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This possibility has been demonstrated by the analysis of existing datasets showing that known rare causal mutations responsible for sickle cell disease and hearing loss can give rise to genome-wide significant synthetic associations that lie a considerable distance (2.5 Mb) from the causal mutations.
The results are comparable for both kinds of datasets, showing that randomly selected subsets are representative of the clustered datasets.
The performance of the proposed approach has been assessed on private and public medical datasets, showing that it can be used to improve the classification performance of the One-per-Class scheme with respect to both multiclass classifiers and other well-known decomposition schemes.
The schizophrenia-dependent right insula differences also align closely with our previous analysis of multiple published datasets showing that that both predisposition to schizophrenia and progression of schizophrenia involves smaller right insula volumes [22].
IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods.
The consistence of our approach was assessed through a 100-run simulation on randomly selected datasets showing that the results obtained were unlikely due to randomness thus supporting further investigation.
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The experiments on three datasets show that degree is the best measure in the undirected graph, which indicates that tf is a very important feature for keyphrase extraction.
Experimental results on 52 Human gene expression datasets show that proposed approach discovers biologically significant patterns.
The experiments carried out on real datasets show that the proposed scheme is scalable and effective.
Experimental results on these datasets show that our feature descriptors could achieve promising performance.
Experimental results on datasets show that our method achieves desired results with high performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com