Biomedical subjects

Shih-Hau Chiu

Publications and source records attributed to Shih-Hau Chiu.

3 recordsLinked to original sources

PubMed2006

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Algorithms↗

PubMed2005

Systemic infection of Kudoa lutjanus n. sp. (Myxozoa: Myxosporea) in red snapper Lutjanus erythropterus from Taiwan.

A new species of Kudoa lutjanus n. sp. (Myxosporea) is described from the brain and internal organs of cultured red snapper Lutjanus erythropterus from Taiwan. The fish, 260 to 390 g in weight, exhibited anorexia and poor appetite and swam in the surface water during outbreaks. Cumulative mortality was about 1% during a period of 3 wk. The red snapper exhibited numerous creamy-white pseudocysts, 0.003 to 0.65 cm (n = 100) in diameter, in the eye, swim bladder, muscle and other internal organs, but especially in the brain. The number of pseudocysts per infected fish was not correlated with fish size or condition. Mature spores were quadrate in apical view and suboval in side view, measuring 8.2 +/- 0.59 microm in width and 7.3 +/- 0.53 microm in length. The 4 valves were equal in size, each with 1 polar capsule. Polar capsules were pyriform in shape, measuring 3.62 +/- 0.49 microm in length and 2.2 +/- 0.49 microm in width. Mild inflammatory responses or liquefaction of host tissue were associated with K. lutjanus n. sp. infection. The junction of shell valves appeared as overlapping, straight lines. The polar filament formed 2 to 3 coils. A general PCR (polymerase chain reaction) primer for Kudoa amplified the small subunit (SSU) rDNA sequences, and the amplified gene was sequenced. It was evident from the phylogenetic tree that the 3 strains tested, AOD93020M, AOD93028M and AOD93028B, were identical and belonged to the Kudoa SS rRNA subgroup. The evolutionary tree showed that these strains form a unique clade, at a distance from other Kudoa species and myxosporeans. The spore's morphological and ultrastructural characteristics, as well as the SS rDNA properties of the isolates, were also essentially identical and served to distinguish them from representative Kudoa. It is, therefore, proposed that the strains isolated from the diseased red snapper be assigned to a new species.

Animals↗

PubMedDate not supplied

Supervised feature ranking using a genetic algorithm optimized artificial neural network.

A genetic algorithm optimized artificial neural network GNW has been designed to rank features for two diversified multivariate data sets. The dimensions of these data sets are 85x24 and 62x25 for 24 or 25 molecular descriptors being computed for 85 matrix metalloproteinase-1 inhibitors or 62 hepatitis C virus NS3 protease inhibitors, respectively. Each molecular descriptor computed is treated as a feature and input into an input layer node of the artificial neural network. To optimize the artificial neural network by the genetic algorithm, each interconnected weight between input and hidden or between hidden and output layer nodes is binary encoded as a 16 bits string in a chromosome, and the chromosome is evolved by crossover and mutation operations. Each input layer node and its associated weights of the trained GNW are systematically omitted once (the self-depleted weights), and the corresponding weight adjustments due to the omission are computed to keep the overall network behavior unchanged. The primary feature ranking index defined as the sum of self-depleted weights and the corresponding weight adjustments computed is found capable of separating good from bad features for some artificial data sets of known feature rankings tested. The final feature indexes used to rank the data sets are computed as a sum of the weighted frequency of each feature being ranked in a particular rank for each data set being partitioned into numerous clusters. The two data sets are also clustered by a standard K-means method and trained by a support vector machine (SVM) for feature ranking using the computed F-scores as feature ranking index. It is found that GNW outperforms the SVM method on three artificial as well as the matrix metalloproteinase-1 inhibitor data sets studied. A clear-cut separation of good from bad features is offered by the GNW but not by the SVM method for a feature pool of known feature ranking.

Algorithms↗