A Bai / P Hourigan (@1.11) vs R Bains / A Poulos (@6.0)

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A Bai / P Hourigan will win
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A Bai / P Hourigan – R Bains / A Poulos Match Prediction | 03-10-2019 02:35

They predict PPIs using PreDIN (Kim et al., 2002) and PreSPI (Han et al., 2004) algorithms based on domain information. They presented XooNET which provides about 3500 possible interaction pairs as well as the graphical visualizations of the interaction networks. (2007) which makes use of domain information from InterProScan (Quevillon et al., 2005). A similar knowledge source is chosen in Kim et al. A study for prediction of interacting proteins of rice and Xanthomonas oryzae pathovar oryzae (Xoo) also uses domain information (Kim et al., 2008).

Conformal prediction is used in Nouretdinov et al. Their approach also allows the user to determine confidence level for prediction. This method evaluates the conformance of new pairs with interacting pairs using a method called non-conformity measure (NCM) which shows distinction measure of an example regarding others. (2012) and the results are compared with those of Tastan et al. (2009) to assess the predictions.

They apply the same method for developing an interaction network between Dengue virus and its hosts (Doolittle and Gomez, 2011). Human proteins which have high structural similarity to a HIV protein are identified and their known interacting partners are determined as targets. Table Table44 summarizes the conducted research for predicting PHIs based on structural data. Again, with a similar idea those proteins with comparable structures share interaction partners. Another research developed a map of interactions between HIV-1 and human proteins based on protein structural similarity (Doolittle and Gomez, 2010). A comparison of known crystal structures is performed to measure structural similarity between host and pathogen proteins. The work suffers from the lack of assessment data in a way that, very limited number of used benchmark PPIs are specific to the viral pathogen. These predicted results refined by two filtering steps using data from the recent RNAi screens and cellular co-localization information. The assumption is that HIV proteins have the same interactions as their human peers.

(2007) due to applying different techniques and datasets for same pathogen-host system. The assumption is that when two orthologous groups are shared between more than two species, there will be a potential Interolog between those orthologous groups. The notable point is negligible intersection of the predicted interactions with those of the reported predictions in Dyer et al. Another research uses high confidence intra-species PPIs to detect Interologs using ortholog information (Lee et al., 2008). The potential interactions are filtered using gene ontology annotations followed by pathogen sequence filtering based on the presence or absence of translocational signals to refine the predictions.

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Pessimistic experiment, which uses only homology features for train and test without incorporating any base proteins (called as target in the article) has promising results, indicating that using homolog information is an effective substitute for the target information to tackle the problem of data unavailability. Mei (2013) uses homolog information (features) when the features of a protein is unavailable. They have designed different experiments to show the performance of substituting homology features. Homolog knowledge can be used indirectly as a remedy for data scarcity and data unavailability by homolog knowledge transfer.

Table Table33 summarizes the published research for predicting PHIs based on homology information. For instance, the number of interologs within bacterial PPIs are not dignificant (Kshirsagar et al., 2013b) demonstrating that we cannot rely only on homolog information for every situation without being cautious about data availability. Clearly, it is reasonable to predict more genomic and proteomic data will be available in the future and consequently more accurate homologs are identified paving the way of studying less-known pathogens. The most important obstacle for using homology based methods is scarcity of available homolog information.

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Each task is formulated as predicting PHI data between each pathogen and its host. Their goal is to predict intra-species pathogen PPIs as target with the aid of human PPIs as source network through defining a similarity matrix to act as a bridge between them. (2013b) to integrate knowledge from different pathogen-host systems to increase the prediction power of the combined model. Another study conducts three different individual classifiers on three GO features (molecular functions, cellular localization, and biological processes) on available protein features and at the same time three classifiers on alternative homolog features to exploit transfer learning. Another multitask formulation is used in Kshirsagar et al. A combination of supervised and semi-supervised approaches is proposed by Qi et al. For PPI prediction, a method was proposed in Xu et al. To define similarity between tasks and transfer shared knowledge, they assume that similar pathogens tend to target same biological process in human. They applied relatively same idea using a multi instance AdaBoost method to transfer homolog feature as the second instance of proteins (Mei, 2014; Mei and Zhu, 2014). Multitask learning uses commonalities among different domains and learn problem simultaneously between them within a shared task formulation, which leads to better performance rather conducting learning task on individual domain. To implement this idea, optimization problem is conducted and dissimilarities are penalized in the objective function. (2010) which uses collective matrix factorization originally proposed by Singh and Gordon (2008) to transfer knowledge from a relatively dense PPI network called source for predicting new PPIs in a sparse target PPI network. An ensemble classifier produces final result using weighting probability outputs of individual classifiers (Mei, 2013). In other words, commonality hypothesis is introduced that assumes pathway membership of human proteins in positive PHIs should be similar between different tasks. Semi-supervised task on partially positive labels is conducted to improve the supervised classification which trains multi-layer perceptron using labeled data. (2013a) for the cases where no known interaction is available by exploiting precisely chosen instances from a source task. A review paper, Xu and Yang (2011) presents some of the studies utilizing this idea in bioinformatics. (2010) through multitask learning. One of the promising remedies to tackle the problem of data scarcity is eliciting and transferring data from related domains to desired formulation. They use transfer learning in Kshirsagar et al.

Then the data was searched for experimentally verified effectors or their homologs in another bacteria. The result is the possible interactions between Salmonella effectors and host proteins. They collect a list of Pfam domains and bacterial-human proteins which contains one of the listed domains. (2012) presents a method to predict and rank bacteria-human PPIs based on domain-domain interactions. The work in Arnold et al.