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.

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.

Artificial proto-modelling: building precursors of a next standard model from simplified model results

We present a novel algorithm to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed “proto-models”, which are tested against simplified-model results from ATLAS and CMS (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the SM hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Demonstrating our method by running over the experimental results in the SModelS database, we find as currently best-performing proto-model a top partner, a light-flavor quark partner, and a lightest neutral new particle with masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The corresponding global p-value for the SM hypothesis is pglobal≈ 0.19; by construction no look-elsewhere effect applies.