Artículos de revistas: "Drug Side Effect Prediction"

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Hu, Baofang, Hong Wang, and Zhenmei Yu. "Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network." Molecules 24, no. 20 (2019): 3668. http://dx.doi.org/10.3390/molecules24203668.

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Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.

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Seo, Sukyung, Taekeon Lee, Mi-hyun Kim, and Youngmi Yoon. "Prediction of Side Effects Using Comprehensive Similarity Measures." BioMed Research International 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/1357630.

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Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

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Kim, Jinwoo, and Miyoung Shin. "A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction." Applied Sciences 13, no. 5 (2023): 2842. http://dx.doi.org/10.3390/app13052842.

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Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data may hinder improvement of performance. Moreover, compared with all possible drug combinations, the highly limited range of drug combinations used for model training prevents achieving high generalizability. To handle these problems, we propose a unified embedding-based prediction model using knowledge graph constructed with data of drug–protein and protein–protein interactions. Herein, single or multiple drugs or proteins are mapped into the same embedding space, allowing us to (1) jointly utilize side effect occurrence data associated with single drugs and multidrug combinations to train prediction models and (2) quantify connectivity strengths between drugs and other entities such as proteins. Due to these characteristics, it becomes also possible to utilize the quantified relationships between distant nodes, as well as neighboring nodes, of all possible multidrug combinations to regularize the models. Compared with existing methods, our model showed improved performance, especially in predicting the side effects of new combinations containing novel drugs that have no clinical information on polypharmacy effects. Furthermore, our unified embedding vectors have been shown to provide interpretability, albeit to a limited extent, for proteins highly associated with multidrug side effect.

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Arshed, Muhammad Asad, Shahzad Mumtaz, Omer Riaz, Waqas Sharif, and Saima Abdullah. "A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure." Vol 4 Issue 1 4, no. 1 (2022): 19–31. http://dx.doi.org/10.33411/ijist/2022040102.

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Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.

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Mohd Ali, Yousoff Effendy, Kiam Heong Kwa, and Kurunathan Ratnavelu. "Predicting new drug indications from network analysis." International Journal of Modern Physics C 28, no. 09 (2017): 1750118. http://dx.doi.org/10.1142/s0129183117501182.

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This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

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Zhao, Xian, Lei Chen, Zi-Han Guo, and Tao Liu. "Predicting Drug Side Effects with Compact Integration of Heterogeneous Networks." Current Bioinformatics 14, no. 8 (2019): 709–20. http://dx.doi.org/10.2174/1574893614666190220114644.

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Background: The side effects of drugs are not only harmful to humans but also the major reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical companies. However, detecting the side effects for a given drug via traditional experiments is time- consuming and expensive. In recent years, several computational methods have been proposed to predict the side effects of drugs. However, most of the methods cannot effectively integrate the heterogeneous properties of drugs. Methods: In this study, we adopted a network embedding method, Mashup, to extract essential and informative drug features from several drug heterogeneous networks, representing different properties of drugs. For side effects, a network was also built, from where side effect features were extracted. These features can capture essential information about drugs and side effects in a network level. Drug and side effect features were combined together to represent each pair of drug and side effect, which was deemed as a sample in this study. Furthermore, they were fed into a random forest (RF) algorithm to construct the prediction model, called the RF network model. Results: The RF network model was evaluated by several tests. The average of Matthews correlation coefficients on the balanced and unbalanced datasets was 0.640 and 0.641, respectively. Conclusion: The RF network model was superior to the models incorporating other machine learning algorithms and one previous model. Finally, we also investigated the influence of two feature dimension parameters on the RF network model and found that our model was not very sensitive to these parameters.

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Duffy, Áine, Marie Verbanck, Amanda Dobbyn, et al. "Tissue-specific genetic features inform prediction of drug side effects in clinical trials." Science Advances 6, no. 37 (2020): eabb6242. http://dx.doi.org/10.1126/sciadv.abb6242.

