Search PubMed⌕ Search

Biomedical subjects

Peter J Fleming

Publications and source records attributed to Peter J Fleming.

5 recordsLinked to original sources
PubMed2003

Sudden infant death syndrome and social deprivation: assessing epidemiological factors after post-matching for deprivation.

As part of the confidential enquiry into stillbirths and deaths in infancy (CESDI), a 3-year population-based case-control study was specifically designed to look at risk factors associated with sudden infant death syndrome (SIDS) after the dramatic fall in incidence. The study was conducted between 1993 and 1996 in five English Health Regions (population 17 million) with parental interviews for each death and four age-matched controls. The aim of this analysis was to investigate the extent to which epidemiological characteristics associated with SIDS were particular to the syndrome or more general markers for socio-economic deprivation. One control was reassigned to each case post-matched for infant age, time of sleep and socio-economic status using components of the Townsend Deprivation Score. The post-matched analysis involved 323 SIDS infants and 323 controls with a similar socio-economic profile. Notable factors significant in the original univariable analysis that became non-significant after post-matching included young maternal age (median: 23 years 4 months SIDS vs. 23 years 11 months post-matched controls), being an unsupported mother (13.6% SIDS vs. 11.1% post-matched controls) and being bottle-fed (56.7% SIDS vs. 55.4% post-matched controls). Other factors, although clearly related to deprivation, such as parental smoking, remained significant in both the univariable and multivariable post-matched analyses.

Adult↗
PubMed2002

Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs.

Deriving quantitative structure-activity relationship (QSAR) models that are accurate, reliable, and easily interpretable is a difficult task. In this study, two new methods have been developed that aim to find useful QSAR models that represent an appropriate balance between model accuracy and complexity. Both methods are based on genetic programming (GP). The first method, referred to as genetic QSAR (or GPQSAR), uses a penalty function to control model complexity. GPQSAR is designed to derive a single linear model that represents an appropriate balance between the variance and the number of descriptors selected for the model. The second method, referred to as multiobjective genetic QSAR (MoQSAR), is based on multiobjective GP and represents a new way of thinking of QSAR. Specifically, QSAR is considered as a multiobjective optimization problem that comprises a number of competitive objectives. Typical objectives include model fitting, the total number of terms, and the occurrence of nonlinear terms. MoQSAR results in a family of equivalent QSAR models where each QSAR represents a different tradeoff in the objectives. A practical consideration often overlooked in QSAR studies is the need for the model to promote an understanding of the biochemical response under investigation. To accomplish this, chemically intuitive descriptors are needed but do not always give rise to statistically robust models. This problem is addressed by the addition of a further objective, called chemical desirability, that aims to reward models that consist of descriptors that are easily interpretable by chemists. GPQSAR and MoQSAR have been tested on various data sets including the Selwood data set and two different solubility data sets. The study demonstrates that the MoQSAR method is able to find models that are at least as good as models derived using standard statistical approaches and also yields models that allow a medicinal chemist to trade statistical robustness for chemical interpretability.

Algorithms↗
PubMed2002

Designing focused libraries using MoSELECT.

When designing a combinatorial library it is usually desirable to optimise multiple properties of the library simultaneously and often the properties are in competition with one another. For example, a library that is designed to be focused around a given target molecule should ideally have minimum cost and also contain molecules that are bioavailable. In this paper, we describe the program MoSELECT for multiobjective library design that is based on a multiobjective genetic algorithm (MOGA). MoSELECT searches the product-space of a virtual combinatorial library to generate a family of equivalent solutions where each solution represents a combinatorial subset of the virtual library optimised over multiple objectives. The family of solutions allows the relationships between the objectives to be explored and thus enables the library designer to make an informed choice on an appropriate compromise solution. Experiments are reported where MoSELECT has been applied to the design of various focused libraries.

Algorithms↗
PubMedDate not supplied

Combinatorial library design using a multiobjective genetic algorithm.

Early results from screening combinatorial libraries have been disappointing with libraries either failing to deliver the improved hit rates that were expected or resulting in hits with characteristics that make them undesirable as lead compounds. Consequently, the focus in library design has shifted toward designing libraries that are optimized on multiple properties simultaneously, for example, diversity and "druglike" physicochemical properties. Here we describe the program MoSELECT that is based on a multiobjective genetic algorithm and which is able to suggest a family of solutions to multiobjective library design where all the solutions are equally valid and each represents a different compromise between the objectives. MoSELECT also allows the relationships between the different objectives to be explored with competing objectives easily identified. The library designer can then make an informed choice on which solution(s) to explore. Various performance characteristics of MoSELECT are reported based on a number of different combinatorial libraries.

Algorithms↗