Kin Hong Lee

A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization

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 constraints. We combine the adaptive elitist genetic algorithm with a local search algorithm called iterative dynamic programming (IDP) to form a new memetic algorithm (MA-IDP) for solving the problem. MA-IDP has been shown to be very efficient in solving the multidrug scheduling optimization problem

Genotype-specific genomic markers associated with primary hepatomas, based on complete genomic sequencing of hepatitis B virus.

ABSTRACT We aimed to identify genomic markers in hepatitis B virus (HBV) that are associated with hepatocellular carcinoma (HCC) development by comparing the complete genomic sequences of HBVs among patients with HCC and those without. One hundred patients with HBV-related HCC and 100 age-matched HBV-infected non-HCC patients (controls) were studied. HBV DNA from serum was directly sequenced to study the whole viral genome. Data mining and rule learning were employed to develop diagnostic algorithms. An independent cohort of 132 cases (43 HCC and 89 non-HCC) was used to validate the accuracy of these algorithms. Among the 100 cases of HCC, 37 had genotype B (all subgenotype Ba) and 63 had genotype C (16 subgenotype Ce and 47 subgenotype Cs) HBV infection. In the control group, 51 had genotype B and 49 had genotype C (10 subgenotype Ce and 39 subgenotype Cs) HBV infection. Genomic algorithms associated with HCC were derived based on genotype/subgenotype-specific mutations. In genotype B HBV, mutations C1165T, A1762T and G1764A, T2712C/A/G, and A/T2525C were associated with HCC. HCC-related mutations T31C, T53C, and A1499G were associated with HBV subgenotype Ce, and mutations G1613A, G1899A, T2170C/G, and T2441C were associated with HBV subgenotype Cs. Amino acid changes caused by these mutations were found in the X, envelope, and precore/core regions in association with HBV genotype B, Ce, and Cs, respectively. In conclusion, infections with different genotypes of HBV (B, Ce, and Cs) carry different genomic markers for HCC at different parts of the HBV genome. Different HBV genotypes may have different virologic mechanisms of hepatocarcinogenesis.

Genetic parallel programming: design and implementation

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential programif required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.

Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits

Experimental results show that parallel programs can be evolved more easily than sequential programs in genetic parallel programming (GPP). GPP is a novel genetic programming paradigm which evolves parallel program solutions. With the rapid development of lookup-table-based (LUT-based) field programmable gate arrays (FPGAs), traditional circuit design and optimization techniques cannot fully exploit the LUTs in LUT-based FPGAs. Based on the GPP paradigm, we have developed a combinational logic circuit learning system, called GPP logic circuit synthesizer (GPPLCS), in which a multilogic-unit processor is used to evaluate LUT circuits. To show the effectiveness of the GPPLCS, we have performed a series of experiments to evolve combinational logic circuits with two- and four-input LUTs. In this paper, we present eleven multi-output Boolean problems and their evolved circuits. The results show that the GPPLCS can evolve more compact four-input LUT circuits than the well-known LUT-based FPGA synthesis algorithms.

Parallel programs are more evolvable than sequential programs

This paper presents a novel phenomenon of the Genetic Parallel Programming (GPP) paradigm - the GPP accelerating phenomenon. GPP is a novel Linear Genetic Programming representation for evolving parallel programs running on a Multi-ALU Processor (MAP). We carried out a series of experiments on GPP with different number of ALUs. We observed that parallel programs are more evolvable than sequential programs. For example, in the Fibonacci sequence regression experiment, evolving a 1-ALU sequential program requires 51 times on average of the computational effort of an 8-ALU parallel program. This paper presents three benchmark problems to show that the GPP can accelerate evolution of parallel programs. Due to the accelerating evolution phenomenon of GPP over sequential program evolution, we could increase the normal GPs evolution efficiency by evolving a parallel program by GPP and if there is a need, the evolved parallel program can be translated into a sequential program so that it can run on conventional hardware.

Evolving parallel machine programs for a multi-ALU processor

This paper proposes a novel genetic parallel programming (GPP) paradigm for evolving optimal parallel programs running on a multi-ALU processor by linear genetic programming. GPP uses a two-phase evolution approach. It evolves completely correct solution programs in the first phase. Then it optimizes execution speeds of solution programs in the second phase. Besides, GPP also employs a new genetic operation that swaps sub-instructions of a solution program. Three experiments (Sextic, Fibonacci and Factorial) are given as examples to show that GPP could discover novel parallel programs that fully utilize the processors parallelism.

A Runtime Reconfigurable Implementation of the GSAT Algorithm

Boolean satisfiability (SAT) problems are an important subset of constraint satisfaction problems (CSPs) which have application in such areas as computer aided design, computer vision, planning, resource allocation and temporal reasoning. In this paper we describe an implementation of an incomplete heuristic search algorithm called GSAT to solve 3-SAT problems. In contrast to other approaches, our design is runtime configurable. The input to this system is a 3-SAT problem from which a software program directly generates a problem-specific configuration which can be directly downloaded to a Xilinx XC6216, avoiding the need for resynthesis, placement and routing for different constraints. We envisage that such systems could be used in hardware based real time constraint solving systems.

Text segmentation for chinese spell checking

Chinese spell checking is different from its counterparts for Western languages because Chinese words in texts are not separated by spaces. Chinese spell checking in this article refers to how to identify the misuse of characters in text composition. In other words, it is error correction at the word level rather than at the character level. Before Chinese sentences are spell checked, the text is segmented into semantic units. Error detection can then be carried out on the segmented text based on thesaurus and grammar rules. Segmentation is not a trivial process due to ambiguities in the Chinese language and errors in texts. Because it is not practical to define all Chinese words in a dictionary, words not predefined must also be dealt with. The number of word combinations increases exponentially with the length of the sentence. In this article, a Block-of-Combinations (BOC) segmentation method based on frequency of word usage is proposed to reduce the word combinations from exponential growth to linear growth. From experiments carried out on Hong Kong newspapers, BOC can correctly solve 10% more ambiguities than the Maximum Match segmentation method. To make the segmentation more suitable for spell checking, user interaction is also suggested.

High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network

BackgroundInferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes.ResultsWe have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain.ConclusionsWe have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed.

Data classification using genetic parallel programming

A novel Linear Genetic Programming (LGP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. It is found that GPP can evolve parallel programs for Data Classification problems. In this paper, five binary-class UCI Machine Learning Repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.