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Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features—tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint—confer a 2.6-fold greater risk of side effects, compared to genes without such features. The presence of eQTL in multiple tissues resulted in more unique phenotypes driven by GWA loci, suggesting that drugs delivered to multiple tissues can induce several side effects. We demonstrate the utility of PheWAS and eQTL data from multiple tissues for informing drug side effect prediction and highlight the need for tissue-specific drug delivery.

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Chen, Lei, Tao Huang, Jian Zhang, et al. "Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/485034.

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A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

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Zhou, Mengshi, Yang Chen, and Rong Xu. "A Drug-Side Effect Context-Sensitive Network approach for drug target prediction." Bioinformatics 35, no. 12 (2018): 2100–2107. http://dx.doi.org/10.1093/bioinformatics/bty906.

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Shaked, Itay, Matthew A. Oberhardt, Nir Atias, Roded Sharan, and Eytan Ruppin. "Metabolic Network Prediction of Drug Side Effects." Cell Systems 2, no. 3 (2016): 209–13. http://dx.doi.org/10.1016/j.cels.2016.03.001.

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Liang, Haiyan, Lei Chen, Xian Zhao, and Xiaolin Zhang. "Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy." Computational and Mathematical Methods in Medicine 2020 (May 9, 2020): 1–16. http://dx.doi.org/10.1155/2020/1573543.

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Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy.

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Seo, Sukyung, Taekeon Lee, and Youngmi Yoon. "Prediction of Drug Side Effects Based on Drug-Related Information." Journal of Korean Institute of Information Technology 17, no. 12 (2019): 21–28. http://dx.doi.org/10.14801/jkiit.2019.17.12.21.

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Chen, Y. H., Y. T. Shih, C. S. Chien, and C. S. Tsai. "Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach." PLOS ONE 17, no. 12 (2022): e0266435. http://dx.doi.org/10.1371/journal.pone.0266435.

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We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the drug information with similar characteristics from the datasets of known drugs and side effect networks. The heterogeneous graph networks explore the potential side effects of drugs by inferring the relationship between similar drugs and related side effects. This novel in silico method will shorten the time spent in uncovering the unseen side effects within routine drug prescriptions while highlighting the relevance of exploring drug mechanisms from well-documented drugs. In our experiments, we inquire about the drugs Vancomycin, Amlodipine, Cisplatin, and Glimepiride from a trained model, where the parameters are acquired from the dataset SIDER after training. Our results show that the performance of the GCNMLP on these three datasets is superior to the non-negative matrix factorization method (NMF) and some well-known machine learning methods with respect to various evaluation scales. Moreover, new side effects of drugs can be obtained using the GCNMLP.

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Zheng, Yi, Wentao Zhao, Chengcheng Sun, and Qian Li. "Drug Side-Effect Prediction Using Heterogeneous Features and Bipartite Local Models." Computers, Materials & Continua 60, no. 2 (2019): 481–96. http://dx.doi.org/10.32604/cmc.2019.05536.

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Niu, Yanqing, and Wen Zhang. "Quantitative prediction of drug side effects based on drug-related features." Interdisciplinary Sciences: Computational Life Sciences 9, no. 3 (2017): 434–44. http://dx.doi.org/10.1007/s12539-017-0236-5.

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Lounkine, Eugen, Michael J. Keiser, Steven Whitebread, et al. "Large-scale prediction and testing of drug activity on side-effect targets." Nature 486, no. 7403 (2012): 361–67. http://dx.doi.org/10.1038/nature11159.

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Pancino, Niccolò, Yohann Perron, Pietro Bongini, and Franco Scarselli. "Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph-of-Graphs Domain." Mathematics 10, no. 23 (2022): 4550. http://dx.doi.org/10.3390/math10234550.

