Download Biological Data Mining in Protein Interaction Networks by See-Kiong Ng, Xiao-Li Li PDF

By See-Kiong Ng, Xiao-Li Li

Tools for detecting protein-protein interactions (PPIs) have given researchers a world photograph of protein interactions on a genomic scale.

organic facts Mining in Protein interplay Networks explains bioinformatic equipment for predicting PPIs, in addition to info mining ways to mine or learn quite a few protein interplay networks. A defining physique of study in the box, this publication discovers underlying interplay mechanisms by means of learning intra-molecular good points that shape the typical denominator of varied PPIs.

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2006), the domain-based prediction methods showed quite high accuracy for training data whereas they did not show very high accuracy for test data. This suggests that there exist some overfitting problems. Thus, it might be able to improve the prediction accuracy if some technique for avoiding overfit can be incorporated into the domain-based methods. However, it may be still difficult to significantly improve the prediction accuracy for test data by using domainbased methods only because domain-based models capture some partial aspects of protein-protein interactions.

In order to apply SVMs, we need positive examples and negative examples. In this case, we treat observed interacting protein pairs as positive examples and non-observed protein pairs as negative examples. For each protein pair (Pi,Pj), we define the feature vector fij by (fij ) mn = 1 if Dmn ∈ Pij , (fij ) mn = 0 otherwise , where (fij)mn denotes the mn-th element of the vector fij. , 2003) suggest that among all above method and LPBN, the EM method has the best performance for binary data. INFERENCE OF STRENGTHS OF PROTEIN-PROTEIN INTERACTIONS In the previous section, it is assumed that protein-protein interaction data are given as binary data.

1984) are among some of the most widely used machine learning algorithms for building pattern classifiers from data. Their popularity is due in part to their ability to: select from all attributes used to describe the data, a subset of attributes that are relevant for classification; identify complex predictive relations among attributes; and produce classifiers that are easy to comprehend for humans. 5 (Quinlan, 1993) are representative for a widely used family of decision tree learning algorithms.

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