Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function.However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses . CC-BY-NC-ND 4.
In recent years, the software applications for medical assistance, including the telerehabilitation, have known a high and a continuous presence in the medical area. The ePHoRt is a Web-based platform for the remote home monitoring rehabilitation exercises in patients after hip replacement surgery. It involves a learning phase and a serious game scheme for the execution and evaluation of the exercises as part of a therapeutic program. Modular software architecture is proposed, under the patient perspective, to be used as a reference model for researchers or professionals who wish to carry out tele-rehabilitation platforms, and to guarantee security, flexibility, and scalability. The architecture incorporates two main components. The first one manages the patient' therapeutic programs taking into account two principles: 1) maintain loose coupling between the layers of the framework and 2) Don't Repeat Yourself (DRY). The second one evaluates the performed exercises in real time considering an independent acquisition mechanism for the patient movements and two artificial algorithms. The first algorithm allows evaluating the quality of the movements, while the second one allows assessing the levels of pain intensity by recognizing the patient' emotions when performing the movements. Details of the components and the meta-model of the architecture are presented and discussed considering their advantages and disadvantages.
The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.
Rice grain production is important for the world economy. Determining the moisture content of the grains, at several stages of production, is crucial for controlling the quality, safety, and storage of the grain. This work inspects how well rice images from global and local descriptors work for determining the moisture content of the grains using artificial vision and intelligence techniques. Three sets of images of rice grains from the INIAP 12 variety (National Institute of Agricultural Research of Ecuador) were captured with a mobile camera. The first one with natural light and the other ones with a truncated pyramid-shaped structure. Then, a set of global descriptors (color, texture) and a set of local descriptors (AZAKE, BRISK, ORB, and SIFT) in conjunction with the dominate technique bag of visual words (BoVW) were used to analyze the content of the image with classification and regression algorithms. The results show that detecting humidity through images with classification and regression algorithms is possible. Finally, f1-score values of at least 0.9 were accomplished for global color descriptors and of 0.8 for texture descriptors, in contrast to the local descriptors (AKAZE, BRISK, and SIFT) that reached up to an f1-score of 0.96.
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