One of the interesting characteristics of crime data is that criminal cases are often interrelated. Criminal acts may be similar, and similar incidents may occur consecutively by the same offender or by the same criminal group. Among many machine learning algorithms, network‐based approaches are well‐suited to reflect these associative characteristics. Applying machine learning to criminal networks composed of cases and their associates can predict potential suspects. This narrows the scope of an investigation, saving time and cost. However, inference from criminal networks is not straightforward as it requires being able to process complex information entangled with case‐to‐case, person‐to‐person, and case‐to‐person connections. Besides, being useful at a crime scene requires urgency. However, predictions from network‐based machine learning algorithms are generally slow when the data is large and complex in structure. These limitations are an immediate barrier to any practical use of the criminal network geared by machine learning. In this study, we propose a criminal network‐based suspect prediction framework. The network we designed has a unique structure, such as a sandwich panel, in which one side is a network of crime cases and the other side is a network of people such as victims, criminals, and witnesses. The two networks are connected by relationships between the case and the persons involved in the case. The proposed method is then further developed into a fast inference algorithm for large‐scale criminal networks. Experiments on benchmark data showed that the fast inference algorithm significantly reduced execution time while still being competitive in performance comparisons of the original algorithm and other existing approaches. Based on actual crime data provided by the Korean National Police, several examples of how the proposed method is applied are shown.

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.

SUMMARY: Immune diseases have a strong genetic component with Mendelian patterns of inheritance. While the tight association has been a major understanding in the underlying pathophysiology for the category of immune diseases, the common features of these diseases remain unclear. Based on the potential commonality among immune genes, we design Gene Ranker for key gene identification. Gene Ranker is a network-based gene scoring algorithm that initially constructs a backbone network based on protein interactions. Patient gene expression networks are added into the network. An add-on process screens the networks of weighted gene co-expression network analysis (WGCNA) on the samples of immune patients. Gene Ranker is disease-specific; however, any WGCNA network that passes the screening procedure can be added on. With the constructed network, it employs the semi-supervised learning for gene scoring. RESULTS: The proposed method was applied to immune diseases. Based on the resulting scores, Gene Ranker identified potential key genes in immune diseases. In scoring validation, an average area under the receiver operating characteristic curve of 0.82 was achieved, which is a significant increase from the reference average of 0.76. Highly ranked genes were verified through retrieval and review of 27 million PubMed literatures. As a typical case, 20 potential key genes in rheumatoid arthritis were identified: 10 were de facto genes and the remaining were novel. AVAILABILITY AND IMPLEMENTATION: Gene Ranker is available at http://www.alphaminers.net/GeneRanker/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.