Extensive research on tumor suppressor genes (TSGs) is helpful to understand the pathogenesis of cancer and design effective treatments. However, using traditional experiments to identify TSGs is of high costs and time-consuming. It is an alternative way to design effective computational methods for screening out latent TSGs. Up to now, some computational methods have been proposed to predict new TSGs. However, these methods did not contain a learning procedure to extract essential properties of validated TSGs, reducing their efficiencies. In this study, a novel computational method was proposed to identify latent TSGs. To this end, we downloaded the validated TSGs from the TSGene database (Version 1.0). These TSGs together with other genes were represented by features that were extracted from protein-protein interaction networks in STRING via a powerful network embedding method, Mashup. Then, thirty random forest models were constructed and used to predict latent TSGs. 135 inferred TSGs were obtained, where 28 genes have been included in the TSGene database (Version 2.0). Our method had better performance than some previous methods according to the validated TSGs in the TSGene database (Version 2.0). For the rest 107 inferred TSGs, some of them can be confirmed to be TSGs with solid literature support. Finally, our method can overcome the defects that only genes with strong associations to validated TSGs can be identified because we obtained several inferred TSGs that had weak associations to validated TSGs and can be novel TSGs with high probabilities.INDEX TERMS Tumor suppressor gene, network embedding method, mashup, machine learning, random forest.

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Fuzzy job-shop scheduling problems (FJSPs) with various imprecise factors are a category of combination optimization problems known as non-deterministic polynomial-hard problems. In this paper, a hybrid algorithm HICATS combining discrete imperialist competition algorithm (ICA) and Tabu search (TS) is proposed to solve FJSPs with fuzzy processing time and fuzzy due date. The objective function is maximizing the minimum agreement index, which is on the basis of the agreement index of fuzzy due date and fuzzy completion time. In the proposed algorithm, ICA conducts the global search and TS performs the local search. The imperialist is used to guide the colonies in the same empire. So, local search approach based on TS is applied to the imperialist to perform fine-grained exploitation. The 6 × 6 and 10 × 10 FJSPs with fuzzy processing time and fuzzy due date are tested to evaluate the performance of the proposed algorithm HICATS in this paper. The highly effective performance of HICATS is shown against the best performing algorithms from the literature. Experimental results demonstrate the advantages of our proposed algorithm HICATS on the feasibility and robustness compared with other algorithms.INDEX TERMS Discrete imperialist competition algorithm, Tabu search, fuzzy job-shop scheduling problem, fuzzy processing time and fuzzy due date, maximizing the minimum agreement index.

The comparison results demonstrate that mRMR-ICA can effectively delete redundant genes to ensure that the algorithm selects fewer informative genes to get better classification results. It also can shorten calculation time and improve efficiency.