Search results for "Test set"
showing 10 items of 50 documents
Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree
2021
Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation p…
Automatic construction of test sets: Theoretical approach
2005
We consider the problem of automatic construction of complete test set (CTS) from program text. The completeness criterion adopted is C1, i.e., it is necessary to execute all feasible branches of program at least once on the tests of CTS. A simple programming language is introduced with the property that the values used in conditional statements are not arithmetically deformed. For this language the CTS problem is proved to be algorithmically solvable and CTS construction algorithm is obtained. Some generalizations of this language containing counters, stacks or arrays are considered where the CTS problem remains solvable. In conclusion the applications of the obtained results to CTS constr…
Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology
2021
[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explain-ability to the estimated value. Methods: The network was des…
A Large-Scale Empirical Evaluation of Cross-Validation and External Test Set Validation in (Q)SAR.
2013
(Q)SAR model validation is essential to ensure the quality of inferred models and to indicate future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to accept the (Q)SAR model, and to approve its use in real world scenarios as alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model, in particular whether to employ variants of cross-validation or external test set validation, is still under discussion. In this paper, we empirically compare a k-fold cross-validation with external test set validation. To this end we introduce a workflow allowing to realistically simulate t…
libvdwxc: A library for exchange-correlation functionals in the vdW-DF family
2017
We present libvdwxc, a general library for evaluating the energy and potential for the family of vdW-DF exchange--correlation functionals. libvdwxc provides an efficient implementation of the vdW-DF method and can be interfaced with various general-purpose DFT codes. Currently, the GPAW and Octopus codes implement interfaces to libvdwxc. The present implementation emphasizes scalability and parallel performance, and thereby enables \textit{ab initio} calculations of nanometer-scale complexes. The numerical accuracy is benchmarked on the S22 test set whereas parallel performance is benchmarked on ligand-protected gold nanoparticles ($\text{Au}_{144}(\text{SC}_{11}\text{NH}_{25})_{60}$) up to…
FABC: Retinal Vessel Segmentation Using AdaBoost
2010
This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…
Modeling Drug-Induced Anorexia by Molecular Topology
2012
Molecular topology (MT) has demonstrated to be a very good technique for describing molecular structures and to predict physical, chemical, and biological properties of compounds. In this paper, a topological-mathematical model based on MT has been developed for identifying drug compounds showing anorexia as a side effect. An external validation (test set) has been carried out, yielding over an 80% correct classification in the active and inactive compounds. These results reinforce the role of MT as a potential useful tool for predicting drug side effects.
Integrated emitter local loss prediction using artificial neural networks.
2010
This paper describes an application of artificial neural networks (ANNs) to the prediction of local losses from integrated emitters. First, the optimum input-output combination was determined. Then, the mapping capability of ANNs and regression models was compared. Afterwards, a five-input ANN model, which considers pipe and emitter internal diameter, emitter length, emitter spacing, and pipe discharge, was used to develop a local losses predicting tool which was obtained from different training strategies while taking into account a completely independent test set. Finally, a performance index was evaluated for the test emitter models studied. Emitter data with low reliability were removed…
Modeling and Parameter Identification of Deflections in Planetary Stage of Wind Turbine Gearbox
2012
The main focus of this paper is the experimental and numerical investigation of a 750[kW] wind turbine gearbox. A detailed model of the gearbox with main shaft has been created using MSC.Adams. Special focus has been put on modeling the planet carrier (PLC) in the gearbox. For this purpose experimental data from a drive train test set up has been analyzed using parameter identification to quantify misalignments. Based on the measurements a combination of main shaft misalignment and planet carrier deflection has been identified. A purely numerical model has been developed and it shows good accordance with the experimental data.
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
2016
Two extensions to the AMR smatch scoring script are presented. The first extension com-bines the smatch scoring script with the C6.0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs. This first extension results in 4% gain over the state-of-art CAMR baseline parser by adding to it a manually crafted wrapper fixing the identified CAMR parser errors. The second extension combines a per-sentence smatch with an en-semble method for selecting the best AMR graph among the set of AMR graphs for the same sentence. This second modification au-tomatically yields further 0.4% gain when ap-plied to outputs of two nondeterministic…