Summary and Future Works

Essential proteins play a critical role on the survival of organism, so the identification of essential proteins has been conducted through experimental or computational approaches. A num- ber of centrality measures have been used to discover essential proteins as computational efforts. These measures which are originally dependent only on the structural properties in a network, can be incorporated with biological information for better performance. Acencio and Lemke [35] de- signed a machine learning based approach with topological properties and gene ontology terms to predict essential genes. Kim et al. [34] ranked each protein with motif centrality (MC) computed in a GO-pruned PPI network to detect essential proteins. In this work, we make use of existing

Figure 6.11: PR curves for the two classifiers are provided. Classifier by Kim et al., 2012 is slightly better than Classifier by Acencio and Lemke, 2009.

centrality measures and biological information to predict essential proteins with machine learning techniques in the yeastSaccharomyces cerevisiaePPI network.

We use a PPI network downloaded from the BioGRID database [278] with 4,854 proteins and 37,209 interactions. Out of 4,854 proteins, 998 are essential, 3,557 are non-essential and the rest are unknown. The network is first pruned by EDGEGO algorithm which removes 14,925 interactions of relatively uninformative GO terms. From the GO-pruned PPI network, we compute 8 centrality measures, namely, DCGO, BCGO, CCGO, ECGO, SCGO, SoECCGO, LACGO and MCGO. We name the set of 8 features as CENT-GO. Then we construct ten balanced data sets where the number of essential proteins and the number of non-essential proteins are the same, to avoid biased performance to the majority set. For evaluation measures, we used the area under ROC (AUC-ROC), area under PR (AUC-PR), accuracy at an optimal threshold (ACC) and computational time (T). We first confirmed that the 10 balanced data sets are statistically similar through Mann- Whitney U-statistics test on AUC-ROC, so that we can randomly choose one data set for further experiments.

We demonstrate that the prediction with CENT-GO has.784 for AUC-ROC,.781 for AUC- PR and .727 accuracy. The performance is compared with the 23 features of ING-GO (Acencio and Lemke, 2009) set used in [35]. ING-GO consists of 12 topological features extracted from

Table 6.5: Statistical Significance of each feature: Each feature was assessed based on its statistical significance. The performance improvement of CENT-GO is statistically verified as all the p- values, compared with each measure, are less than0.05.

CENT-GO DCGO BCGO CCGO SCGO ECGO LACGO SoECCGO DCGO 0.001 BCGO 0.000 0.000 CCGO 0.000 0.019 0.000 SCGO 0.000 0.000 0.009 0.220 ECGO 0.000 0.000 0.472 0.020 0.131 LACGO 0.000 0.047 0.000 0.884 0.170 0.003 SoECCGO 0.000 0.214 0.000 0.393 0.031 0.000 0.405 MCGO 0.000 0.260 0.000 0.468 0.073 0.000 0.488 0.977

an integrated (PPI, transcriptional regulatory and metabolic) network and 11 biological process and cellular localization gene ontology features. With only eight features, CENT-GO performs better than 23 features of ING-GO with ACC and T evaluation measures, although it does not beat the AUC values (See the result in Table 6.3). Therefore, when all the features are integrated with 31 CENT-ING-GO (combined), the prediction performance is significantly improved. The improvement is confirmed as statistically significant with Mann-Whitney U-statistic test as well.

We compared the prediction performance with different classifier methods as well. Theclas- sifier by Kim et al., 2012, used in this work, is a meta-classifier using seven decision trees, support vector machine and neural network. For each algorithm, a “bagging” technique is applied to reduce variance and all the eight algorithms are combined by the average rule. The classifier by Kim et al., 2012 in fact improves the performance in the prediction compared with classifier by Acencio and Lemke, 2009, which is a decision tree based meta-classifier used in the study [35].

We analyzed individual features as well to see the impact of each measure compared to all in- tegrated features. When we apply the same classifier to each individual measure, DCGO produces relatively better result than others, although the integration of all eight features perform signifi- cantly better. The analysis is conducted by deriving a general rule using a decision tree algorithm as well. We could see that most of decision trees in balanced data sets have MCGO or DCGO as

Table 6.6: Comparison of each measure: The experiment with CENT-GO is compared with the experiment with a single feature each. The performances are compared based on its AUC-ROC, AUC-PR, ACC and T measures.

AUC-ROC AUC-PR ACC Tsecond

CENT-GO(ALL) 0.784 0.781 0.727 174.19 DCGO 0.760 0.733 0.716 82.53 BCGO 0.711 0.682 0.682 83.34 CCGO 0.742 0.711 0.701 84.82 SCGO 0.732 0.719 0.682 129.89 ECGO 0.718 0.720 0.675 101.64 SoECCGO 0.743 0.741 0.702 115.13 LACGO 0.743 0.738 0.689 101.95 MCGO 0.749 0.747 0.710 96.61

a root node, indicating their important impacts on the general rule. The superiority of MCGO and DCGO has been proven in the previous study [34].

The work has three contributions in the discovery of essential proteins: 1) We combined eight centrality measures computed from PPI network to predict essential proteins using machine learning techniques. Because this feature set includes a smaller number of features than ING-GO (Acencio and Lemke, 2009), the classification process saves computation complexity but produces similar quality of results as ING-GO. 2) We incorporated GO information into the process of centrality measures using EDGEGO, so that additional GO term features are unnecessary. 3) We improved the performance significantly by combining CENT-GO (Kim et al., 2012) and ING-GO (Acencio and Lemke, 2009), with a new design of classifier which adds neural network and support vector machine algorithms.

Prediction of essential proteins can be improved further. First, if we can extract the features from integrated networks, not a single PPI network, then we can obtain enriched feature sets to improve the prediction performance. However, the construction of integrated network requires a large amount of experiments and these networks are available only a limited number of organisms. This limitation hinders the prediction task for various organisms. Second, more robust features can

Figure 6.12: Decision tree on the balanced dataset5 with CENT-GO features with 64 instances per leaf: The data set contains only CENT-GO features and the tree algorithm generate a rule where “MCGO” as a root. The values are normalized before running the algorithm, and it produces72% of accuracy and the area under ROC is.734. The eclipses are the features and in this set, “MCGO” and “ECGO” are likely to determine the essentiality of proteins.

improve the prediction task further. Most of centrality measures depend on current GO terms and a PPI network, which are being consistently updated. This limitation can hamper robust experiments and results. Therefore, future studies need to focus on overcoming these limitations.

Figure 6.13: Decision tree on the balanced dataset5 with combined features of CENT-ING-GO with 64 instances per leaf: The data set contains 31 features and the tree algorithm generate a rule where “MCGO” as a root. The values are normalized before running the algorithm, and it produces73%of accuracy and the area under ROC is.752. The eclipses are the features and in this set, “MCGO” and “ECGO”, “clustering coefficient (c)”, “nucleus” and “endoplasmic reticulum (er)” are likely to determine the essentiality of proteins.