Biomedical subjects

Qi Shen

Publications and source records attributed to Qi Shen.

42 records · Page 3Linked to original sources

PubMed2002

Variable selection by an evolution algorithm using modified Cp based on MLR and PLS modeling: QSAR studies of carcinogenicity of aromatic amines.

The variable selection in QSAR studies by MLR and PLS modeling has been performed using the evolution algorithm (EA). The Cp statistic has been modified and used as the objective function in the EA search for different combinations of molecular descriptors. For MLR modeling a few information-rich descriptors are selected for model formulation. In PLS modeling, the proposed procedure selects a relatively large number of information-containing descriptors, and a PLS model is formulated based on a few latent variables, which are linear combinations of the selected descriptors. The proposed procedures were used for the prediction of carcinogenicity of aromatic amines.

Algorithms↗

PubMed1999

Expression of Inducible Nitric Oxide Synthase Gene in Neuronal Cells Mediated by Retrovirus Vector.

The role of an inducible nitric oxide synthase (iNOS)expression in the mechanisms of opioid tolerance and dependence was investigated. A recombinant retroviral expression vector containing a cDNA fragment of iNOS was transfected into the neuroblastomaxglioma NG108-15 cells by lipofectamine gene transferring technique. G418-resistant clones were selected and were named NG-LNCXiNOS cells. Using Southern blot, PCR amplification for Neo gene, RT-PCR and Western blot analysis, NG-LNCXiNOS cells were confirmed to have an integral exogenous iNOS gene which was being transcribed and translated into protein. NADPH-diaphorase (NADPH-d) histochemical staining and immunohistochemical staining with iNOS-specific antibody demonstrated that high-level expression of iNOS protein was present in the cytoplasm of NG-LNCXiNOS cells. The catalytic activity and NO( )(2) content in supernatant medium were obviously enhanced in iNOS gene-transfected cells. The results show that the biochemical and pharmacological properties of the recombinant enzyme were similar to those of native enzyme. The recombinant enzyme activity was completely dependent on NADPH and failed to be stimulated by the addition of calcium and calmodulin. Chelating agents failed to decrease its activity. NOS inhibitors could markedly reduce NO( )(2) production at a concentration-dependent manner. The expression of iNOS gene was involved in the up-regulation of NO-cGMP signal transduction cascade. Therefore, an iNOS gene-modified neuronal cell line was successfully established, offering an excellent model system for seeking and screening new drugs to treat opioid tolerance and dependence.

Journal Article↗

PubMedDate not supplied

Optimized partition of minimum spanning tree for piecewise modeling by particle swarm algorithm. QSAR studies of antagonism of angiotensin II antagonists.

In quantitative structure-activity relationship (QSAR) modeling, when compounds in a training set exhibit a significant structural distinction between each other, in particular when chemicals of biological interest interacting on the receptor involve a different mechanism, it might be difficult to construct a single linear model for the whole population of compounds of interest with desired residuals. Developing a piecewise linear local model can be effective to circumvent the aforementioned problem. In this paper, piecewise modeling by the particle swarm optimization (PMPSO) approach is applied to QSAR study. The minimum spanning tree is used for clustering all compounds in the training set to form a tree, and the modified discrete PSO is applied to divide the tree to find satisfactory piecewise linear models. A new objective function is formulated for searching the appropriate piecewise linear models. The proposed PMPSO algorithm was used to predict the antagonism of angiotensin II. The results demonstrated that PMPSO is useful for improvement of the performance of regression models.

Algorithms↗

PubMedDate not supplied

Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors.

A new version of an ant colony optimization (ACO) algorithm has been proposed. A modified ACO algorithm is proposed to select variables in QSAR modeling and to predict inhibiting action of some diarylimidazole derivatives on cyclooxygenase (COX) enzyme. As a comparison to this method, the evolution algorithm (EA) was also tested. Experimental results have demonstrated that the modified ACO is a useful tool for variable selection that needs few parameters to be adjusted and converges quickly toward the optimal position.

Algorithms↗

PubMedDate not supplied

Piecewise hypersphere modeling by particle swarm optimization in QSAR studies of bioactivities of chemical compounds.

As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.

Angiotensin II↗

PubMedDate not supplied

Optimized block-wise variable combination by particle swarm optimization for partial least squares modeling in quantitative structure-activity relationship studies.

The use of numerous descriptors that are indicative of molecular structure is becoming common in quantitative structure-activity relationship (QSAR) studies. As all of the descriptors might carry more or less molecular information, it seems more advisable to investigate the possible variable combination rather than variable selection. In this paper, an optimized block-wise variable combination (OBVC) by particle swarm optimization based on partial least squares modeling has been proposed for variable combination. An F statistic is also introduced to determine the dimensionality of the PLS model. The performance is assessed using two QSAR data sets. Experimental results have shown the good performance of this technique compared to those obtained by stepwise regression.

Amines↗