MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

The aromatic composition and key odorants of young red wines produced from Cabernet Sauvignon, Cabernet Franc, and Cabernet Gernischet wines were compared and the reasons for the difference in their aromatic compounds were discussed. Forty-three odorants were detected in Cabernet Sauvignon and Cabernet Franc wines compared to 50 in Cabernet Gernischet wine. Quantitatively, acids formed the most abundant group in the aromatic components of the 3 wines, followed by alcohols and esters. Compared to Cabernet Sauvignon and Cabernet Franc wines, the profiles of alcohols and esters for Cabernet Gernischet wine were more diverse. Monoterpenes, namely, 4-terpinenol, citronellol, and nerol, were found solely in Cabernet Gernischet wine. Only 10 compounds, namely, ethyl octanoate, ethyl hexanoate, isoamyl acetate, ethyl butyrate, beta-damascenone, ethyl decanoate, isoamyl alcohol, acetic acid, octanoic acid, and phenylethyl acetate, were always present in the 3 wines at concentrations higher than their threshold values. However, ethyl octanoate, ethyl hexanoate, and isoamyl acetate were found to jointly contribute to 97%, 98.9%, and 99% of the global aroma of Cabernet Sauvignon, Cabernet Franc, and Cabernet Gernischet wines, respectively. This result showed that the aroma indistinguishableness of the 3 wines was mainly due to the dominance of the fruity notes exerted by the ethyl esters and, to a lesser extent, to the contribution of varietal aromatic compounds to the global aroma of the wines.

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Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer’s disease, Parkinson’s disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

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