic_hybridization/Tumor | M38/Genome/Genome_sizes |
M11/SNPs/Polymorphism |
Border themes
Extension share: high | |
M 2/Translocation/FISH/leukemia | M21/Hybrid/Somatic_hybrids/Ferility |
Extension share: average | |
M13/Transcriptional/Saccharomyces_cerevisiae/Transcriptome | M50/Virus/Virus_replication/Virus_recombinatiio, |
M26/Virus/Nucleotide_sequence | |
Noisy, mostly Non genomics | |
Extension share: high | |
M30/Mutant/Mutagenesis | |
Extension weight: average | |
M 7/Enzyme/Escherichia_Coli | M37/Cell/DNA_damage |
M19/Repair/DNA_damage | M39/DNA/Arrays/Genomic techniques |
M29/Promoter/Transcription | M41/Signaling/Kinase/MAPK |
M33/RNA-/Virus | M42/Mutation/Missence_mutation |
M34/PCR/Methods/applications | M49/Residue/Amino_acid_sequence |
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Laurens, P., Zitt, M. & Bassecoulard, E. Delineation of the genomics field by hybrid citation-lexical methods: interaction with experts and validation process. Scientometrics 82, 647–662 (2010). https://doi.org/10.1007/s11192-010-0177-9
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DOI: https://doi.org/10.1007/s11192-010-0177-9