Enhanced PMF Model to Predict User Interest for Web API Recommendation

Many methods focus solely on the relationship between the API and the user and fail to capture their contextual value. Because of this, they could not get better accuracy. The accuracy of the API recommendation can be improved by considering the effect of API contextual information on their latent attribute and the effect of the user time factor on the latent attribute of the user through the deep learning-based matrix factorization method (DL-PMF). In this chapter, a CNN (convolutional neural network) with an attention mechanism for the hidden features of web API elements and an LSTM (long-term and short-term memory) network is introduced to find the hidden features of service users. Finally, the authors combined PMF (probabilistic matrix factorization) to estimate the value of the recommended results. Experimental results obtained by the DL-PMF method show better than the experimental results obtained by the PMF and the ConvMF (convolutional matrix factorization) method in the recommended accuracy.

Predicting the Effects of Drug Combinations Using Probabilistic Matrix Factorization

Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.

Predicting the effects of drug combinations using probabilistic matrix factorization

Drug development is costly and time-consuming, and developing novel practical strategies for creating more effective treatments is imperative. One possible solution is to prescribe drugs in combination. Synergistic drug combinations could allow lower doses of each constituent drug, reducing adverse reactions and drug resistance. However, it is not feasible to sufficiently test every combination of drugs for a given illness to determine promising synergistic combinations. Since there is a finite amount of time and resources available for finding synergistic combinations, a model that can identify synergistic combinations from a limited subset of all available combinations could accelerate development of therapeutics. By applying recommender algorithms, such as the low-rank matrix completion algorithm Probabilistic Matrix Factorization (PMF), it may be possible to identify synergistic combinations from partial information of the drug interactions. Here, we use PMF to predict the efficacy of two-drug combinations using the NCI ALMANAC, a robust collection of pairwise drug combinations of 104 FDA-approved anticancer drugs against 60 common cancer cell lines. We find that PMF is able predict drug combination efficacy with high accuracy from a limited set of combinations and is robust to changes in the individual training data. Moreover, we propose a new PMF-guided experimental design to detect all synergistic combinations without testing every combination.

When does gap filling of trait data confound taxonomic and functional analyses?

<p>Data on plant traits are increasingly used to understand relationships between biodiversity and ecosystem processes. Large trait databases are sparse because they are compiled from many smaller and usually more local databases. This sparsity severely limits the potential for both multivariate and global data analyses, and so "gap-filling" (imputation) approaches are commonly used to predict missing trait data prior to analysis. Data imputation can result in large biases and circularity; yet, no best practice has evolved for the appropriate use of gap-filled data. Here, we use the TRY database, the largest global database of plant traits, in combination with the commonly used gap-filling algorithm, BayesianHierarchical Probabilistic Matrix Factorization (BHPMF), to address opportunities and problems introduced by gap-filling. BHPMF is the gap-filling method of choice for both TRY, and the large and widely used database sPLOT. It predicts missing trait data using the taxonomic hierarchy and observed patterns of trait variance and trait-trait correlations. We use three metrics: root mean square error estimates, coefficient of variation to assess univariate deviation, and silhouette indices to assess multivariate deviation and clustering strength. We show that gap-filling results in deviation of these metrics calculated for groupings at lower taxonomic levels (intra-specific and intra-genera), but less so at higher taxonomic levels (family) and for functional groups. Trait-trait correlations are preserved at all levels. The strength of deviations depends both on the percentage of gaps, and on data characteristics, e.g. intra-taxa variability. Gap-filling with dataset-external trait data generally ameliorates prediction error, but the deviations of intra-taxonomic variation measures depend on the content of the added data. We conclude that BHPMF gap-filling introduces little bias if specifically used for analyses of traits within functional groups, including growth forms and plant functional types (PFTs), as well as trait-trait correlations. However, we generally discourage their use for analyses of taxonomic groupings at or below the family level. In summary, our study supports decisions on when and how to integrate BHPMF gap-filled trait data in future studies. We conclude with selected best practices when using sparse databases.</p>

Locus recommendation using probabilistic matrix factorization techniques

Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20. Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users. Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF. This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well. Conclusion: The motive of the work is to identify the best technique for recommending locations with the highest accuracy and allow users to choose from a plethora of available locations; the best and interesting location based on the individual’s profile. Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models. Limitations: User’s contextual information like demographics, social and geographical preferences have not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.