Annotation-Modules: a tool for finding significant combinations of multisource annotations for gene lists is a research paper published in Bioinformatics (2008). On theSindex it has a DataRank of 1.2. It has been cited 36 times, with 16 citing works in its 1-hop citation network.
Abstract Motivation: The ontological analysis of the gene lists obtained from DNA microarray experiments constitutes an important step in understanding the underlying biology of the analyzed system. Over the last years, many other high-throughput techniques emerged, covering now basically all ‘omics’ fields. However, for some of these techniques the generally used functional ontologies might not be sufficient to describe the biological system represented by the derived gene lists. For a more complete and correct interpretation of these experiments, it is important to extend substantially the number of annotations, adapting the ontological analysis to the new emerging techniques. Results: We developed Annotation-Modules, which offers an improvement over the current tools in two critical aspects. First, the underlying annotation database implements features from many different fields like gene regulation and expression, sequence properties, evolution and conservation, genomic localization and functional categories—resulting in about 60 different annotation features. Second, it examines not only single annotations but also all the combinations, which is important to gain insight into the interplay of different mechanisms in the analyzed biological system. Availability: http://web.bioinformatics.cicbiogune.es/AM/AnnotationModules.php Contact: [email protected]
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.542
From this paper's citation signal
Citation Network Contribution
0.624
From 13 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 46% comes from its base citations and 54% from the citation network (13 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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