Biomedical subjects

Xudong Xie

Publications and source records attributed to Xudong Xie.

3 recordsLinked to original sources

PubMed2006

PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm.

MOTIVATION: Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, that is, local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a high-dimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most 'informative' or 'discriminating' features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false positive. RESULTS: PromoterExplorer is tested based on large-scale DNA sequences from different databases, including the EPD, DBTSS, GenBank and human chromosome 22. Experimental results show that consistent and promising performance can be achieved.

Algorithms↗

PubMed2006

Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image.

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed features have a higher discriminating power, and the relative importance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conventional kernel PCA to be called "doubly" nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial models. Experiments show that consistent and promising results are obtained.

Algorithms↗

PubMed2005

Age- and gender-related difference of ACE2 expression in rat lung.

Epidemiologic data suggested that there was an obvious predominance of young adult patients with a slight female proneness in severe acute respiratory syndrome (SARS). The angiotensin-converting enzyme 2 (ACE2) was very recently identified as a functional receptor for SARS virus and is therefore a prime target for pathogenesis and pharmacological intervention. Rats of both genders at three distinct ages (young-adult, 3 months; middle-aged, 12 months; old, 24 months) were evaluated to determine the characteristic of ACE2 expression in lung and the effect of aging and gender on its expression. ACE2 was predominantly expressed in alveolar epithelium, bronchiolar epithelium, endothelium and smooth muscle cells of pulmonary vessels with similar content, whereas no obvious signal was detected in the bronchiolar smooth muscle cells. ACE2 expression is dramatically reduced with aging in both genders: young-adult vs. old P < 0.001 (by 78% in male and 67% in female, respectively) and middle-aged vs. old P < 0.001 (by 71% in male rats and 59% in female rats, respectively). The decrease of ACE2 content was relatively slight between young-adult and middle-aged groups (by 25% in male and 18% in female, respectively). Although there was no gender-related difference of ACE2 in young-adult and middle-aged groups, a significantly higher ACE2 content was detected in old female rats than male. In conclusion, the more elevated ACE2 in young adults as compared to aged groups may contribute to the predominance in SARS attacks in this age group.

Aging↗