A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

High-Throughput Sequencing to Reveal Genes Involved in Reproduction and Development in Bactrocera dorsalis (Diptera: Tephritidae)

BACKGROUND: Tephritid fruit flies in the genus Bactrocera are of major economic significance in agriculture causing considerable loss to the fruit and vegetable industry. Currently, there is no ideal control program. Molecular means is an effective method for pest control at present, but genomic or transcriptomic data for members of this genus remains limited. To facilitate molecular research into reproduction and development mechanisms, and finally effective control on these pests, an extensive transcriptome for the oriental fruit fly Bactrocera dorsalis was produced using the Roche 454-FLX platform. RESULTS: We obtained over 350 million bases of cDNA derived from the whole body of B. dorsalis at different developmental stages. In a single run, 747,206 sequencing reads with a mean read length of 382 bp were obtained. These reads were assembled into 28,782 contigs and 169,966 singletons. The mean contig size was 750 bp and many nearly full-length transcripts were assembled. Additionally, we identified a great number of genes that are involved in reproduction and development as well as genes that represent nearly all major conserved metazoan signal transduction pathways, such as insulin signal transduction. Furthermore, transcriptome changes during development were analyzed. A total of 2,977 differentially expressed genes (DEGs) were detected between larvae and pupae libraries, while there were 1,621 DEGs between adults and larvae, and 2,002 between adults and pupae. These DEGs were functionally annotated with KEGG pathway annotation and 9 genes were validated by qRT-PCR. CONCLUSION: Our data represent the extensive sequence resources available for B. dorsalis and provide for the first time access to the genetic architecture of reproduction and development as well as major signal transduction pathways in the Tephritid fruit fly pests, allowing us to elucidate the molecular mechanisms underlying courtship, ovipositing, development and detailed analyses of the signal transduction pathways

Gene Expression in a Drosophila Model of Mitochondrial Disease

Background A point mutation in the Drosophila gene technical knockout (tko), encoding mitoribosomal protein S12, was previously shown to cause a phenotype of respiratory chain deficiency, developmental delay, and neurological abnormalities similar to those presented in many human mitochondrial disorders, as well as defective courtship behavior. Methodology/Principal Findings Here, we describe a transcriptome-wide analysis of gene expression in tko25t mutant flies that revealed systematic and compensatory changes in the expression of genes connected with metabolism, including up-regulation of lactate dehydrogenase and of many genes involved in the catabolism of fats and proteins, and various anaplerotic pathways. Gut-specific enzymes involved in the primary mobilization of dietary fats and proteins, as well as a number of transport functions, were also strongly up-regulated, consistent with the idea that oxidative phosphorylation OXPHOS dysfunction is perceived physiologically as a starvation for particular biomolecules. In addition, many stress-response genes were induced. Other changes may reflect a signature of developmental delay, notably a down-regulation of genes connected with reproduction, including gametogenesis, as well as courtship behavior in males; logically this represents a programmed response to a mitochondrially generated starvation signal. The underlying signalling pathway, if conserved, could influence many physiological processes in response to nutritional stress, although any such pathway involved remains unidentified. Conclusions/Significance These studies indicate that general and organ-specific metabolism is transformed in response to mitochondrial dysfunction, including digestive and absorptive functions, and give important clues as to how novel therapeutic strategies for mitochondrial disorders might be developed.Public Library of Scienc

Systems Perspectives on the Interaction Between Human and Technological Resources

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