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Drug side effects (DSEs), or adverse drug reactions (ADRs), constitute an important health risk, given the approximately 197,000 annual DSE deaths in Europe alone. Therefore, during the drug development process, DSE detection is of utmost importance, and the occurrence of ADRs prevents many candidate molecules from going through clinical trials. Thus, early prediction of DSEs has the potential to massively reduce drug development times and costs. In this work, data are represented in a non-euclidean manner, in the form of a graph-of-graphs domain. In such a domain, structures of molecule are represented by molecular graphs, each of which becomes a node in the higher-level graph. In the latter, nodes stand for drugs and genes, and arcs represent their relationships. This relational nature represents an important novelty for the DSE prediction task, and it is directly used during the prediction. For this purpose, the MolecularGNN model is proposed. This new classifier is based on graph neural networks, a connectionist model capable of processing data in the form of graphs. The approach represents an improvement over a previous method, called DruGNN, as it is also capable of extracting information from the graph-based molecular structures, producing a task-based neural fingerprint (NF) of the molecule which is adapted to the specific task. The architecture has been compared with other GNN models in terms of performance, showing that the proposed approach is very promising.

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Guney, Emre. "Revisiting Cross-Validation of Drug Similarity Based Classifiers Using Paired Data." Genomics and Computational Biology 4, no. 1 (2017): 100047. http://dx.doi.org/10.18547/gcb.2018.vol4.iss1.e100047.

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Following the recent availability of high-throughput data for drug discovery, computational methods, especially machine learning based approaches, have gained remarkable attention. A number of studies use chemical, target and side effect similarity between drugs to build knowledge-based models that predict drug indications and drug-drug interactions. In light of previous works demonstrating the perils of cross-validation using paired data, in this study, we employ a disjoint cross validation approach for similarity-based drug-drug interaction (DDI) prediction and we investigate the prediction accuracy of classifier under various settings. Our results point to the dependence on the cross validation strategy used to evaluate prediction accuracy of drug similarity-based classifiers operating on paired data such as pharmacokinetic interactions between drugs.

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Huang, Wei, Chunyan Li, Ying Ju, and Yan Gao. "The Next Generation of Machine Learning in DDIs Prediction." Current Pharmaceutical Design 27, no. 23 (2021): 2728–36. http://dx.doi.org/10.2174/1381612827666210127122312.

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Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to find all the adverse reactions through drug experiments. Therefore, new attempts have been made in using computational techniques to deal with this problem. In this review, we conduct a review of the literature on applying the computational method for predicting drug-drug interactions. We first briefly introduce the widely used data sets. After that, we elaborate on the existing state-of-art deep learning models for drug-drug interactions prediction. We also discussed the challenges and opportunities of applying the computational method in drug-drug interactions prediction.

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Lim, Seungsoo, Hayon Lee, and Youngmi Yoon. "Prediction of New Drug-Side Effect Relation using Word2Vec Model-based Word Similarity." Journal of Korean Institute of Information Technology 18, no. 11 (2020): 25–33. http://dx.doi.org/10.14801/jkiit.2020.18.11.25.

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Yamanishi, Yoshihiro, Edouard Pauwels, and Masaaki Kotera. "Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces." Journal of Chemical Information and Modeling 52, no. 12 (2012): 3284–92. http://dx.doi.org/10.1021/ci2005548.

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Zhou, Mengshi, Chunlei Zheng, and Rong Xu. "Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery." Bioinformatics 36, Supplement_1 (2020): i436—i444. http://dx.doi.org/10.1093/bioinformatics/btaa451.

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Abstract Motivation Predicting drug–target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration. Results We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision–recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value &lt; 0.0001]. The EHR-based case–control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value &lt; 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients’ EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases. Availability and implementation nlp.case.edu/public/data/TargetPredict.

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Che, Jingang, Lei Chen, Zi-Han Guo, Shuaiqun Wang, and Aorigele. "Drug Target Group Prediction with Multiple Drug Networks." Combinatorial Chemistry & High Throughput Screening 23, no. 4 (2020): 274–84. http://dx.doi.org/10.2174/1386207322666190702103927.

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Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

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Mohanapriya, D., and Dr R. Beena. "Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 281–89. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21042.

