Artificial Intelligence for chemical risk assessment.

As the basis for managing the risks of chemical exposure, the Chemical Risk Assessment (CRA) process can impact a substantial part of the economy, the health of hundreds of millions of people, and the condition of the environment. However, the number of properly assessed chemicals falls short of societal needs due to a lack of experts for evaluation, interference of third party interests, and the sheer volume of potentially relevant information on the chemicals from disparate sources. In order to explore ways in which computational methods may help overcome this discrepancy between the number of chemical risk assessments required on the one hand and the number and adequateness of assessments actually being conducted on the other, the European Commission's Joint Research Centre organised a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA). The workshop identified a number of areas where Artificial Intelligence could potentially increase the number and quality of regulatory risk management decisions based on CRA, involving process simulation, supporting evaluation, identifying problems, facilitating collaboration, finding experts, evidence gathering, systematic review, knowledge discovery, and building cognitive models. Although these are interconnected, they are organised and discussed under two main themes: scientific-technical process and social aspects and the decision making process.

Depression, depressive symptoms and treatments in women who have recently given birth: UK cohort study.

OBJECTIVES: To investigate how depression is recognised in the year after child birth and treatment given in clinical practice. DESIGN: Cohort study based on UK primary care electronic health records. SETTING: Primary care. PARTICIPANTS: Women who have given live birth between 2000 and 2013. OUTCOMES: Prevalence of postnatal depression, depression diagnoses, depressive symptoms, antidepressant and non-pharmacological treatment within a year after birth. RESULTS: Of 206 517 women, 23 623 (11%) had a record of depressive diagnosis or symptoms in the year after delivery and more than one in eight women received antidepressant treatment. Recording and treatment peaked 6-8 weeks after delivery. Initiation of selective serotonin reuptake inhibitors (SSRI) treatment has become earlier in the more recent years. Thus, the initiation rate of SSRI treatment per 100 pregnancies (95% CI) at 8 weeks were 2.6 (2.5 to 2.8) in 2000-2004, increasing to 3.0 (2.9 to 3.1) in 2005-2009 and 3.8 (3.6 to 3.9) in 2010-2013. The overall rate of initiation of SSRI within the year after delivery, however, has not changed noticeably. A third of the women had at least one record suggestive of depression at any time prior to delivery and of these one in four received SSRI treatment in the year after delivery.Younger women were most likely to have records of depression and depressive symptoms. (Relative risk for postnatal depression: age 15-19: 1.92 (1.76 to 2.10), age 20-24: 1.49 (1.39 to 1.59) versus age 30-34). The risk of depression, postnatal depression and depressive symptoms increased with increasing social deprivation. CONCLUSIONS: More than 1 in 10 women had electronic health records indicating depression diagnoses or depressive symptoms within a year after delivery and more than one in eight women received antidepressant treatment in this period. Women aged below 30 and from the most deprived areas were at highest risk of depression and most likely to receive antidepressant treatment.

Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge.

Motivation: Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation: Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information: Supplementary data are available at Bioinformatics online.

Phenotype-driven identification of epithelial signalling clusters.

In metazoans, epithelial architecture provides a context that dynamically modulates most if not all epithelial cell responses to intrinsic and extrinsic signals, including growth or survival signalling and transforming oncogene action. Three-dimensional (3D) epithelial culture systems provide tractable models to interrogate the function of human genetic determinants in establishment of context-dependency. We performed an arrayed genetic shRNA screen in mammary epithelial 3D cultures to identify new determinants of epithelial architecture, finding that the key phenotype impacting shRNAs altered not only the data population average but even more noticeably the population distribution. The broad distributions were attributable to sporadic gene silencing actions by shRNA in unselected populations. We employed Maximum Mean Discrepancy concept to capture similar population distribution patterns and demonstrate here the feasibility of the test in identifying an impact of shRNA in populations of 3D structures. Integration of the clustered morphometric data with protein-protein interactions data enabled hypothesis generation of novel biological pathways underlying similar 3D phenotype alterations. The results present a new strategy for 3D phenotype-driven pathway analysis, which is expected to accelerate discovery of context-dependent gene functions in epithelial biology and tumorigenesis.

