Biomedical subjects

Rory Martin

Publications and source records attributed to Rory Martin.

3 recordsLinked to original sources

PubMed2006

Assessing performance of prediction rules in machine learning.

INTRODUCTION: An important goal in machine learning is to assess the degree to which prediction rules are robust and replicable, since these rules are used for decision making and for planning follow-up studies. This requires an estimate of a prediction rule's true error rate, a statistic that can be estimated by resampling data. However, there are many possible approaches depending upon whether we draw observations with or without replacement, or sample once, repeatedly, or not at all, and the pros and cons of each are often unclear. This study illustrates and compares different methods for estimating true error with the aim of providing practical guidance to users of machine learning techniques. METHODS: We conducted Monte Carlo simulation studies using four different error estimators: bootstrap, split sample, resubstitution and a direct estimate of true error. Here, 'split sample' refers to a single random partition of the data into a pair of training and test samples, a popular scheme. We used stochastic gradient boosting as a learning algorithm, and considered data from two studies for which the underlying data mechanism was known to be complex: a library of 6000 tripeptide substrates collected for analysis of proteasome inhibition as part of anticancer drug design, and a cardiovascular study involving 600 subjects receiving antiplatelet treatment for acute coronary syndrome. RESULTS: There were important differences in the performance of the various error estimators examined. Error estimators for split sample and resubstitution, while being the most transparent in action and the simplest to apply, did not quantify the performance of prediction rules as accurately as the bootstrap. This was true for both types of study data, despite their highly different nature. CONCLUSIONS: The robustness and reliability of decisions based on analysis of genomics data could, in many cases, be improved by following best practices for prediction error estimation. For this, techniques such as bootstrap should be considered.

Algorithms↗

PubMed2006

Toxicogenomics strategies for predicting drug toxicity.

INTRODUCTION: The failure of pharmaceutical drug candidates due to toxicity, especially hepatotoxicity, is an important and continuing problem for drug development. The current manuscript explores new toxicogenomics approaches to better understand the hepatotoxic potential of human pharmaceutical compounds and to assess their toxicity earlier in the drug development process by means of a toxicity screen. RESOURCES: Data consisted of two commercial knowledgebases that employed a hybrid experimental design in which human drug toxicity information was extracted from the literature, dichotomized and merged with rat-based gene expression measures. One knowledgebase used gene expression from rat primary hepatocytes while the other employed whole rats. Approximately 100 compounds were used in each. METHODS: Toxicity classification rules were built using a stochastic gradient boosting machine learner, with classification error estimated using a modified bootstrap estimate of true error. Several types of clustering methods were also applied, some based on sets of compounds and others based on sets of genes. RESULTS: Robust classification rules were constructed for both in vitro (hepatocytes) and in vivo (liver) data, based on a high dose, 24-hour design. There appeared to be little overlap between the two classifiers, at least in terms of their gene lists. Robust classifiers could not be fitted when earlier timepoints and/or low dose data were included, indicating that experimental design is important for these systems. CONCLUSIONS: In light of these findings, a working compound screen based on these toxicity classifiers appears feasible, with classifier operating characteristics used to tune a screen for a specific implementation. To ensure robust and optimal performance, issues such as site variability of microarrays and generalizability of findings should be addressed as indicated.

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed2003

Haplotype transmission analysis provides evidence of association for DISC1 to schizophrenia and suggests sex-dependent effects.

We have previously reported a linkage peak on 1q42 in a Finnish schizophrenia sample. In this study we genotyped 28 single nucleotide polymorphisms (SNPs) from 1q42 covering the three candidate genes TRAX, DISC1 and DISC2, using a study sample of 458 Finnish families ascertained for schizophrenia. Two-point and haplotype association analysis revealed a significant region of interest within the DISC1 gene. A common haplotype (HEP3) was observed to be significantly under-transmitted to affected individuals (P=0.0031). HEP3 represents a two SNP haplotype spanning from intron 1 to exon 2 of DISC1. This haplotype also displayed sex differences in transmission distortion, the under-transmission being significant only to affected females (P=0.00024). Three other regions of interest were observed in the TRAX and DISC genes. However, analysis of only those families with complete genotype information specifically highlights the HEP3 haplotype as a true observation. The finding of a common under-transmitted SNP haplotype might imply that this particular allele offers some protection from the development of schizophrenia. Analysis of component-traits of schizophrenia, derived from the Operational Criteria Checklist of Psychotic Illness (OCCPI), displayed association of HEP3 to features of the general phenotype of schizophrenia, including traits representing delusions, hallucinations and negative symptoms. This study provides further evidence for the hypothesis that the DISC1 gene is involved in the aetiology of schizophrenia, and implies a putative sex difference for the effect of the gene. Our findings would also encourage more detailed analyses of the effect of DISC1 on the component-traits of schizophrenia.

Female↗