Biomedical subjects

Weiping Ma

Publications and source records attributed to Weiping Ma.

10 recordsLinked to original sources

PubMed2026

Proteogenomic analysis of pediatric and AYA high-grade glioma reveals age-dependent biology, female-male differences, and kinase targets.

High-grade gliomas (HGGs) in children and adolescents and young adults (AYA) exhibit distinct biology across the neurodevelopmental spectrum. To dissect tumor-intrinsic molecular characteristics independent of developmental variation, we perform comprehensive proteogenomic analyses of tumors from 112 HGG patients aged 0-40 years. Our multi-omics analysis identifies two AYA subgroups-adolescents (aged 15-26 years) and young adults (aged 26-40 years)-with distinct molecular profiles and survival outcomes. Tumor-normal comparisons and survival modeling highlight roles of oxidative phosphorylation and neuronal system biology in glioma progression. Causal network analysis and cell line studies provide a rationale for personalized therapies targeting candidate kinases, such as CDK8. Survival modeling, clustering, and immune-landscape analyses identify proteins, post-translational modifications, and immune signatures linked to outcomes and reveal clinically relevant differences between male and female patients.

adolescent and young adult glioma↗

PubMed2006

Accurate quantitative structure-property relationship model of mobilities of peptides in capillary zone electrophoresis.

The aim of this work was to predict electrophoretic mobilities of peptides in capillary zone electrophoresis (CZE) using the linear heuristic method (HM) and a nonlinear radial basis function neural network (RBFNN). Two data sets, consisting of 125 peptides ranging in size between 2 and 14 amino acids and 58 peptides ranging in size between 2 and 39 amino acids, are researched to test applicability of the QSPR methods. In this study, the root mean squared (RMS) errors of the training set, the test set and the whole set of data set 1 are 1.3766, 1.5608 and 1.4157 and the correlation coefficients (R2) are 0.9740, 0.9671 and 0.9724 predicted by RBFNN, respectively. While the RMS errors of the training set, the test set and the whole set of data set 2 are 0.6279, 0.8145 and 0.6673 and the correlation coefficients (R2) are 0.9773, 0.9489 and 0.9732, respectively. So the Offord charge-over-mass term (Q/M(2/3)) combined with descriptors calculated by CODESSA represents the structural features of the peptides appropriately. The electrophoretic mobilities of peptides can be accurately predicted by the linear and nonlinear model. Furthermore, the results of nonlinear model are closer to the experimental data than those of linear model.

Electrophoresis, Capillary↗

PubMed2006

Quantitative structure-property relationships for pesticides in biopartitioning micellar chromatography.

The retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides was studied by quantitative structure-property relationships (QSPR) method. Heuristic method (HM) and support vector machine (SVM) method were used to build linear and nonlinear models, respectively. Compared the results of these two methods, those obtained by the SVM model are much better. For the test set, a predictive correlation coefficient (R) of 0.9755 and root-mean-square (RMS) error of 0.1403 were obtained. The proposed QSPR models, both by HM and SVM, contain the same descriptors that agree with the classical Abraham parameters of well-known linear solvation energy relationships (LSER).

Chromatography↗

PubMed2006

Quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether (PBDE) congeners.

Levels of Polybrominated diphenyl ether (PBDEs) are increasing in the environment due to their use as flame retardants. The similarities of structure to polychlorinated biphenyl (PCB) congeners suggest that they may share similar toxicological properties, such as hepatic enzyme induction. In this work, quantitative structure-activity relationship (QSAR) models were constructed based on 406 descriptors for the logarithm of toxicology index (aryl hydrocarbon receptor relative binding affinities, AhR, I) of 18 PBDE congeners. The method used for building model is the Heuristic method, which is included in comprehensive descriptors for structural and statistical analysis (CODESSA) software. The best regression model involved four descriptors, which were related to the conformational changes, atomic reactivity, molecular electrostatic field, and non-uniformity of mass distribution in a molecule of PBDEs, etc. The high square of the correlation coefficient R(2)(0.903) showed the model was satisfactory.

Environmental Pollutants↗

PubMed2005

Development of quantitative structure gas chromatographic relative retention time models on seven stationary phases for 209 polybrominated diphenyl ether congeners.

Quantitative structure retention relationships (QSRRs) were developed to predict the gas chromatographic (GC) relative retention times (RRTs) for 209 polybrominated diphenyl ether (PBDE) congeners using the heuristic method included in the computer software Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA). A total of 445 constitutional, topological, geometrical, electrostatic, and semi-empirical quantum chemical descriptors were derived for all PBDEs. Using experimental RRT data for 126 PBDE congeners from the literature, predictive regression models were built for seven individual GC capillary columns differing in stationary phases. Each model includes four descriptors which included Wiener index, Randic index, polarity parameter, etc., selected by CODESSA. High predictability was obtained. High multiple correlation coefficients R(2) indicated that >98.5% (except for stationary phase CP-Sil 19) of the total variation in the predicted RRTs is explained by the fitted models. The models were subsequently used to predict the RRTs of the remaining 83 PBDE congeners on seven different stationary phases. The statistical results show that, compared with others, DB-XLB column not only produces the least number of peak overlaps but also results in shorter retention times.

Chromatography, Gas↗

PubMed2005

Quantitative structure-activity relationship models for prediction of sensory irritants (logRD50) of volatile organic chemicals.

