Search results for "artificial intelligence"
showing 10 items of 6122 documents
<strong>Predicting Proteasome Inhibition using Atomic Weighted Vector and Machine Learning</strong>
2018
Ubiquitin/Proteasome System (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. Through the concerted actions of a series of enzymes, proteins are marked for proteasomal degradation by being linked to the polypeptide co-factor, ubiquitin. The UPS participates in a wide array of biological functions such as antigen presentation, regulation of gene transcription and the cell cycle, and activation of NF-κB. Some researchers have applied QSAR method and machine learning in the study of proteasome inhibition (EC50(µmol/L)), such as: the analysis of proteasome inhibition prediction, in the prediction of multi-target inhibitors of UPP and in the prediction of p…
QSAR Analysis of Hypoglycemic Agents Using the Topological Indices
2001
The molecular topology model and discriminant analysis have been applied to the prediction of some pharmacological properties of hypoglycemic drugs using multiple regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies performed on the selected prediction models confirmed the goodness of the fits. The method used for hypoglycemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and selection of new hypoglycemic agents, and we …
Exercise, Sports & Cardiovascular Health: Relevant Questions and Answers
2019
Introduction Currently, it is quite common for a clinical cardiologist to be questioned about exercise and sports, topics that are rarely discussed during formal medical education. In this regard, there is a clear need to access high-quality data and evidence-based information to give patients and family members the best advice. Aiming to present the state-of-the art scientific information on the topic, we have invited several experts from different countries, all of them “knowledge-producers” in exercise and sports cardiology, to contribute [...]
Enterprise Knowledge Modeling, UML vs Ontology: Formal Evaluation
2019
International audience; Everyday activities in enterprises rely heavily on the experts' know-how. Due to experts departure, the loss of expertise and knowledge is a reoccurring problem in these enterprises. Recently, in order to capture experts knowledge into intelligent systems, formal knowledge representation methods, such as ontologies, are being studied and have caught up with non-formal or semi-formal representation, such as UML. The similarities and differences between UML class diagram and computational ontology have for long raised questions about the possibility of synthesizing them in a common representation (usually an ontology). Indeed, the problem of migrating knowledge encoded…
DISTATIS: The Analysis of Multiple Distance Matrices
2006
In this paper we present a generalization of classical multidimensional scaling called DISTATIS which is a new method that can be used to compare algorithms when their outputs consist of distance matrices computed on the same set of objects. The method first evaluates the similarity between algorithms using a coefficient called the RV coefficient. From this analysis, a compromise matrix is computed which represents the best aggregate of the original matrices. In order to evaluate the differences between algorithms, the original distance matrices are then projected onto the compromise. We illustrate this method with a "toy example" in which four different "algorithms" (two computer programs …
Detection and Recognition of Target Signals in Radar Clutter via Adaptive CFAR Tests
2006
In this paper, adaptive CFAR tests are described which allow one to classify radar clutter into one of several major categories, including bird, weather, and target classes. These tests do not require the arbitrary selection of priors as in the Bayesian classifier. The decision rule of the recognition techniques is in the form of associating the p-dimensional vector of observations on the object with one of the m specific classes. When there is the possibility that the object does not belong to any of the m classes, then this object is to be classified as belonging to one of the m classes or to class m+1 whose distribution is unspecified. The tests are invariant to intensity changes in the …
Application of learning pallets for real-time scheduling by the use of radial basis function network
2013
The expansion of the scope and scale of products in the current business environments causes a continuous increase in complexity of logistics activities. In order to deal with this challenge in planning and control of logistics activities, several solutions have been introduced. One of the most latest one is the application of autonomy. The paradigm of autonomy in inbound logistics, can be reflected in decisions for real-time scheduling and control of material flows. Integration of autonomous control with material carrier objects can realize the expected advantages of this alternative into shop-floors. Since pallets (bins, fixtures, etc.) are some common used carrier objects in logistics, t…
Joint image and motion reconstruction for PET using a B-spline motion model.
2012
We present a novel joint image and motion reconstruction method for PET. The method is based on gated data and reconstructs an image together with a motion function. The motion function can be used to transform the reconstructed image to any of the input gates. All available events (from all gates) are used in the reconstruction. The presented method uses a B-spline motion model, together with a novel motion regularization procedure that does not need a regularization parameter (which is usually extremely difficult to adjust). Several image and motion grid levels are used in order to reduce the reconstruction time. In a simulation study, the presented method is compared to a recently propos…
Estimation of Personalized Minimal Purkinje Systems From Human Electro-Anatomical Maps
2021
The Purkinje system is a heart structure responsible for transmitting electrical impulses through the ventricles in a fast and coordinated way to trigger mechanical contraction. Estimating a patient-specific compatible Purkinje Network from an electro-anatomical map is a challenging task, that could help to improve models for electrophysiology simulations or provide aid in therapy planning, such as radiofrequency ablation. In this study, we present a methodology to inversely estimate a Purkinje network from a patient's electro-anatomical map. First, we carry out a simulation study to assess the accuracy of the method for different synthetic Purkinje network morphologies and myocardial junct…
Wavelet analysis and neural network classifiers to detect mid-sagittal sections for nuchal translucency measurement
2016
We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagitta…