In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity mea...In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.展开更多
Traditional Chinese medicine （TCM） relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of...Traditional Chinese medicine （TCM） relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature se- lection algorithm known as a co-evolving memetic wrapper （COW） is proposed in this paper. COW takes advantage of recent research in genetic algorithms （GAs） and memetic al- gorithms （MAs） by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.展开更多
文摘In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.
文摘Traditional Chinese medicine （TCM） relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature se- lection algorithm known as a co-evolving memetic wrapper （COW） is proposed in this paper. COW takes advantage of recent research in genetic algorithms （GAs） and memetic al- gorithms （MAs） by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.
