Biomedical subjects

Lipo Wang

Publications and source records attributed to Lipo Wang.

6 recordsLinked to original sources

PubMed2006

An efficient semi-unsupervised gene selection method via spectral biclustering.

Gene selection is an important issue in microarray data processing. In this paper, we propose an efficient method for selecting relevant genes. First, we use spectral biclustering to obtain the best two eigenvectors for class partition. Then gene combinations are selected based on the similarity between the genes and the best eigenvectors. We demonstrate our semi-unsupervised gene selection method using two microarray cancer data sets, i.e., the lymphoma and the liver cancer data sets, where our method is able to identify a single gene or a two-gene combinations which can lead to predictions with very high accuracy.

Artificial Intelligence↗

PubMed2006

A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks.

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.

Electricity↗

PubMed2005

Applications of support vector machines to cancer classification with microarray data.

Microarray gene expression data usually have a large number of dimensions, e.g., over ten thousand genes, and a small number of samples, e.g., a few tens of patients. In this paper, we use the support vector machine (SVM) for cancer classification with microarray data. Dimensionality reduction methods, such as principal components analysis (PCA), class-separability measure, Fisher ratio, and t-test, are used for gene selection. A voting scheme is then employed to do multi-group classification by k(k - 1) binary SVMs. We are able to obtain the same classification accuracy but with much fewer features compared to other published results.

Algorithms↗

PubMed2005

Pattern recognition methods for protein functional site prediction.

Protein functional site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the functional sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein functional site prediction and outline the future research directions in this important area.

Algorithms↗

PubMed2004

A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.

Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.

Algorithms↗

PubMedDate not supplied

Broadcast scheduling in wireless multihop networks using a neural-network-based hybrid algorithm.

In wireless multihop networks, the objective of the broadcast scheduling problem is to find a conflict free transmission schedule for each node at different time slots in a fixed length time cycle, called TDMA cycle. The optimization criterion is to find an optimal TDMA schedule with minimal TDMA cycle length and maximal node transmissions. In this paper we propose a two-stage hybrid method to solve this broadcast scheduling problem in wireless multihop networks. In the first stage, we use a sequential vertex-coloring algorithm to obtain a minimal TDMA frame length. In the second stage, we apply the noisy chaotic neural network to find the maximum node transmission based on the results obtained in the previous stage. Simulation results show that this hybrid method outperforms previous approaches, such as mean field annealing, a hybrid of the Hopfield neural network and genetic algorithms, the sequential vertex coloring algorithm, and the gradual neural network.

Algorithms↗