Search results for "Test set"
showing 10 items of 50 documents
Virtual lock-and-key approach: The in silico revival of Fischer model by means of molecular descriptors
2010
Abstract In the last years the application of computational methodologies in the medicinal chemistry fields has found an amazing development. All the efforts were focused on the searching of new leads featuring a close affinity on a specific biological target. Thus, different molecular modeling approaches in simulation of molecular behavior for a specific biological target were employed. In spite of the increasing reliability of computational methodologies, not always the designed lead, once synthesized and screened, are suitable for the chosen biological target. To give another chance to these compounds, this work tries to resume the old concept of Fischer lock-and-key model. The same can …
Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles
1998
A substructural approach to quantitative structure−property relationships based on the spectral moments of the edge adjacency matrix is extended to molecules containing cycles. Spectral moments are expressed as linear combinations of structural fragments of any kind of nonweighted graphs. The boiling points of a series of 80 cycloalkanes was well-described by the present approach. The predictive power of the model was proved by using a test set of another 26 compounds. An equation that expresses the contribution of the different fragments of the molecules to the boiling point was obtained.
Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity
2018
Nonparametric topic models based on hierarchical Dirichlet processes (HDPs) allow for the number of topics to be automatically discovered from the data. The computational complexity of standard Gibbs sampling techniques for model training is linear in the number of topics. Recently, it was reduced to be linear in the number of topics per word using a technique called alias sampling combined with Metropolis Hastings (MH) sampling. We propose a different proposal distribution for the MH step based on the observation that distributions on the upper hierarchy level change slower than the document-specific distributions at the lower level. This reduces the sampling complexity, making it linear i…
A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular Structure.
2011
In the preset report, for the first time, support vector machine (SVM), artificial neural network (ANN), Baye- sian networks (BNs), k-nearest neighbor (k-NN) are applied and compared on two "in-house" datasets to describe the tyrosinase inhibitory activity from the molecular structure. The data set Data I is used for the identification of tyrosi- nase inhibitors (TIs) including 701 active and 728 inactive compounds. Data II consists of active chemicals for potency estimation of TIs. The 2D TOMOCOMD-CARDD atom-based quadratic indices are used as molecular descriptors. The de- rived models show rather encouraging results with the areas under the Receiver Operating Characteristic (AURC) curve …
Modeling anti-allergic natural compounds by molecular topology.
2013
Molecular topology has been applied to the search of QSAR models able to identify the anti-allergic activity of a wide group of heterogeneous compounds. Through the linear discriminant analysis and artificial neural networks, correct classification percentages above 85% for both the training set and the test set have been obtained. After carrying out a virtual screening with a natural product library, about thirty compounds with theoretical anti-allergic activity have been selected. Among them, hesperidin, naringin, salinomycin, sorbitol, curcumol, myricitrin, diosmin and kinetin stand out. Some of these compounds have already been referenced as having anti-allergic activity.
Distinctive amino acid residue periodicities in terminal sequences of type III and type I secreted proteins from proteobacteria
2007
AbstractThe Fourier transform (FT) method was applied to specify the distribution of 14 predefined groups of amino acids (64 residues) at both termini of annotated type III and type I secreted proteins from proteobacteria. Type I proteins displayed a higher occurrence of significant periodicities at both C-and N-termini, indicating potent features to discriminate between secretion types, particularly by the use of variables selected from the full periodicity profiles at 19 orders of FT. The Fishers linear discriminant analysis, together with the stepwise selection of variables throughout equal pairs of combinations for all predefined groups of residues, revealed the C-terminal harmonics of …
Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines
2014
Abstract The main objective of this work has been the authentication by Fourier transform near infrared (FT-NIR) spectrometry of the origin of persimmon fruits cultivated in different regions of Spain. In order to achieve this goal, 166 persimmon samples from 7 different regions of Spain were analyzed by FT-NIR spectrometry. By splitting the spectral data in training and independent test sets, a classification model was built using least squares support vector machines chemometric technique. Orthogonal signal correction and principal component analysis were performed prior to conduct the classification strategy. The verified model was applied for the prediction of the origin of 50 samples f…
Infantile Hemangioma Detection using Deep Learning
2020
Infantile hemangiomas are the most common type of benign tumor which appear in the first weeks of life. As currently there is no robust protocol to monitor and assess the hemangioma status, this study proposes a preliminary method to detect the lesion. Therefore, in this paper we describe a hemangiomas classifier based on a linear convolutional neural network architecture. The challenge was to achieve a good classification using a relatively small internal database of 240 images from 40 different patients. The results are promising as the CNN performance evaluation showed a level of accuracy on the test set of 93.84%. Five metrics were calculated to assess the proposed model performances: a…
Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality
2020
[EN] Objective: Sensory processing is the ability to capture, elaborate, and integrate information through the five senses and is impaired in over 90% of children with autism spectrum disorder (ASD). The ASD population shows hyper¿hypo sensitiveness to sensory stimuli that can generate alteration in information processing, affecting cognitive and social responses to daily life situations. Structured and semi-structured interviews are generally used for ASD assessment, and the evaluation relies on the examiner¿s subjectivity and expertise, which can lead to misleading outcomes. Recently, there has been a growing need for more objective, reliable, and valid diagnostic measures, such as biomar…
Active disturbance rejection control of linear induction motor
2017
This paper proposes the theoretical framework and the experimental application of the active disturbance rejection control to linear induction motors. Such a nonlinear control (ADRC) technique can be viewed as a particular kind of input-output linearization control, where the nonlinear transformation of the state is not computed by means of the model, but it is estimated online. Such an approach permits to cope with unmodelling dynamics, as well as uncertainty in the knowledge of the model parameters and exogenous disturbances. The effectiveness of the proposed ADRC control law has been verified both by numerical simulations and experimentally on a suitably developed test setup. Moreover, t…