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In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditional Random Field (BILSTM-CRF). From the sentences in the literature data, a dependency graph was constructed which discovered the relations between gene and drug. The product of the drug on phenotype rule was identified by the Gene Regulation Score (GRS) which creates the drug-topic probability matrix. The probability matrix and a syntactic distance measure was processed in Classification and Regression Tree (CART), Naïve Bayes (NB), logistic regression and Convolutional Neural Network (CNN) classifiers for estimating the drug-gene and drug-side effects. Besides the literature data, social media offers various promising resources with massive volume of data that can be useful in the drug-phenotype and drug-side effect association prediction. So in this paper, drug information with gene, disease and side effects are extracted from different social media such as Twitter, Facebook and LinkedIn and it can be used with the literature data to provide more relevant disease and drug relations. In addition to this, topic modeling with transfer learning is introduced to consider the element categories, probability of overlapping elements and deep contextual significance of a text for better modeling of topics. The topic modeling with transfer learning shares as much knowledge as possible between the literature data and social media information for topic modeling. The topics from social media and literature data are used for creating the drug-topic matrix. The probability matrix and syntactic distance measure are given as input to CART, NB, logistic regression and CNN for estimating the drug-gene and drug-side effect association. This proposed work is named as Enhanced Topic Modeling with Transfer Leaning- IPISTON (ETPTL-IPISTON). The simulation findings exhibit that the efficiency of ETPTL-IPISTON than the traditional methods.

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Mower, Justin, Devika Subramanian, and Trevor Cohen. "Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications." Journal of the American Medical Informatics Association 25, no. 10 (2018): 1339–50. http://dx.doi.org/10.1093/jamia/ocy077.

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Abstract Objective The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring. Methods Using ≈80 million concept-relationship-concept triples extracted from the literature using the SemRep Natural Language Processing system, distributed vector representations (embeddings) were generated for concepts as functions of their relationships utilizing two unsupervised representational approaches. Embeddings for drugs and side effects of interest from two widely used reference standards were then composed to generate embeddings of drug/side-effect pairs, which were used as input for supervised machine learning. This methodology was developed and evaluated using cross-validation strategies and compared to contemporary approaches. To qualitatively assess generalization, models trained on the Observational Medical Outcomes Partnership (OMOP) drug/side-effect reference set were evaluated against a list of ≈1100 drugs from an online database. Results The employed method improved performance over previous approaches. Cross-validation results advance the state of the art (AUC 0.96; F1 0.90 and AUC 0.95; F1 0.84 across the two sets), outperforming methods utilizing literature and/or spontaneous reporting system data. Examination of predictions for unseen drug/side-effect pairs indicates the ability of these methods to generalize, with over tenfold label support enrichment in the top 100 predictions versus the bottom 100 predictions. Discussion and Conclusion Our methods can assist the pharmacovigilance process using information from the biomedical literature. Unsupervised pretraining generates a rich relationship-based representational foundation for machine learning techniques to classify drugs in the context of a putative side effect, given known examples.

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Yao, Yuanzhe, Zeheng Wang, Liang Li, et al. "An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example." Computational and Mathematical Methods in Medicine 2019 (October 1, 2019): 1–7. http://dx.doi.org/10.1155/2019/8617503.

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In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

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Dykeman, J., M. Lowerison, P. Faris, et al. "Prediction of Antiepileptic Drug Side Effects in Patients with Epilepsy (S06.007)." Neurology 78, Meeting Abstracts 1 (2012): S06.007. http://dx.doi.org/10.1212/wnl.78.1_meetingabstracts.s06.007.

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Kanji, Rakesh, Abhinav Sharma, and Ganesh Bagler. "Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs." Molecular BioSystems 11, no. 11 (2015): 2900–2906. http://dx.doi.org/10.1039/c5mb00312a.

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Knowing the importance of identification of drug features that are critical for specifying their adverse effects, we propose a generalized ordinary canonical correlation analysis model that integrates the target profiles and chemical profiles of drugs.

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Wilson, Jennifer L., Alessio Gravina, and Kevin Grimes. "From random to predictive: a context-specific interaction framework improves selection of drug protein–protein interactions for unknown drug pathways." Integrative Biology 14, no. 1 (2022): 13–24. http://dx.doi.org/10.1093/intbio/zyac002.

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Abstract With high drug attrition, protein–protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches—statistical and distance-based—and our novel per-phenotype approach, ‘context-specific interaction’ (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76–95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.

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30

Wang, Chen, and Lukasz Kurgan. "Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome." Briefings in Bioinformatics 20, no. 6 (2018): 2066–87. http://dx.doi.org/10.1093/bib/bby069.

