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

(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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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.

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.


However, homology to known interactions is not sufficient for evaluating the biological evidence of the predicted results. Different filtering techniques should be considered for assessing the feasibility of the interactions under an in vivo condition and consequently decreasing the false positives. Simplicity and clear biological basis are the main advantages of these methods. The conserved interaction is called as Interolog. The simple method of identifying Interologs is as follows: Consider a template PPI pair (a, b) in a source species, find the homolog a in the host and the homolog b in the pathogen, conclude that (a, b) interact. The rationale behind this type of methods is the expectation of conserved interactions between a pair of proteins which have interacting homologs in another species.

Data unavailability and scarcity refer to verified interacting PPIs, lack of verified non-interacting protein pairs and missing feature information for proteins. HIV-1 is the most distinguished pathogen which studied specifically using data-requiring machine learning methods. In this paper, we reviewed the studies which directly focused on computationally PHI prediction. Clearly some pathogen systems are well studied and targeted in more research regarding the availability of the required data. Knowledge transfer from related pathogen systems has shown to be an effective remedy, even for situations with no available interactions. Inter-species PPI predictions have gained more popularity in recent years. These methods enlighten a promising future direction for establishing computational methods which are augmented with additional transferred knowledge. Recent studies have found a new source of data to overcome these limitations. Computational methods may have important roles in paving the way for experimental PHI verifications by highlighting the high potential interactions and limiting the experimental scope which lead to expense reduction and probably the rapid knowledge development. Published approaches are categorized based on pathogen-host and the method they utilize. Therefore, the most important challenge for computationally prediction of PHIs, is the lack of available verified interactions and the relevant feature information in most of the pathogens systems.

They put aside sub-cellular co-localized pairs from the negative class and report better performance in comparison with random sampling. The rate of positive to negative class is chosen in different manners to avoid biasing classifier toward wrong predictions. However, ignoring non-interacting patterns may increase the rate of false positives (Mei, 2013). (2012) and instead they use unknown label for other pairs. The negative set is not defined in Nouretdinov et al. A ratio of 1:100 is chosen in Kshirsagar et al. Mei (2013) chooses the same ratio for negative and positive classes, however proposes different idea for choosing negative samples. Since there is no available verified non-interacting PPI to be used for training the model, selecting negative data remains as a challenge for PPI prediction. (2009) expecting one interaction pair within 100 random pathogen-host pairs. Most of the studies which formulate the problem as a classification task, have to construct negative class through randomly sampling the data. (2011) conducted experiments with different ratios and 10 randomly chosen sets for each ratio and stated that beside clearly different results for different ratios, variability of randomly selected negative samples for each ratio does not have major effect on the result accuracy. Some studies try to circumvent the obstacle by using methods which do not require negative samples (Ray et al., 2012). The study in Dyer et al. (2012, 2013b) and Tastan et al.

This has motivated some studies to overcome this problem by removing the need for negative data through using alternative methods (Mukhopadhyay et al., 2010, 2012, 2014; Mondal et al., 2012; Ray et al., 2012). Machine learning based methods which formulate PPI prediction as a classification task use both interacting and non-interacting protein pairs as positive and negative classes, respectively. Constructing negative class is not straightforward due to the fact that there is no experimentally verified non-interacting pair. They integrate bi-clustering with association rule mining, utilizing only positive samples to predict virus-human interactions.

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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The idea of exploiting domains as building blocks of proteins for predicting PPIs is well-studied for single organisms (Wojcik and Schchter, 2001; Pagel et al., 2004) regarding the fact that domains are the mediators of interactions. However, small list of interactions are presented and their biological relevance are not strongly evaluated. (2007) is one of the pioneer published research for predicting PHIs. To apply this idea to a pathogen-host system, they identify domains in every host and pathogen proteins and compute the interaction probability for each pair of host and pathogen proteins that contain at least one domain. The approach presented in Dyer et al. To predict interactions between host and pathogen proteins, they present an algorithm that integrates protein domain profiles with interactions between proteins from the same organism. For every pair of functional domains (d, e) which is present in protein pair (g, h) respectively, the probability of interacting (g, h) is assessed using Bayesian statistics.

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