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

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.

The semi-supervised extension of their work is presented in Qi et al. However, they have gained better performance through incorporating likely interactions (called partially labeled), which do not have sufficient evidence to be categorized as direct interaction. (2010) which discarded 17 attributes from the feature vector that is related to determining 17 HIV-1 proteins. The same classifier is used as a quality control in Wuchty (2011), where a RF classifier assesses the quality of candidate interactions, obtained by discovering homologous and conserved interactions. (2009) integrating 35 features within eight groups using Random Forest (RF) classifier to deal with noisy and redundant features. Applying machine learning techniques to bioinformatics is a well-accepted idea (Baldi and Brunak, 2001), which includes early efforts for PPI predictions (Bock and Gough, 2001). Both semi-supervised and supervised learning are used for PHI prediction. A Supervised method, which exploits exclusively labeled data, is applied in Tastan et al. These methods utilize available PPI data as features for training and classifying interacting and non-interacting protein pairs. The author filters the predicted results based on expression and molecular properties.

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Machine learning and data mining based approaches

They emphasize the importance of constructing a high-resolution, 3D structural view of pathogen-host and within-host PPI networks to discover new principles of PHIs through their review paper in Franzosa et al. (2012). The method starts with extracting human interacting pairs from PDB and followed by mapping virus proteins to them by sequence similarity. Applicability of the method is limited to human-human and virus-human PPIs for which 3D structural models are available. Authors in Franzosa and Xia (2011) claim to significantly reduce the rate of false positives by presenting virus-human structural interaction network, in which, each PPI is associated with a high confidence 3D structural model.

Computational methods for predicting PHIs exploit known protein and domain interactions, and information on sequence of proteins. Network topology measures can complement these data. For instance, targeting hubs and bottleneck proteins in human PPI network by pathogen proteins is a well-accepted idea (Dyer et al., 2008; Durmu Tekir et al., 2012; Schleker and Trilling, 2013; Zheng et al., 2014), though, they are not the sole targeted proteins (Chen et al., 2012). Classic machine learning methods are valuable remedy for cases where enough data for training are available. However, valuable efforts have recently been performed to apply these techniques for situations suffer from scarcity of known interaction data using machine learning based methods as transfer and multitask learning (Xu et al., 2010; Kshirsagar et al., 2013a,b). Considering the relative availability of interaction data for HIV-Human system, notable number of studies are dedicated to this pathogen. Some other viral and bacterial pathogens are investigated and human is the main target as the host for investigation.

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.

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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).

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.

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.

On the opposite case, transfer and multitask learning methods are preferred. The computational methods primarily utilize sequence information, protein structure and known interactions. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs.


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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.