Bayesian active learning for drug combinations

IEEE Transactions on Biomedical Engineering

Volumn 60, Issue 11, 2013, Pages 3248-3255

  Park, Mijung ^a   Nassar, Marcel ^a   Vikalo, Haris ^a  

^a The University of Texas at Austin   (United States)

Author keywords

Drug combinations; expected improvement; Gaussian processes (GPs); hybrid Monte Carlo (HMC); particle filter

Indexed keywords

DRUG COMBINATIONS; EXPECTED IMPROVEMENTS; GAUSSIAN PROCESSES; HYBRID MONTE CARLO; PARTICLE FILTER;

BIOLOGICAL SYSTEMS; CELL DEATH; COMPLEX NETWORKS; COMPUTER VIRUSES; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); MONTE CARLO METHODS; VIRUSES;

DRUG DOSAGE;

ANTIVIRUS AGENT;

APOPTOSIS; ARTICLE; BAYESIAN LEARNING; COMPUTER SIMULATION; DOSE CALCULATION; DROSOPHILA; DRUG COMBINATION; DRUG MIXTURE; DRUG RESPONSE; DRUG SENSITIVITY; FACTORIAL DESIGN; GENETIC ALGORITHM; HERPES SIMPLEX VIRUS 1; MONTE CARLO METHOD; NONHUMAN; NORMAL DISTRIBUTION; PROBABILITY; RANDOM SAMPLE; STATISTICAL ANALYSIS;

ALGORITHMS; ANIMALS; ANTIVIRAL AGENTS; APOPTOSIS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; DROSOPHILA; DRUG COMBINATIONS; HERPESVIRUS 1, HUMAN; HUMANS; MODELS, THEORETICAL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; PHARMACOLOGY;

EID: 84886714080     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2013.2272322     Document Type: Article

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