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AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

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Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review II, no. I (2017): 1–8. http://dx.doi.org/10.31703/gdddr.2017(ii-i).01.

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In modern day, Data on different diseases and drug substances with their properties like modification, side effects, and dose requires documentation data and building library exploring, such library with vast information in every aspect needs computational methods used in CADD. Recognition of specific targets for the drug tested and defining pharmacological activity of a drug candidate based on the structure of both drug and its target, finding outside effects of drugs at the molecular level and calculation of toxicity caused by metabolism of drug applications of Computer aided drug design in the drug discovery process. We can get additional tools and websites which serve As a tool for the source of data and computational drug design are available on the web interface and being used extensively by researchers and scientists to save time and budget for speeding up the process of experiments for Novel Drug compound.

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Hwang, Youhyeon, Min Oh, and Youngmi Yoon. "Extraction of specific common genetic network of side effect pair, and prediction of side effects for a drug based on PPI network." Journal of the Korea Society of Computer and Information 21, no. 1 (2016): 115–23. http://dx.doi.org/10.9708/jksci.2016.21.1.115.

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Pauwels, Edouard, Véronique Stoven, and Yoshihiro Yamanishi. "Predicting drug side-effect profiles: a chemical fragment-based approach." BMC Bioinformatics 12, no. 1 (2011): 169. http://dx.doi.org/10.1186/1471-2105-12-169.

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Yu, Liyi, Meiling Cheng, Wangren Qiu, Xuan Xiao, and Weizhong Lin. "idse-HE: Hybrid embedding graph neural network for drug side effects prediction." Journal of Biomedical Informatics 131 (July 2022): 104098. http://dx.doi.org/10.1016/j.jbi.2022.104098.

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35

Lee, Chun Yen, and Yi‐Ping Phoebe Chen. "Descriptive prediction of drug side‐effects using a hybrid deep learning model." International Journal of Intelligent Systems 36, no. 6 (2021): 2491–510. http://dx.doi.org/10.1002/int.22389.

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Jahid, Md Jamiul, and Jianhua Ruan. "Structure-based prediction of drug side effects using a novel classification algorithm." International Journal of Computational Biology and Drug Design 9, no. 1/2 (2016): 87. http://dx.doi.org/10.1504/ijcbdd.2016.074985.

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37

CHEN, Y. Z., Z. R. LI, and C. Y. UNG. "COMPUTATIONAL METHOD FOR DRUG TARGET SEARCH AND APPLICATION IN DRUG DISCOVERY." Journal of Theoretical and Computational Chemistry 01, no. 01 (2002): 213–24. http://dx.doi.org/10.1142/s0219633602000166.

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Ligand-protein inverse docking has recently been introduced as a computer method for identification of potential protein targets of a drug. A protein structure database is searched to find proteins to which a drug can bind or weakly bind. Examples of potential applications of this method in facilitating drug discovery include: (1) identification of unknown and secondary therapeutic targets of a drug, (2) prediction of potential toxicity and side effect of an investigative drug, and (3) probing molecular mechanism of bioactive herbal compounds such as those extracted from plants used in traditional medicines. This method and recent results on its applications in solving various drug discovery problems are reviewed.

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38

Islam, Sk Mazharul, Sk Md Mosaddek Hossain, and Sumanta Ray. "DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation." PLOS ONE 16, no. 2 (2021): e0246920. http://dx.doi.org/10.1371/journal.pone.0246920.

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In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

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Sun, Yifan, Yi Xiong, Qian Xu, and Dongqing Wei. "A Hadoop-Based Method to Predict Potential Effective Drug Combination." BioMed Research International 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/196858.

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Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

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Xuan, Ping, Yangkun Cao, Tiangang Zhang, Xiao Wang, Shuxiang Pan, and Tonghui Shen. "Drug repositioning through integration of prior knowledge and projections of drugs and diseases." Bioinformatics 35, no. 20 (2019): 4108–19. http://dx.doi.org/10.1093/bioinformatics/btz182.

