Biomedical subjects

Z R Li

Publications and source records attributed to Z R Li.

At least 19 recordsLinked to original sources

PubMed2007

MODEL-molecular descriptor lab: a web-based server for computing structural and physicochemical features of compounds.

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Amino Acids↗

PubMed2006

PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence.

Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein-protein interactions and MHC-binding peptides. PROFEAT is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/prof/prof.cgi.

Algorithms↗

PubMed2006

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods.

Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.

Forecasting↗

PubMed2006

Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods.

Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.

Pharmacokinetics↗

PubMed2006

Application of support vector machines to in silico prediction of cytochrome p450 enzyme substrates and inhibitors.

Cytochrome P450 enzymes are responsible for phase I metabolism of the majority of drugs and xenobiotics. Identification of the substrates and inhibitors of these enzymes is important for the analysis of drug metabolism, prediction of drug-drug interactions and drug toxicity, and the design of drugs that modulate cytochrome P450 mediated metabolism. The substrates and inhibitors of these enzymes are structurally diverse. It is thus desirable to explore methods capable of predicting compounds of diverse structures without over-fitting. Support vector machine is an attractive method with these qualities, which has been employed for predicting the substrates and inhibitors of several cytochrome P450 isoenzymes as well as compounds of various other pharmacodynamic, pharmacokinetic, and toxicological properties. This article introduces the methodology, evaluates the performance, and discusses the underlying difficulties and future prospects of the application of support vector machines to in silico prediction of cytochrome P450 substrates and inhibitors.

Animals↗

PubMed2005

Relationships between the folding rate constant and the topological parameters of small two-state proteins based on general random walk model.

In this paper, we propose an analytically tractable model of protein folding based on one-dimensional general random walk. A second-order differential equation for the mean folding time of a single protein is constructed which can be used to derive the observed relationship between the folding rate constant and the number of native contacts. The parameters appearing in the model can be determined by fitting the theoretical prediction to the experimental result. In addition, taking into account the fact that the number of native contacts is almost proportional to the relative contact order, we can also explain the observed relationship between the folding rate constant and the relative contact order.

Animals↗

PubMed2005

Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods.

Quantitative structure-pharmacokinetic relationships (QSPkR) have increasingly been used for the prediction of the pharmacokinetic properties of drug leads. Several QSPkR models have been developed to predict the total clearance (CL(tot)) of a compound. These models give good prediction accuracy but they are primarily based on a limited number of related compounds which are significantly lesser in number and diversity than the 503 compounds with known CL(tot) described in the literature. It is desirable to examine whether these and other statistical learning methods can be used for predicting the CL(tot) of a more diverse set of compounds. In this work, three statistical learning methods, general regression neural network (GRNN), support vector regression (SVR) and k-nearest neighbour (KNN) were explored for modeling the CL(tot) of all of the 503 known compounds. Six different sets of molecular descriptors, DS-MIXED, DS-3DMoRSE, DS-ATS, DS-GETAWAY, DS-RDF and DS-WHIM, were evaluated for their usefulness in the prediction of CL(tot). GRNN-, SVR- and KNN-developed models have average-fold errors in the range of 1.63 to 1.96, 1.66-1.95 and 1.90-2.23, respectively. For the best GRNN-, SVR- and KNN-developed models, the percentage of compounds with predicted CL(tot) within two-fold error of actual values are in the range of 61.9-74.3% and are comparable or slightly better than those of earlier studies. QSPkR models developed by using DS-MIXED, which is a collection of constitutional, geometrical, topological and electrotopological descriptors, generally give better prediction accuracies than those developed by using other descriptor sets. These results suggest that GRNN, SVR, and their consensus model are potentially useful for predicting QSPkR properties of drug leads.

Adult↗

PubMed2005

Assessment of approximate string matching in a biomedical text retrieval problem.

