Ensemble approach combining multiple methods improves human transcription start site prediction.
BACKGROUND
The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.


RESULTS
We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool.


CONCLUSIONS
Supervised learning methods are a useful way to combine predictions from diverse sources.
Complex and Adaptive Systems Laboratory (CASL), University College Dublin, Belfield, Dublin 4, Ireland. david.dineen@ucd.ie
David G
DG
Dineen
Markus
M
Schröder
Desmond G
DG
Higgins
Pádraig
P
Cunningham
D020029:Base Pairing / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D025341:Principal Component Analysis; D011401:Promoter Regions, Genetic / Q000235:genetics; D012984:Software; D024363:Transcription Initiation Site