A Bai / P Hourigan (@1.11) vs R Bains / A Poulos (@6.0)
03-10-2019

Our Prediction:

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

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.

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.

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.

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.

Alison Bai

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.

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.

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.

(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.

Remember that this is the window of negotiation. If you are lets say your range was between 45-60K and the offer they make is 45K, you can then use this time to negotiate for other non salary benefits. This allows room for negotiations with the interviewer. Provide a Range: When asked What are your salary expectations, Mr. Use it carefully. Wainaina says that you should give a range rather than limiting yourself to a single number.