Tony Shu Kam Mok

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This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of(More)
In this paper, we introduce a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population-based genetic algorithm (AEGA) to solve it. Working closely with an oncologist, we first modify the existing model, because its equation for the cumulative drug toxicity is inconsistent with medical knowledge and(More)
The toxicity of an anticancer drug is cleared from the body by different processes, including saturable metabolic and nonsaturable renal-excretion pathways. According to the principles of toxicokinetics, we propose a new anticancer drug scheduling model with different toxic elimination processes in this paper. We also present a sophisticated automating drug(More)
Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this(More)
This paper presents a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population based genetic algorithm (AEGA) to solve it. Working closely with an on-cologist, we firstly modify the existing model, because the existing equation of the cumulative drug toxicity is not consistent with the clinical(More)
This paper proposes a new memetic algorithm (MA) to solve the Multi-drug chemotherapy optimization problem. The new MA combines GA with a local search algorithm called Iterative Dynamic Programming (IDP). A multi-drug chemotherapy model is introduced to simulate the possible response of the tumor cells under drugs administration. Optimization of the(More)
In this paper, we propose a novel fast evolutionary algorithm — cycle-wise genetic algorithm (CWGA) based on the theoretical analyses of a drug scheduling mathematical model for cancer chemotherapy. CWGA is more efficient than other existing algorithms to solve the drug scheduling optimization problem. Moreover, its simulation results match well with the(More)
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