A Random Walk with Restart Model Based on Common Neighbors for Predicting the Clinical Drug Combinations on Coronary Heart Disease

As the approaching of the clinical big data era, the prediction of whether drugs can be used in combination in clinical practice is a fundamental problem in the analysis of medical data. Compared with high-throughput screening, it is more cost-effective to treat this problem as a link prediction problem and predict by algorithms. Inspired by the rule of combined clinical medication, a new computational model is proposed. The drug-drug combination was predicted by combining the number of adjacent complete subgraphs shared by the two points with the restart random walk algorithm. The model is based on the semisupervised random walk algorithm, and the same neighborhood is used to improve the random walk with restart (CN-RWR). The algorithm can effectively improve the prediction performance and assign a score to any combination of drugs. To fairly compare the predictive performance of the improved model with that of the random walk with restart model (RWR), a cross-validation of the two models on the same drug data was performed. The AUROC of CN-RWR and RWR under the LOOCV validation framework is 0.9741 and 0.9586, respectively, and the improved model results are more reliable. In addition, the top 3 predictive drug combinations have been approved by the public. The new model is expected that this model can be extended to predict the use of combination drugs for other diseases to find combinations of drugs with potential clinical benefits.

Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk

Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.

Personalized Scholar Recommendation Based on Multi-Dimensional Features

The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot topic. Thus, we propose a personalized scholar recommendation approach, Mul-RSR (Multi-dimensional features based Research Scholar Recommendation), which improves accuracy and interpretability. In this work, Mul-RSR aims to provide personalized recommendation for academic social platforms. Mul-RSR uses the Doc2Vec text model and the random walk algorithm to calculate textual similarity and social relevance to measure the correlation between scholars. It is able to recommend Top-N scholars for each scholar based on multi-layer perception and attention mechanism. To evaluate the proposed approach, we conduct a series of experiments based on public and self-collected ResearchGate datasets. The results demonstrate that our approach improves the recommendation hit rate, and the hit rate reaches 59.31% when the N value is 30. Through these evaluations, we show Mul-RSR can provide a more solid scientific decision-making basis and achieve a better recommendation effect.

Amoebic Foraging Model of Metastatic Cancer Cells

The Lévy walk is a pattern that is often seen in the movement of living organisms; it has both ballistic and random features and is a behavior that has been recognized in various animals and unicellular organisms, such as amoebae, in recent years. We proposed an amoeba locomotion model that implements Bayesian and inverse Bayesian inference as a Lévy walk algorithm that balances exploration and exploitation, and through a comparison with general random walks, we confirmed its effectiveness. While Bayesian inference is expressed only by P(h) = P(h|d), we introduce inverse Bayesian inference expressed as P(d|h) = P(d) in a symmetry fashion. That symmetry contributes to balancing contracting and expanding the probability space. Additionally, the conditions of various environments were set, and experimental results were obtained that corresponded to changes in gait patterns with respect to changes in the conditions of actual metastatic cancer cells.

Quantitative Analysis of Genomic Sequences of Virus RNAs Using a Metric-Based Algorithm

The worldwide spread of SARS-CoV-2 virus increases interest in the research of virus genomics and the creation of more advanced study methods. This work aims to develop a new fast DNA walk algorithm for one-dimensional visualization of RNAs based on a big-data method and comparative examination of several viruses and their lines and strains. In this work, a new metric-based algorithm for quantitative and visual analyses of RNAs is proposed and considered. It allows finding any fragments of genomic sequences using the Hamming distance between the binary-expressed RNA characters and symbols of a fragment under the search and building one-dimensional trajectories of genomic walks convenient for quantitative and qualitative analyses of RNAs and DNAs. Similarly, human-language texts can be processed and compared with genomic sequences.This algorithm is used to investigate the complete genomic sequences of SARS CoV-2, MERS, Dengue, and Ebola viruses available from Genbank and GISAID databases. The distributions of atg codon-starting triplets along with these sequences are built and considered as their atg-schemes. Additionally to the atg-walks, single-symbols distributions are calculated to detect the codon-content mutations, which do not change the atg-triplet coordinates along with genomic sequences. The visual analyses of distributions consisting of several hundred triplets enable us to define the level of stability of RNAs towards essential mutations and perform their classing. Statistical studies are applied to distributions of the inter-atg and inter-symbol distances along with genomic sequences. The fractal dimension values of these distributions are calculated, enabling them to correspond to the mutations discovered by Hamming walks and fractal-dimension values of several ten virus samples investigated here. The developed metric-based-based algorithm allows building one-dimensional RNA schemes of different scale levels and effectively analyzing the virus mutations with their classing.