RESUMEN
The identification of potential interactions and relationships between diseases and drugs is significant in public health care and drug discovery. As we all know, experimenting to determine the drug-disease interactions is very expensive in both time and money. However, there are still many drug-disease associations that are still undiscovered and potential. Therefore, the development of computational methods to explore the relationship between drugs and diseases is very important and essential. Many computational methods for predicting drug-disease associations have been developed based on known interactions to learn potential interactions of unknown drug-disease pairs. In this paper, we propose 3 new main groups of meta-paths based on the heterogeneous biological network of drug-protein-disease objects. For each meta-path, we design a machine learning model, then an integrated learning method is formed by these models. We evaluated our approach on 3 standard datasets which are DrugBank, OMIM, and Gottlieb's dataset. Experimental results demonstrate that the proposed method is better than some recent methods such as EMP-SVD, LRSSL, MBiRW, MPG-DDA, SCMFDD,. . . in some measures such as AUC, AUPR, and F1-score.
RESUMEN
Decision making in stock market is a movement in which investors gather information and carry out complex analysis to select options, based on market variations and investor's preferences. This involves the facts of risk of return, appreciating or depreciating of stock markets in value and dynamic circumstances. We present a design to study and discover bear and bull markets from macroeconomic variables in a probabilistic manner to assist the decision-making process. Features such as return, risk, simple, and exponential moving average are represented as flexible time series. The learning method that involves conditional dependence of stock variables and inference is described by the base of Bayesian theorem. We highlight our learning method using an actual case study with a consistent stock portfolio optimization. The case study addresses a set of selected stock symbols of VN-index and the logical method is illustrated by significant rates of accuracy over a variation of types of stock symbols.
