By Michael Hirsch, Allan Tucker, Stephen Swift, Nigel Martin, Christine Orengo (auth.), Michael R. Berthold, Robert C. Glen, Ingrid Fischer (eds.)
This publication constitutes the refereed complaints of the second one foreign Symposium on Computational existence Sciences, CompLife 2006, held in Cambridge, united kingdom, in September 2006.
The 25 revised complete papers offered have been conscientiously reviewed and chosen from fifty six preliminary submissions. The papers are equipped in topical sections on genomics, information mining, molecular simulation, molecular informatics, platforms biology, organic networks/metabolism, and computational neuroscience.
Read Online or Download Computational Life Sciences II: Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings PDF
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Additional resources for Computational Life Sciences II: Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings
Sample text
Cameron-Jones, and A. Sale sets are cumulative. That is, the 5% dataset contains all of the 1% dataset; the 10% dataset contains all of the 5% and the 1%, etc. Using the same approach as Bajic, Tan et al. [2], we count a true positive (TP) when a prediction falls within 2000bps of a TSS. We also aggregate predictions that are within 1000bp of each other. In evaluating the performance of the classifier we make use of five metrics throughout the paper. These are Sensitivity – abbreviated to Se (the percentage of positives that are correctly identified), Positive Predictive Value – abbreviated to PPV (the percentage of positive predictions that are correct), Accuracy (the percentage of predictions that are correct, be they negative or positive), True Positive cost (the number of FPs required to achieve a TP) and finally specificity – abbreviated to Sp (the percentage of negatives correctly identified).
The window threshold is shown on the x-axis. We graph both sensitivity and positive predictive value for threshold application both before and after aggregation. 4 Conclusions We have set out within this paper to address two distinct points. Firstly, we intended to demonstrate that a combined model of physico-chemical properties could be used for promoter prediction, improving the accuracy of using a single model. We have demonstrated this fact, showing an increase of approximately 2% over the best of the single models.
For the purposes of comparing TSS prediction position, we examined taking the start, middle and end of this window. Given the metrics we are using, we determined that taking either the middle or end produced the same results, but taking the start was markedly worse. Due to the fact that training times are quite long for some of the approaches we present herein (most notably the combined model with 839 attributes), we do not perform a ten-fold cross validation as is often done. Instead, we present the result of training the model on increasing segments of the data (from 1% up to 50%) and show the result of testing on the left over data (from 99% down to 50%).