Text-based search is widely used for biomedical data mining and knowledge discovery. Character errors in literatures affect the accuracy of data mining. Methods for solving this problem are being explored. This work tests the usefulness of the Smith-Waterman algorithm with affine gap penalty as a method for biomedical literature retrieval. Names of medicinal herbs collected from herbal medicine literatures are matched with those from medicinal chemistry literatures by using this algorithm at different string identity levels (80-100%). The optimum performance is at string identity of 88%, at which the recall and precision are 96.9% and 97.3%, respectively. Our study suggests that the Smith-Waterman algorithm is useful for improving the success rate of biomedical text retrieval.

Algorithms↗

PubMed2005

Prediction of genotoxicity of chemical compounds by statistical learning methods.

Various toxicological profiles, such as genotoxic potential, need to be studied in drug discovery processes and submitted to the drug regulatory authorities for drug safety evaluation. As part of the effort for developing low cost and efficient adverse drug reaction testing tools, several statistical learning methods have been used for developing genotoxicity prediction systems with an accuracy of up to 73.8% for genotoxic (GT+) and 92.8% for nongenotoxic (GT-) agents. These systems have been developed and tested by using less than 400 known GT+ and GT- agents, which is significantly less in number and diversity than the 860 GT+ and GT- agents known at present. There is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules and to evaluate other statistical learning methods not yet applied to genotoxicity prediction. This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (k-NN), and C4.5 decision tree (DT). A feature selection method, recursive feature elimination, is used for selecting molecular descriptors relevant to genotoxicity study. The overall accuracies of SVM, k-NN, and PNN are comparable to and those of DT lower than the results from earlier studies, with SVM giving the highest accuracies of 77.8% for GT+ and 92.7% for GT- agents. Our study suggests that statistical learning methods, particularly SVM, k-NN, and PNN, are useful for facilitating the prediction of genotoxic potential of a diverse set of molecules.

Computational Biology↗

PubMed2004

Protein designability analysis in sequence principal component space using 2D lattice model.

The number of proteins that fold into a certain structure differs drastically. The designability of a protein structure, which is defined as the number of sequences that have that structure as their unique lowest energy state, is studied in this paper using a simplified lattice model. The two-letter (HP) code and the pair-contact energy model are employed in the formulation of the relationship between the protein sequences and the compact structures. Due to the correlations between different dimensions, principal component analysis (PCA) is carried out to remove these correlations and develop reliable approximations of probability density functions of the protein sequences and the compact structures. An estimation of designability is derived using these probability density functions. Good correlation between estimated designabilities and those obtained through enumerative calculations is successfully achieved.

Algorithms↗

PubMed1998

Tumor suppressor proteins as regulators of cell differentiation.

The products of the tumor suppressor genes are considered to function as specific inhibitors of tumor cell growth. In this communication, we present evidence to show that these proteins inhibit tumor cell proliferation by participating in the activation of tumor cell differentiation. The ML-1 human myeloblastic leukemia cells used in this study proliferate when treated with insulin-like growth factor I and transferrin but differentiate to monocytes when exposed to tumor necrosis factor alpha or transforming growth factor beta1, or to macrophage-like cells when treated with both these cytokines. Initiation of proliferation but not of differentiation was followed by a 20- to 25-fold increase in the nuclear level of the DNA polymerase-associated processivity factor PCNA and of the proliferation-specific transcription factor E2F1. In contrast, induction of differentiation but not of proliferation was followed by a 25- to 30-fold increase in the nuclear level of the tumor suppressor proteins p53 (wild type), pRb, and p130/Rb2 and of the p53-dependent cyclin kinase inhibitor p21/Cip1. p53 and p21/Cip1, respectively, inhibit the expression and activation of PCNA, whereas p130 and pRb, respectively, inhibit the expression and activation of E2F1. As a result, G1-S-associated DNA and mRNA synthesis is inhibited, growth uncoupled from differentiation, and maturation enabled to proceed. Where this function of the tumor suppressor proteins is impaired, the capacity for differentiation is lost, which leads to the sustained proliferation that is characteristic of the cancer cell.

