MOTIVATION: Polypharmacy side effects should be carefully considered for new drug development. However, considering all the complex drug-drug interactions that cause polypharmacy side effects is challenging. Recently, graph neural network (GNN) models have handled these complex interactions successfully and shown great predictive performance. Nevertheless, the GNN models have difficulty providing intelligible factors of the prediction for biomedical and pharmaceutical domain experts. METHOD: A novel approach, graph feature attention network (GFAN), is presented for interpretable prediction of polypharmacy side effects by emphasizing target genes differently. To artificially simulate polypharmacy situations, where two different drugs are taken together, we formulated a node classification problem by using the concept of line graph in graph theory. RESULTS: Experiments with benchmark datasets validated interpretability of the GFAN and demonstrated competitive performance with the graph attention network in a previous work. And the specific cases in the polypharmacy side-effect prediction experiments showed that the GFAN model is capable of very sensitively extracting the target genes for each side-effect prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/SunjooBang/Polypharmacy-side-effect-prediction.

Presents corrections to author information in the above named paper.

BACKGROUND: Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. METHODS: In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug. RESULTS AND CONCLUSIONS: The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets.

BACKGROUND: The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing 'n-of-1 utility' (n potential diseases of one patient) to human disease network-the translational disease network. RESULTS: We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. CONCLUSIONS: The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

In recent years, there has been numerous studies constructing a disease network with diverse sources of data. Many researchers attempted to extend the usage of the disease network by employing machine learning algorithms on various problems such as prediction of comorbidity. The relations between diseases can further be specified into causal relations. When causality is laid on the edges in the network, prediction for comorbid diseases can be more improved. However, not many machine learning algorithms have been developed to concern causality. In this study, we exploit a network based machine learning algorithm that generates comorbidity scores from a causal disease network. In order to find comorbid diseases, semi-supervised scoring for causal networks is proposed. It computes scores of entire nodes in the network when a specific node is labeled. Each score is calculated one at a time and affects to the others along causal edges. The algorithm iterates until it converges. We compared the scoring results of the causal disease network and those of simple association network. As a gold standard, we referenced the values of relative risk from prevalence database, HuDiNe. Scoring by the proposed method provides clearer distinguishability between the top-ranked diseases in the comorbidity list. This is a benefit because it allows the choosing of the most significant ones on an easier fashion. To present typical use of the resulting list, comorbid diseases of Huntington disease and pnuemonia are validated via PubMed literature, respectively.

MOTIVATION: Causality between two diseases is valuable information as subsidiary information for medicine which is intended for prevention, diagnostics and treatment. Conventional cohort-centric researches are able to obtain very objective results, however, they demands costly experimental expense and long period of time. Recently, data source to clarify causality has been diversified: available information includes gene, protein, metabolic pathway and clinical information. By taking full advantage of those pieces of diverse information, we may extract causalities between diseases, alternatively to cohort-centric researches. METHOD: In this article, we propose a new approach to define causality between diseases. In order to find causality, three different networks were constructed step by step. Each step has different data sources and different analytical methods, and the prior step sifts causality information to the next step. In the first step, a network defines association between diseases by utilizing disease-gene relations. And then, potential causalities of disease pairs are defined as a network by using prevalence and comorbidity information from clinical results. Finally, disease causalities are confirmed by a network defined from metabolic pathways. RESULTS: The proposed method is applied to data which is collected from database such as MeSH, OMIM, HuDiNe, KEGG and PubMed. The experimental results indicated that disease causality that we found is 19 times higher than that of random guessing. The resulting pairs of causal-effected diseases are validated on medical literatures. AVAILABILITY AND IMPLEMENTATION: http://www.alphaminers.net CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.