Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

BackgroundLung cancer, especially non-small cell lung cancer, is a leading cause of malignant tumor death worldwide. Understanding the mechanisms employed by the main regulators, such as microRNAs (miRNAs) and transcription factors (TFs), still remains elusive. The patterns of their cooperation and biological functions in the synergistic regulatory network have rarely been studied.ResultsHere, we describe the first miRNA-TF synergistic regulation network in human lung cancer. We identified important regulators (MYC, NFKB1, miR-590, and miR-570) and significant miRNA-TF synergistic regulatory motifs by random simulations. The two most significant motifs were the co-regulation of miRNAs and TFs, and TF-mediated cascade regulation. We also developed an algorithm to uncover the biological functions of the human lung cancer miRNA-TF synergistic regulatory network (regulation of apoptosis, cellular protein metabolic process, and cell cycle), and the specific functions of each miRNA-TF synergistic subnetwork. We found that the miR-17 family exerted important effects in the regulation of non-small cell lung cancer, such as in proliferation and cell cycle regulation by targeting the retinoblastoma protein (RB1) and forming a feed forward loop with the E2F1 TF. We proposed a model for the miR-17 family, E2F1, and RB1 to demonstrate their potential roles in the occurrence and development of non-small cell lung cancer.ConclusionsThis work will provide a framework for constructing miRNA-TF synergistic regulatory networks, function analysis in diseases, and identification of the main regulators and regulatory motifs, which will be useful for understanding the putative regulatory motifs involving miRNAs and TFs, and for predicting new targets for cancer studies.

To provide an ability to characterize local features for the chaotic neural network (CNN), Gauss wavelet is used for the self-feedback of the CNN with the dilation parameter acting as the bifurcation parameter. The exponentially decaying dilation parameter and the chaotically varying translation parameter not only govern the wavelet self-feedback transform but also enable the CNN to generate complex dynamics behavior preventing the network from being trapped in the local minima. Analysis of the energy function of the CNN indicates that the local characterization ability of the proposed CNN is effectively provided by the wavelet self-feedback in the manner of inverse wavelet transform and that the proposed CNN can achieve asymptotical stability. The experimental results on traveling salesman problem (TSP) suggest that the proposed CNN has a higher average success rate for obtaining globally optimal or near-optimal solutions.

The new-generation wireless communication networks are envisioned to offer higher sum data rates along with the required level of fairness. Previous works tend to suffer from a decayed performance as subcarriers become relatively insufficient in allocation to users. To maximize the sum data rates and ensure the required level of proportional fairness, this paper presents a hybrid OFDMA resource allocation scheme which uses Hungarian algorithm combined with a greedy method for subcarrier allocation and uses bee colony optimization for power allocation. The proposed subcarrier allocation scheme can make full use of advantages of both globally optimal Hungarian algorithm in enhancing sum data rates and locally optimal greedy method in maintaining a reasonable fairness level and can make Hungarian algorithm work in a searching mode for further improvement of sum data rates and fairness. The proposed power allocation scheme can converge to the required level of proportional fairness but with higher sum data rates if the subcarrier allocation does not achieve the required fairness. Simulation results show that the proposed scheme can obtain the required level of proportional fairness but with higher sum data rates even if subcarriers are relatively insufficient in allocation to users. Complexity analysis shows the proposed method has moderate complexity.

No abstract