Carrier Proteins↗

PubMed1998

Melatonin reduces the increase in 8-hydroxy-deoxyguanosine levels in the brain and liver of kainic acid-treated rats.

In the present study, the effect of melatonin on oxidative DNA damage induced by kainic acid (KA) treatment was investigated. 8-hydroxy-deoxyguanosine (8-OH-dG) is a main product of oxidatively damaged DNA and was used as the endpoint in these studies. The levels of 8-OH-dG were found to be elevated in the hippocampus and frontal cortex of rats treated with KA. These elevated levels were significantly reduced in animals that were co-treated with melatonin. Thus, there was no difference in 8-OH-dG levels in the brain of control rats compared to those treated with KA (10 mg/kg) plus melatonin (10 mg/kg). The levels of 8-OH-dG also increased in the liver of rats treated with KA. This rise in oxidatively damaged DNA was also prevented by melatonin administration. Melatonin's ability to reduce KA-induced increases in neural and hepatic 8-OH-dG levels presumably relates to its direct free radical scavenging ability and possibly to other antioxidative actions of melatonin.

8-Hydroxy-2'-Deoxyguanosine↗

PubMed1998

Preparing for the third millennium: the views of life informatics.

The chief aspects of this paper are the condition of the birth of life informatics and its tasks, basic concepts, principles, and structure. There are three phases of combining informatics with medicine: product, technological, and theoretic application of which the goals are respectively the informatization of numerical and word processing, data of medical treatment, and the knowledge of medicine. While reached the third phase we have dealt with two types of biological information, physical and nonphysical, i.e., body information (i.e., the information about body's components and structure), and life information (i.e., the information about life codes and life programs). Life informatics is a main branch of bioinformatics. It is a new member of the medical informatics family, and as such is younger than health informatics, nursing informatics, and dental informatics. It's task is to assist biologists and medical doctors to recognize and interfere the human life information procedure just as they are doing well with human body's matter and energy system. Its basic concepts are life information, life information medicine, and life information therapy. Its most important principles are information materialism, general informatics, and information determinism. Its main branches are biomolecule, cellular, organic, individual, and social informatics. In the third millennium, the life informatics will be a leading discipline in biology, medicine and informatics, which will gradually influence modern philosophy and other humanities.

Biology↗

PubMed1998

Upgrading clinical decision support with published evidence: what can make the biggest difference?

BACKGROUND: To enhance clinical decision support, presented messages are increasingly supplemented with information from the medical literature. The goal of this study was to identify types of evidence that can lead to the biggest difference. METHODS: Seven versions of a questionnaire were mailed to randomly selected active family practice physicians and internists across the United States. They were asked about the perceived values of evidence from randomized controlled trials, locally developed recommendations, no evidence, cost-effectiveness studies, expert opinion, epidemiologic studies, and clinical studies. Analysis of variance and pairwise comparisons were used for statistical testing. RESULTS: Seventy-six (52%) physicians responded. On a Likert scale from one to six, randomized controlled clinical trial was the highest rated evidence (mean 5.07, SD +/- 1.14). Such evidence was significantly superior to locally developed recommendations and no evidence at all (P < .05). The interaction was also strong between the types of evidence and clinical areas (P = .0001). CONCLUSION: While most health care organizations present data without interpretation or simply try to enforce locally developed recommendations, such approaches appear to be inferior to techniques of reporting data with pertinent controlled evidence from the literature. Investigating physicians' perceptions is likely to benefit the design of computer generated messages.

Adult↗

PubMed1997

Cataractogenesis and lipid peroxidation in newborn rats treated with buthionine sulfoximine: preventive actions of melatonin.

