These findings provide compelling clinical and molecular evidence to support the conclusion that CXCL8 contributes to the genesis and progression of CRC. Copyright © 2020 Li, Liu, Huang, Cai, Song, Xie, Liu, Chen, Xu, Zeng, Chu and Zeng.Pathogen-host interactions play an important role in understanding the mechanism by which a pathogen can infect its host. Some approaches for predicting pathogen-host association have been developed, but prediction accuracy is still low. In this paper, we propose a bipartite network module-based approach to improve prediction accuracy. First, a bipartite network with pathogens and hosts is constructed. Next, pathogens and hosts are divided into different modules respectively. Then, modular information on the pathogens and hosts is added into a bipartite network projection model and the association scores between pathogens and hosts are calculated. Finally, leave-one-out cross-validation is used to estimate the performance of the proposed method. https://www.selleckchem.com/products/pemigatinib-incb054828.html Experimental results show that the proposed method performs better in predicting pathogen-host association than other methods, and some potential pathogen-host associations with higher prediction scores are also confirmed by the results of biological experiments in the publically available literature. Copyright © 2020 Li, Wang, Chen and Wang.As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important research area in the bioinformatics field. In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with graph regularization based on score function (NSLRG-S). First, a lowest rank matrix is obtained after NSLRG decomposition. The lowest rank matrix preserves the local data manifold information and the global data structure information of the gene expression data. Second, we construct the Score function based on the lowest rank matrix to weight all of the features of the gene expression data and calculate the score of each feature. Third, we rank the features according to their scores and select the feature genes for cancer sample clustering. Finally, based on selected feature genes, we use the K-means method to cluster the cancer samples. The experiments are conducted on The Cancer Genome Atlas (TCGA) data. Comparative experiments demonstrate that the NSLRG-S framework can significantly improve the clustering performance. Copyright © 2020 Lu, Wang, Liu, Zheng, Kong and Zhang.Shandong indigenous pig breeds are an invaluable source of data on genetics in Chinese pigs. However, information on the genetic basis of these breeds remains limited. In this study, we used specific-locus amplified fragment sequencing to conduct whole-genome screening to investigate genetic diversity in Shandong indigenous breeds and Western pig breeds. The results showed that Duroc pigs (DD) had clear genetic relationships with Dapulian pigs (DPL; Fst = 0.4386) and Laiwu pigs (LW; Fst = 0.5134), and DPL and LW were relatively close genetically (Fst = 0.2334). In general, Shandong indigenous breeds showed greater genetic variety than the Western breeds. Both neighbor-joining trees and principal components analyses were able to differentiate the breeds, but population structure analyses indicated that the Western breeds genetically influenced the Shandong indigenous breeds to some extent. A total of 162 differentially selected regions (DSRs) with 841 genes and 157 DSRs with 707 genes were identified in DPL and LW, respectively. Gene annotation of the selected regions identified a series of genes regulating immunity and fat deposition. Our data confirm the rationality and accuracy of the current classification of pig breeds in Shandong province. Our results point to candidate genes in Shandong indigenous pig breeds and further promote the importance of follow-up research on functional verification. Copyright © 2020 Qin, Li, Li, Chen and Zeng.Different genes have their protein products localized in various subcellular compartments. The diversity in protein localization may serve as a gene characteristic, revealing gene essentiality from a subcellular perspective. To measure this diversity, we introduced a Subcellular Diversity Index (SDI) based on the Gene Ontology-Cellular Component Ontology (GO-CCO) and a semantic similarity measure of GO terms. Analyses revealed that SDI of human genes was well correlated with some known measures of gene essentiality, including protein-protein interaction (PPI) network topology measurements, dN/dS ratio, homologous gene number, expression level and tissue specificity. In addition, SDI had a good performance in predicting human essential genes (AUC = 0.702) and drug target genes (AUC = 0.704), and drug targets with higher SDI scores tended to cause more side-effects. The results suggest that SDI could be used to identify novel drug targets and to guide the filtering of drug targets with fewer potential side effects. Finally, we developed a user-friendly online database for querying SDI score for genes across eight species, and the predicted probabilities of human drug target based on SDI. The online database of SDI is available at http//www.cuilab.cn/sdi. Copyright © 2020 Jia, Zhou and Cui.Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA-protein interactions are usually involved. Therefore, identifying lncRNA-protein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA-protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA-protein interaction predictors usually require prior knowledge of lncRNA-protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA-protein interactions. In this paper, we explored a novel way to predict lncRNA-protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA-protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods.