MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.

Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease associations, and uncovering new circRNA-disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA-disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA-disease associations and can provide the most promising candidates for biological experiments.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.

BACKGROUND: Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information. RESULTS: In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins. CONCLUSIONS: Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

Antiprotozoal activity of medicinal plants against Ichthyophthirius multifiliis in goldfish (Carassius auratus).

Ichthyophthiriasis is a widespread disease in aquaculture and causes mass mortalities of fish. The development of new antiprotozoal agents for the treatment of Ichthyophthirius multifiliis infections is of increasing interest. The aim of the present study was to investigate the efficacy of 30 medicinal plants against I. multifiliis. The results showed that the methanol extracts of Magnolia officinalis and Sophora alopecuroides displayed the highest antiprotozoal activity against theronts, with 4-h LC(50) values estimated to be 2.45 and 3.43 mg L(-1), respectively. Concentrations of 2.5, 5.0, 10.0, and 20.0 mg L(-1) of M. officinalis extracts resulted in tomont mortality of 9.7, 43.7, 91.3, and 100% at 20 h, respectively. From 40 to 320 mg L(-1) of S. alopecuroides extracts, tomont mortality increased from 29.7 to 100%. Antiprotozoal efficacy against settled tomonts (2 and 10 h) was also applied; the results indicated that encysted I. multifiliis tomonts were less susceptible to these plant extract treatments. In vivo experiments demonstrated that high concentrations of M. officinalis and S. alopecuroides extracts could kill tomonts, and M. officinalis significantly reduced its reproduction (P < 0.05). These results suggested that the methanol extracts of M. officinalis and S. alopecuroides have the potential to be used as an eco-friendly approach for the control of I. multifiliis.

Bioassay-guided isolation and identification of active compounds from Semen pharbitidis against Dactylogyrus intermedius (Monogenea) in goldfish (Carassius auratus).

The present study was undertaken to isolate the active compounds responsible for the anthelmintic activity of methanol extract of Semen pharbitidis against Dactylogyrus intermedius in goldfish (Carassius auratus). The active methanol extract was fractionated on silica gel column chromatography in a bioassay-guided fractionation, eventually yielding two bioactive compounds: palmitic acid and pharnilatin A by comparing spectral data (NMR and ESI-MS) with literature values. According to in vivo anthelmintic assays, they were found to be 50% effective at the concentrations (EC(50)) of 5.3 and 1.4 mg L(-1), respectively. The promising palmitic acid and pharnilatin A from S. pharbitidis were also subjected to acute toxicity tests for the evaluation of their safety to the host (goldfish). After 48h exposure, the mortalities of goldfish were recorded, and the established LC(50) values were 2.45- and 5.29-fold higher than the corresponding EC(50), demonstrating that pharnilatins A may have better application potential than palmitic acid. The present results provide evidence that pharnilatins A might be potential source of new anti-parasitic drug for the control of Dactylogyrus.