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Abstract Motivation Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug–disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. Results We present a method based on non-negative matrix factorization, DisDrugPred, to predict the drug-related candidate disease indications. A new type of drug similarity is firstly calculated based on their associated diseases. DisDrugPred completely integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different levels including the chemical structures of drugs, the target proteins of drugs, the diseases associated with drugs and the side effects of drugs. The prior knowledge of drugs and diseases and the sparse characteristic of drug–disease associations provide a deep biological perspective for capturing the relationships between drugs and diseases. Simultaneously, the possibility that a drug is associated with a disease is also dependant on their projections in the low-dimension feature space. Therefore, DisDrugPred deeply integrates the diverse prior knowledge, the sparse characteristic of associations and the projections of drugs and diseases. DisDrugPred achieves superior prediction performance than several state-of-the-art methods for drug–disease association prediction. During the validation process, DisDrugPred also can retrieve more actual drug–disease associations in the top part of prediction result which often attracts more attention from the biologists. Moreover, case studies on five drugs further confirm DisDrugPred’s ability to discover potential candidate disease indications for drugs. Availability and implementation The fourth type of drug similarity and the predicted candidates for all the drugs are available at https://github.com/pingxuan-hlju/DisDrugPred. Supplementary information Supplementary data are available at Bioinformatics online.

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Liang, Siqi, and Haiyuan Yu. "Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach." Bioinformatics 36, no. 16 (2020): 4490–97. http://dx.doi.org/10.1093/bioinformatics/btaa495.

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Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.

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Jaundoo, Rajeev, and Travis J. A. Craddock. "DRUGPATH: A New Database for Mapping Polypharmacology." Alberta Academic Review 2, no. 3 (2019): 4. http://dx.doi.org/10.29173/aar92.

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While there are existing databases that curate only drug, target, or pathway data for instance, none of these alone are exhaustive. The Drug Gene Pathway (DRUGPATH) meta database was created as a response to the complex treatment required for various diseases including Gulf War Illness (GWI) and post-traumatic stress disorder (PTSD), where therapy involves using multiple drugs in combination. Here, drug-drug interactions can occur due to the promiscuous nature of pharmaceuticals, which can then lead to various side effects or can alternatively be utilized towards drug repurposing. The objective was to develop a database that maps the interactions between drugs, genes, pathways, and targets for use in the treatment of complex diseases, including the prediction of off-target interactions, otherwise known as side effects. Using MATLAB and Python scripts, interactions between known drugs, genes, targets, and pathways amalgamated from numerous expert-curated sources such as PharmGKB, DrugBank, DGIdb, ConsesusPathDB, Guide to PHARMACOLOGY, HUGO Gene Nomenclature Committee, Toxin and Toxin-Target Database, repoDB, the FDA’s National Drug Code database, etc. were mapped together. The raw data was first downloaded from its source and subsequently cleaned, where extraneous information such as data from non-humans, internal identifiers, timestamps, etc. were removed. The remaining information was then integrated into an SQLite database. DRUGPATH currently contains a total of 2,632,516 unique entries, and of these, there are 54,757 unique genes, 2,632,242 unique pathways, and 31,042 unique drugs. DRUGPATH allows researchers and clinicians to discern which pathways are affected by each drug, reducing the likelihood of an adverse drug reaction occurring. The incorporation of drug, gene, target, and pathway information makes DRUGPATH a powerful resource for predicting potential side effects when designing or refining a given drug combination therapy. Not only that, but we have additionally added the FDA status, half-life, and indication for each drug whenever possible for clinical applications of this database.

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Sachdev, Kanica, and Manoj K. Gupta. "A comprehensive review of computational techniques for the prediction of drug side effects." Drug Development Research 81, no. 6 (2020): 650–70. http://dx.doi.org/10.1002/ddr.21669.

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Samizadeh, Mina, and Behrouz Minaei-Bidgoli. "Drug-target Interaction Prediction by Metapath2vec Node Embedding in Heterogeneous Network of Interactions." International Journal on Artificial Intelligence Tools 29, no. 01 (2020): 2050001. http://dx.doi.org/10.1142/s0218213020500013.

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Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-down huge experimental space and reduce the costs and help to faster drug development as well as predicting the side effects and potential function of new drugs. Many approaches have been taken by researchers to solve DTI problem and enhance the accuracy of methods. State-of-the-art approaches are based on various techniques, such as deep learning methods-like stacked auto-encoder-, matrix factorization, network inference, and ensemble methods. In this work, we have taken a new approach based on node embedding in a heterogeneous interaction network to obtain the representation of each node in the interaction network and then use a binary classifier such as logistic regression to solve this prominent problem in the pharmaceutical industry. Most introduced network-based methods use a homogeneous network of interactions as their input data whereas in the real word problem there exist other informative networks to help to enhance the prediction and by considering the homogeneous networks we lose some precious network information. Hence, in this work, we have tried to work on the heterogeneous network and have improved the accuracy of methods in comparison to baseline methods.

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Kulemina, Lidia V., and David A. Ostrov. "Prediction of Off-Target Effects on Angiotensin-Converting Enzyme 2." Journal of Biomolecular Screening 16, no. 8 (2011): 878–85. http://dx.doi.org/10.1177/1087057111413919.

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The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)–approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs that enhance catalytic efficiency of ACE2 in vitro. These data demonstrate that libraries of approved drugs can be rapidly screened to identify potential side effects due to interactions with specific proteins other than the intended targets.

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Wang, Meng, Haofen Wang, Xing Liu, Xinyu Ma, and Beilun Wang. "Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study." JMIR Medical Informatics 9, no. 6 (2021): e28277. http://dx.doi.org/10.2196/28277.

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Background Minimizing adverse reactions caused by drug-drug interactions (DDIs) has always been a prominent research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discovering various DDIs. However, these data contain a huge amount of noise and provide knowledge bases that are far from being complete or used with reliability. Most existing studies focus on predicting binary DDIs between drug pairs and ignore other interactions. Objective Leveraging both drug knowledge graphs and biomedical text is a promising pathway for rich and comprehensive DDI prediction, but it is not without issues. Our proposed model seeks to address the following challenges: data noise and incompleteness, data sparsity, and computational complexity. Methods We propose a novel framework, Predicting Rich DDI, to predict DDIs. The framework uses graph embedding to overcome data incompleteness and sparsity issues to make multiple DDI label predictions. First, a large-scale drug knowledge graph is generated from different sources. The knowledge graph is then embedded with comprehensive biomedical text into a common low-dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. Results To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world data sets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy. Conclusions We propose a novel framework, Predicting Rich DDI, to predict DDIs. Using rich DDI information, it can competently predict multiple labels for a pair of drugs across numerous domains, ranging from pharmacological mechanisms to side effects. To the best of our knowledge, this framework is the first to provide a joint translation-based embedding model that learns DDIs by integrating drug knowledge graphs and biomedical text simultaneously in a common low-dimensional space. The model also predicts DDIs using multiple labels rather than single or binary labels. Extensive experiments were conducted on real-world data sets to demonstrate the effectiveness and efficiency of the model. The results show our proposed framework outperforms several state-of-the-art baselines.

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Gao, Yu-Fei, Lei Chen, Guo-Hua Huang, et al. "Prediction of Drugs Target Groups Based on ChEBI Ontology." BioMed Research International 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/132724.

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Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset—the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.

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B, Nithya, and Anitha G. "Drug Side-effects Prediction using Hierarchical Fuzzy Deep Learning for Diagnosing Specific Disease." International Journal of Engineering Trends and Technology 70, no. 8 (2022): 140–48. http://dx.doi.org/10.14445/22315381/ijett-v70i8p214.

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Zhao, Xian, Lei Chen, and Jing Lu. "A similarity-based method for prediction of drug side effects with heterogeneous information." Mathematical Biosciences 306 (December 2018): 136–44. http://dx.doi.org/10.1016/j.mbs.2018.09.010.

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Domingo-Fernández, Daniel, Yojana Gadiya, Abhishek Patel, et al. "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery." PLOS Computational Biology 18, no. 2 (2022): e1009909. http://dx.doi.org/10.1371/journal.pcbi.1009909.

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Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

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