| D010963 |
Plasmodium falciparum |
A species of protozoa that is the causal agent of falciparum malaria (MALARIA, FALCIPARUM). It is most prevalent in the tropics and subtropics. |
Plasmodium falciparums,falciparums, Plasmodium |
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| D000962 |
Antimalarials |
Agents used in the treatment of malaria. They are usually classified on the basis of their action against plasmodia at different stages in their life cycle in the human. (From AMA, Drug Evaluations Annual, 1992, p1585) |
Anti-Malarial,Antimalarial,Antimalarial Agent,Antimalarial Drug,Anti-Malarials,Antimalarial Agents,Antimalarial Drugs,Agent, Antimalarial,Agents, Antimalarial,Anti Malarial,Anti Malarials,Drug, Antimalarial,Drugs, Antimalarial |
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| D016571 |
Neural Networks, Computer |
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. |
Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron |
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| D060388 |
Support Vector Machine |
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples. |
Support Vector Network,Machine, Support Vector,Machines, Support Vector,Network, Support Vector,Networks, Support Vector,Support Vector Machines,Support Vector Networks,Vector Machine, Support,Vector Machines, Support,Vector Network, Support,Vector Networks, Support |
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| D021281 |
Quantitative Structure-Activity Relationship |
A quantitative prediction of the biological, ecotoxicological or pharmaceutical activity of a molecule. It is based upon structure and activity information gathered from a series of similar compounds. |
Structure Activity Relationship, Quantitative,3D-QSAR,QSAR,QSPR Modeling,Quantitative Structure Property Relationship,3D QSAR,3D-QSARs,Modeling, QSPR,Quantitative Structure Activity Relationship,Quantitative Structure-Activity Relationships,Relationship, Quantitative Structure-Activity,Relationships, Quantitative Structure-Activity,Structure-Activity Relationship, Quantitative,Structure-Activity Relationships, Quantitative |
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