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

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.

Mei (2013) uses homolog information when the features of a protein is unavailable. Applying machine learning methods and specially supervised learning for situations suffer from data scarcity is challenging. First, they rely on homologous proteins data to provide feature values like GO annotations and gene expression data. Being limited to well-studied pathogen systems like HIV-1 is the consequence of data dependency. (2012) two different methods are proposed including information transfer from other species and model-based imputation. However, for proteins with no available homolog, they have modeled gene expression value distribution. The first method is called RF which initiates the missing data to mean value and re-estimate it by choosing the nearest leaf node of the created forest. For instance, in Kshirsagar et al. They have compared the proposed Cross species imputation with other imputation techniques. This contributes a lot and downgrades the missing data significantly. Clear improvements are reported in comparison with the listed imputation methods. Recently, some solutions are proposed to overcome this limitation by offering substituted values for missing data. It should be noted that using solely statistical methods for estimating features like GO values will be hard due to high dimensionality. Pessimistic experiment, which uses only homolog features to train and test without incorporating any base proteins (called 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. They have designed various experiments to show the performance of substituting homolog features. Another intuitive method is choosing the average of the feature values and the last compared method is discarding any pair with missing value which leads to a reduced dataset.

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However, it should be noted that making use of a lot of features without enriching training data may lead to over fitting in the model (Mei, 2014). Outperforming other features was the motivation for some studies to use GO features in PHI prediction (Mei, 2013, 2014) while features extracted from protein sequences, reported as not promising (Yu et al., 2010). Various studies utilize different sets of biological information through data integration to improve the prediction performance. Table Table22 summarizes the utilized features within different studies on PHI prediction, providing all the cataloged feature information is not always possible for all pathogen systems. Furthermore, various features claimed to have different predictive effects in PHI prediction.

Not all pathogen systems are appropriate for applying the mentioned domain based approaches, since domains and the related information are not available for all pathogens. (2009) concentrates on protein interactions based on short eukaryotic linear motifs (ELMs) for HIV-1 proteins interacting with human protein counter domains (CDs). They do not accept the idea of having relatively weak link among motif/domain bindings and the actual virus-host PPIs which is presented in Tastan et al. They predict two kinds of interactions for each virus protein, including direct human protein targets (called H1) which bind to virus via a human CD and a virus ELM and the second type includes indirect interactions in which, host proteins that their normal interactions with H1 proteins are potentially disrupted by competition with an HIV-1 protein. For instance, information on domains and the related statistics are not available for a considerable number of the HIV-1 proteins. Table Table55 summarizes the conducted research for predicting PHIs based on domain and motif knowledge. Regarding this limitation, the work in Evans et al. (2009).

(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.

Structure based approaches

Interactions between pathogen and host proteins allow pathogenic microorganisms to manipulate host mechanisms in order to use host capabilities and to escape from host immune responses (Dyer et al., 2010). Many studies concerning identification of protein interactions and their associated networks were published (Aloy and Russell, 2003). Inter-species interactions may take many forms; in this survey, however, we focus on PPIs between pathogens and their hosts. Therefore, a complete understanding of infection mechanisms through PHIs is crucial for the development of new and more effective therapeutics. Most of the previous studies were primarily focused on determining protein-protein interactions (PPIs) within a single organism (intra-species PPI prediction), while the prediction of PPIs between different organisms (inter-species PPI prediction) has recently emerged. Pathogen-host interaction (PHI) prediction is worthwhile to enlighten the infection mechanisms in the scarcity of experimentally-verified PHI data.

(2014) introduces the stringent homology which does not rely only on intra-species template PPIs to discover interologs and make use of two different organisms as the source of template PPIs to predict PHIs. Zhou et al. They also claim that it is not only for the targeted host proteins which tend to be hub in their own PPI network and this is also true about targeting pathogen proteins.

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.

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A homology detection method using template PPI databases, DIP (Salwinski et al., 2004) and iPfam (Finn et al., 2014), is published in Krishnadev and Srinivasan (2008) to predict PHI pairs. Searching the sequences of host and pathogen proteins within two template databases are conducted to find a superset of all interactions which are physically and structurally compatible. The authors have applied the same procedure for different pathogens in their subsequent works (Tyagi et al., 2009; Krishnadev and Srinivasan, 2011). These potential interactions are refined within two additional filtering steps, to detect biologically feasible interactions including integration of expression and sub-cellular localization data.

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.

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.

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Some studies validate their results by measuring the shared interactions with other published materials (Mukhopadhyay et al., 2012, 2014; Segura-Cabrera et al., 2013). The lack of gold standard PHI data and the complexity of PHI mechanisms lead to a hard assessment phase, in a way that predicted interactions are rarely supported by a biological basis. Here we focus on computational metrics which are widely used in publications to evaluate the accuracy of their results, which are shown in Table Table66.