Biomedical subjects

Wei-Qi Lin

Publications and source records attributed to Wei-Qi Lin.

7 recordsLinked to original sources

PubMed2006

Boosting support vector regression in QSAR studies of bioactivities of chemical compounds.

In this paper, boosting has been coupled with SVR to develop a new method, boosting support vector regression (BSVR). BSVR is implemented by firstly constructing a series of SVR models on the various weighted versions of the original training set and then combining the predictions from the constructed SVR models to obtain integrative results by weighted median. The proposed BSVR algorithm has been used to predict toxicities of nitrobenzenes and inhibitory potency of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. As comparisons to this method, the multiple linear regression (MLR) and conventional support vector regression (SVR) have also been investigated. Experimental results have shown that the introduction of boosting drastically enhances the generalization performance of individual SVR model and BSVR is a well-performing technique in QSAR studies superior to multiple linear regression.

Algorithms↗

PubMed2005

QSAR analysis of substituted bis[(acridine-4-carboxamide)propyl]methylamines using optimized block-wise variable combination by particle swarm optimization for partial least squares modeling.

In the current work, we employed optimized block-wise variable combination (OBVC) by particle swarm optimization (PSO) based on partial least squares (PLS) modeling for variable combination and compared it to the traditional methods. It has been demonstrated that the modified PSO is a useful tool for searching optimized variable combination. Quantitative structure-activity relationship (QSAR) model has been formulated for a set of DNA binding topoisomerase (topo) (substituted bis[(acridine-4-carboxamide)propyl]methylamines) on murine Lewis lung carcinoma (LL(c)) cells. The spatial descriptors especially Jurs descriptors play important roles in predicting the compound's inhibitory activity to murine LL(c) cells, and polar interactions are the principal binding strength between compounds and murine LL(c) cells. In addition, rotatable bonds in molecules and molar refractivity of the compounds will markedly affect the compounds' inhibitory activity.

Acridines↗

PubMed2004

Hybridized particle swarm algorithm for adaptive structure training of multilayer feed-forward neural network: QSAR studies of bioactivity of organic compounds.

The multilayer feed-forward ANN is an important modeling technique used in QSAR studying. The training of ANN is usually carried out only to optimize the weights of the neural network and without paying attention to the network topology. Some other strategies used to train ANN are, first, to discover an optimum structure of the network, and then to find weights for an already defined structure. These methods tend to converge to local optima, and may also lead to overfitting. In this article, a hybridized particle swarm optimization (PSO) approach was applied to the neural network structure training (HPSONN). The continuous version of PSO was used for the weight training of ANN, and the modified discrete PSO was applied to find appropriate the network architecture. The network structure and connectivity are trained simultaneously. The two versions of PSO can jointly search the global optimal ANN architecture and weights. A new objective function is formulated to determine the appropriate network architecture and optimum value of the weights. The proposed HPSONN algorithm was used to predict carcinogenic potency of aromatic amines and biological activity of a series of distamycin and distamycin-like derivatives. The results were compared to those obtained by PSO and GA training in which the network architecture was kept fixed. The comparison demonstrated that the HPSONN is a useful tool for training ANN, which converges quickly towards the optimal position, and can avoid overfitting in some extent.

Journal Article↗

PubMed2002

N-allyl-4-(N-2'-hydroxyethyl)amino-1,8-naphthalimide as a fluorophore for optical chemosensing of nitrofurantoin.

N-Allyl-4-(N-2'-hydroxyethyl)amino-1,8-naphthalimide (AHEAN), a naphthalimide derivative, was synthesized as a new fluorophore for optical chemical sensor preparation. To prevent leakage of the fluorophore, AHEAN was photo-copolymerized with 2-hydroxypropyl methacrylate on a glass surface treated with a silanizing agent. An optical chemical sensor based on AHEAN can be utilized for nitrofurantoin assay based on fluorescence quenching. The sensor shows sufficient repeatability, selectivity and a fast response of less than 30 s. Nitrofurantoin can be determined in the range between 1.00 x 10(-6) and 1.00 x 10(-3) mol l(-1) with a detection limit of 4.8 x 10(-7) mol l(-1). Most commonly co-existing drug substances and ions do not interfere with the nitrofurantoin assay. The sensor was applied to the analysis of pharmaceutical and urine samples.

Allyl Compounds↗

PubMed2001

Covalently immobilized aminonaphthalimide as fluorescent carrier for the preparation of optical sensors.

By replacing the hydrogen of the 4-amino group of a 4-amino-1,8-naphthalimide derivative with an N-acryloxyethyl group, the fluorophore has been covalently immobilized on an optical sensor surface by UV photopolymerization. The optical sensor obtained can be used for the determination of picric acid. The linear range and detection limit of the sensor are 9.80x10(-7)-1.96x10(-4) mol L(-1) and 7.1x10(-7) mol L(-1), respectively. Leaching of the fluorophore from the membrane is effectively prevented by covalent immobilization, resulting in a sensor with a relatively long lifetime. The response time of the sensor is short, and the reproducibility and reversibility are good. The sensor has been used for the indirect determination of the chloroquine content of pharmaceutical tablets.

Journal Article↗

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↗