Clustering as an example of optimizing arbitrarily chosen objective functions

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison

Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure–activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure–property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics

Fuzzy Filtering: A Mathematical Theory and Applications in Life Science

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Transcriptomics in Toxicogenomics, Part III : Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.Peer reviewe

Functional analysis and transcriptional output of the Göttingen minipig genome

In the past decade the Göttingen minipig has gained increasing recognition as animal model in pharmaceutical and safety research because it recapitulates many aspects of human physiology and metabolism. Genome-based comparison of drug targets together with quantitative tissue expression analysis allows rational prediction of pharmacology and cross-reactivity of human drugs in animal models thereby improving drug attrition which is an important challenge in the process of drug development.; Here we present a new chromosome level based version of the Göttingen minipig genome together with a comparative transcriptional analysis of tissues with pharmaceutical relevance as basis for translational research. We relied on mapping and assembly of WGS (whole-genome-shotgun sequencing) derived reads to the reference genome of the Duroc pig and predict 19,228 human orthologous protein-coding genes. Genome-based prediction of the sequence of human drug targets enables the prediction of drug cross-reactivity based on conservation of binding sites. We further support the finding that the genome of Sus scrofa contains about ten-times less pseudogenized genes compared to other vertebrates. Among the functional human orthologs of these minipig pseudogenes we found HEPN1, a putative tumor suppressor gene. The genomes of Sus scrofa, the Tibetan boar, the African Bushpig, and the Warthog show sequence conservation of all inactivating HEPN1 mutations suggesting disruption before the evolutionary split of these pig species. We identify 133 Sus scrofa specific, conserved long non-coding RNAs (lncRNAs) in the minipig genome and show that these transcripts are highly conserved in the African pigs and the Tibetan boar suggesting functional significance. Using a new minipig specific microarray we show high conservation of gene expression signatures in 13 tissues with biomedical relevance between humans and adult minipigs. We underline this relationship for minipig and human liver where we could demonstrate similar expression levels for most phase I drug-metabolizing enzymes. Higher expression levels and metabolic activities were found for FMO1, AKR/CRs and for phase II drug metabolizing enzymes in minipig as compared to human. The variability of gene expression in equivalent human and minipig tissues is considerably higher in minipig organs, which is important for study design in case a human target belongs to this variable category in the minipig. The first analysis of gene expression in multiple tissues during development from young to adult shows that the majority of transcriptional programs are concluded four weeks after birth. This finding is in line with the advanced state of human postnatal organ development at comparative age categories and further supports the minipig as model for pediatric drug safety studies.; Genome based assessment of sequence conservation combined with gene expression data in several tissues improves the translational value of the minipig for human drug development. The genome and gene expression data presented here are important resources for researchers using the minipig as model for biomedical research or commercial breeding. Potential impact of our data for comparative genomics, translational research, and experimental medicine are discussed