The purpose of this study was to examine the influence of exogenously administered melatonin on cataract formation and lipid peroxidation in newborn rats treated with buthionine sulfoximine (BSO), a drug which inhibits the rate-limiting enzyme in glutathione (GSH) synthesis, gamma-glutamylcysteine synthase, thereby depleting animals of their stores of the important intracellular antioxidant, GSH. BSO (3 mmol/kg BW) was given for three consecutive days beginning on postnatal day 2; melatonin (4 mg/kg) was injected daily beginning on postnatal day 2 and continuing until the animals were killed (either day 9 or day 17 after birth). None of the control animals (rats treated with neither BSO nor with melatonin) developed lenticular opacification during the observation period. In the BSO-treated rats, 16 of 18 animals (89%) had observable cataracts when they were examined. In rats that received both BSO and melatonin, the incidence of cataracts was highly significantly decreased, i.e., only 3 of 18 rats (7%) had observable cataracts. In addition to cataracts, the level of lipid peroxidation products (malondialdehyde (MDA) and 4-hydroxyalkenals (4-HDA)) was examined in the lens, brain, liver, lung, and kidney of control and experimental animals. In BSO-treated rats, the lens, kidney, and lung exhibited increased levels of MDA plus 4-HDA relative to those measured in the control rats; these increases were reversed in the BSO-treated rats who were injected with melatonin daily. While BSO administration did not increase basal levels of MDA plus 4-HDA in either the brain or liver, melatonin reduced levels of lipid peroxidation products below those measured in the control rats (at 17 days after birth). The changes induced by melatonin are consistent with the free-radical scavenging and antioxidative properties of this indole.

Animals↗

PubMed1995

The Missouri Medical Informatics Thesaurus.

The Missouri Medical Informatics Thesaurus, containing approximately 2,000 sorted terms, arranged within a hierarchical structure, covers the multi-disciplinary medical informatics field more accurately than anything else available. Researchers at the University of Missouri-Columbia developed this thesaurus using the four primary methods of thesaurus construction to assure both literary and user warrant in the final terminology. The thesaurus can also be quickly revised to include the rapidly evolving terminology of the discipline.

Medical Informatics↗

PubMed1995

Multipurpose practice guideline modules for clinical decision analysis and quality improvement.

This study aimed to measure the loss of information in communication between clinical researchers and information system developers, to design a tool for sharing clinical practice guidelines, and to test the feasibility of this tool in knowledge base development. The analysis of a sample of 101 randomized controlled clinical trials indicated that exact numeric results were published in only 64 percent of the trials. While structured abstracts were associated with improved total quality scores (p0.001), there was no significant improvement in reporting numeric results (p=0.31). Based on the documented loss of information and the needs of various parties involved, the concept of a multipurpose knowledge module was developed for sharing clinical practice recommendations. Such clinical practice guideline modules were applied in developing the knowledge base of a quality feedback expert system (QFES).

Abstracting and Indexing↗

PubMed1994

Down-regulation of c-myc gene expression with induction of high molecular weight DNA fragments by fluorodeoxyuridine.

5-Fluoro-2'-deoxyuridine (FdUrd), a potent inhibitor of thymidylate synthase, induces extensive bulk DNA damage at drug concentrations that produce significant in vitro growth inhibition of human ileocecal carcinoma (HCT-8) cells. Constant- and pulsed-field gel electrophoresis (CFGE and PFGE), to detect size distribution of DNA double-strand breaks and repair kinetics, in parallel with northern and western blot analyses, to quantitate c-myc gene and protein expression, were utilized to analyze drug effects. At 24-hr post in vitro drug treatment, when maximum bulk DNA damage was detected, FdUrd produced a broad range of high molecular weight DNA fragments, clustering between 0.1 and 5.7 megabases in size, and resulted in a decrease in the level of c-myc transcripts and protein with no significant effect on the level of v-myc and H-ras. These effects preceded the observed cellular growth inhibition. Addition of the reduced folate leucovorin potentiated the effects induced by FdUrd, indicating that thymidylate synthase inhibition is an important initial step in drug effect followed by DNA fragmentation and suppression of c-myc expression. Changes in the integrity of the genetic materials and regulatory genes occurred prior to the observed cell growth inhibition by FdUrd, suggesting that these molecular alterations by FdUrd may be associated with subsequent FdUrd-induced cell growth inhibition.

Cell Division↗