Biomedical subjects

Xiaogang Ruan

Publications and source records attributed to Xiaogang Ruan.

3 recordsLinked to original sources

PubMed2006

Tumor-specific gene expression patterns with gene expression profiles.

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.

Algorithms↗

PubMed2006

[Research on EEG classification with evolving cascade neural networks].

To correctly classify EEG with different mental tasks, a new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described to avoid over-fitting of a neural network due to noise and redundant features. The learning algorithm calculates the value of a fitness function on validate set and accordingly updates the connection weights on training set. The learning algorithm uses the regularity criterion for selecting the neurons with relevant connection. If the value Cr calculated for the rth neuron is less than the value Cr-1 calculated for the previous (r-1) neuron, the features that feed the rth neuron are relevant, else they are irrelevant. An ECNN starts to learn with one input node and then, adding new inputs as well as new hidden neurons, evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm is applied to classify EEG with two mental tasks. The trained ECNN has correctly classified 83.1% of the testing segments. It shows a better result, compared with a standard BP network.

Algorithms↗

PubMed2003

[A logistic cellular automaton for simulating tumor growth].

This paper focuses on a differential equation logistic model simulating tumor growth. We design a kind of tumor dynamic growth model with one-dimensional cellular automata. A discrete logistic model is developed from the continuous logistic model. Based on others' work, we design discrete mathematical growth dynamic model with cellular automaton. In terms of discrete model, we design stochastic evolving rules of cellular automaton. And this paper simulates the tumor growth dynamic model with cellular automata. The theoretic analysis and results of cellular automaton model are in agreement with data from the ideal differential equation logistic growth of cancer.

Algorithms↗