Quantitative classification and regression models for prediction of sensory irritants (logRD50) of volatile organic chemicals (VOCs) have been developed. Each compound was represented by the calculated structural descriptors to encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method (HM) was then used to search the descriptor space and select the descriptors responsible for activity. The best classification results were found using support vector machine (SVM): the accuracy for training, test and overall data set is 96.5%, 85.7% and 94.4%, respectively. The nonlinear regression models were built by radial basis function neural networks (RNFNN) and SVM, respectively. The root mean squared errors (RMS) in prediction for the training, test and overall data set are 0.4755, 0.6322 and 0.5009 for reactive group, 0.2430, 0.4798 and 0.3064 for nonreactive group by RBFNN. The comparative results obtained by SVM are 0.4415, 0.7430 and 0.5140 for reactive group, 0.3920, 0.4520 and 0.4050 for nonreactive group, respectively. This paper proposes an effective method for poisonous chemicals screening and considering.

Irritants↗

PubMed2005

Prediction of pK(a) for neutral and basic drugs based on radial basis function Neural networks and the heuristic method.

PURPOSES: Quantitative structure-property relationships (QSPR) were developed to predict the pK(a) values of a set of neutral and basic drugs via linear and nonlinear methods. The ability of the models to predict pK(a) was assessed and compared. METHODS: The descriptors of 74 neutral and basic drugs in this study were calculated by the software CODESSA, which can calculate constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. Linear and nonlinear QSPR models were developed based on the heuristic method (HM) and radial basis function neural networks (RBFNN), respectively. The heuristic method was also used for the preselection of appropriate molecular descriptors. RESULTS: The obtained linear model had a correlation coefficient of r=0.884, F=37.72 with a root-mean-squared (RMS) error of 0.482 for the training set, and r=0.693, F=11.99, nd RMS=0.987 for the test set. The RMS in predicting the overall data set is 0.619. The nonlinear model gave better results; for the training set, r=0.886, F=202.314, and RMS=0.458, and for the test set r=0.737, F=15.41, and RMS=0.613. The RMS error in prediction for overall data set is 0.493. Prediction results from nonlinear model are in good agreement with experimental values. CONCLUSIONS: In present study, we developed a QSPR model to predict the important parameter (pK(a)) of neutral and basic drugs. The model is useful in predicting pK(a) during the discovery of new drugs when experimental data are unknown.

Neural Networks, Computer↗

PubMed2005

Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte.

A new method support vector machine (SVM) and the heuristic method (HM) were used to develop nonlinear and linear models between the solubility in electrolyte containing sodium chloride and three molecular descriptors of 217 nonelectrolytes. The molecular descriptors representing the structural features of the compounds include two topological and one electrostatic descriptor. The three molecular descriptors selected by HM in CODESSA were used as inputs for SVM. The results obtained by HM and SVM both were satisfactory. The model of HM leads to a correlation coefficient (R) of 0.980 and root-mean-square error (RMS) of 0.219 for the test set. The same descriptors were also employed to build the model in pure water, and the prediction results were consistent with the experimental solubilities. Furthermore, a predictive correlation coefficient R = 0.988 and RMS error of 0.170 for the test set were obtained by SVM. The prediction results are in very good agreement with the experimental values. This paper provides a new and effective method for predicting the solubility in electrolyte and reveals some insight into the structural features that are related to the noneletrolytes.

Journal Article↗

PubMed2005

Genome-wide identification of female-enriched genes in zebrafish.

Characteristic differences in morphology, physiology, and behavior between a male and female are correlated to the differential selection of sex-dependent transcriptomes. By using a cDNA array carrying approximately 9,000 zebrafish unique genes, we identified a group of genes whose expression are enriched in the female fish. A subset of these genes have been confirmed and further grouped as egg-enriched genes, as both somatic- and egg-enriched genes or as somatic-enriched genes by means of RNA gel blot hybridization. Most importantly, a significant proportion of these genes are either functionally unknown or are novel genes. Thus, future studies of this group of genes will help us greatly to understand more about sex-determination and sex-related physiology and behavior. In addition, comparison of zebrafish female-enriched genes with that in Drosophila, we found that only germline genes are shared between vertebrate and invertebrate, suggesting that the process of oogenesis is highly conserved during the evolution.

Animals↗

PubMed2003

15000 unique zebrafish EST clusters and their future use in microarray for profiling gene expression patterns during embryogenesis.

A total of 15590 unique zebrafish EST clusters from two cDNA libraries have been identified. Most significantly, only 22% (3437) of the 15590 unique clusters matched 2805 (of 15200) clusters in the Danio rerio UniGene database, indicating that our EST set is complementary to the existing ESTs in the public database and will be invaluable in assisting the annotation of genes based on the upcoming zebrafish genome sequence. Blast search showed that 7824 of our unique clusters matched 6710 known or predicted proteins in the nonredundant database. A cDNA microarray representing approximately 3100 unique zebrafish cDNA clusters has been generated and used to profile the gene expression patterns across six different embryonic stages (cleavage, blastula, gastrula, segmentation, pharyngula, and hatching). Analysis of expression data using K-means clustering revealed that genes coding for muscle-specific proteins displayed similar expression patterns, confirming that the coordinate gene expression is important for myogenesis. Our results demonstrate that the combination of microarray technology with the zebrafish model system can provide useful information on how genes are coordinated in a genetic network to control zebrafish embryogenesis and can help to identify novel genes that are important for organogenesis.

Animals↗