Brain-to-brain hyperclassification reveals action-specific motor mapping of observed actions in humans.

Seeing an action may activate the corresponding action motor code in the observer. It remains unresolved whether seeing and performing an action activates similar action-specific motor codes in the observer and the actor. We used novel hyperclassification approach to reveal shared brain activation signatures of action execution and observation in interacting human subjects. In the first experiment, two "actors" performed four types of hand actions while their haemodynamic brain activations were measured with 3-T functional magnetic resonance imaging (fMRI). The actions were videotaped and shown to 15 "observers" during a second fMRI experiment. Eleven observers saw the videos of one actor, and the remaining four observers saw the videos of the other actor. In a control fMRI experiment, one of the actors performed actions with closed eyes, and five new observers viewed these actions. Bayesian canonical correlation analysis was applied to functionally realign observers' and actors' fMRI data. Hyperclassification of the seen actions was performed with Bayesian logistic regression trained on actors' data and tested with observers' data. Without the functional realignment, between-subjects accuracy was at chance level. With the realignment, the accuracy increased on average by 15 percentage points, exceeding both the chance level and the accuracy without functional realignment. The highest accuracies were observed in occipital, parietal and premotor cortices. Hyperclassification exceeded chance level also when the actor did not see her own actions. We conclude that the functional brain activation signatures underlying action execution and observation are partly shared, yet these activation signatures may be anatomically misaligned across individuals.

Finite adaptation and multistep moves in the metropolis-hastings algorithm for variable selection in genome-wide association analysis.

High-dimensional datasets with large amounts of redundant information are nowadays available for hypothesis-free exploration of scientific questions. A particular case is genome-wide association analysis, where variations in the genome are searched for effects on disease or other traits. Bayesian variable selection has been demonstrated as a possible analysis approach, which can account for the multifactorial nature of the genetic effects in a linear regression model.Yet, the computation presents a challenge and application to large-scale data is not routine. Here, we study aspects of the computation using the Metropolis-Hastings algorithm for the variable selection: finite adaptation of the proposal distributions, multistep moves for changing the inclusion state of multiple variables in a single proposal and multistep move size adaptation. We also experiment with a delayed rejection step for the multistep moves. Results on simulated and real data show increase in the sampling efficiency. We also demonstrate that with application specific proposals, the approach can overcome a specific mixing problem in real data with 3822 individuals and 1,051,811 single nucleotide polymorphisms and uncover a variant pair with synergistic effect on the studied trait. Moreover, we illustrate multimodality in the real dataset related to a restrictive prior distribution on the genetic effect sizes and advocate a more flexible alternative.

Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study).

Diabetic kidney disease, diagnosed by urinary albumin excretion rate (AER), is a critical symptom of chronic vascular injury in diabetes, and is associated with dyslipidemia and increased mortality. We investigated serum lipids in 326 subjects with type 1 diabetes: 56% of patients had normal AER, 17% had microalbuminuria (20 ≤ AER < 200 µg/min or 30 ≤ AER < 300 mg/24 h) and 26% had overt kidney disease (macroalbuminuria AER ≥ 200 µg/min or AER ≥ 300 mg/24 h). Lipoprotein subclass lipids and low-molecular-weight metabolites were quantified from native serum, and individual lipid species from the lipid extract of the native sample, using a proton NMR metabonomics platform. Sphingomyelin (odds ratio 2.53, P < 10(-7)), large VLDL cholesterol (odds ratio 2.36, P < 10(-10)), total triglycerides (odds ratio 1.88, P < 10(-6)), omega-9 and saturated fatty acids (odds ratio 1.82, P < 10(-5)), glucose disposal rate (odds ratio 0.44, P < 10(-9)), large HDL cholesterol (odds ratio 0.39, P < 10(-9)) and glomerular filtration rate (odds ratio 0.19, P < 10(-10)) were associated with kidney disease. No associations were found for polyunsaturated fatty acids or phospholipids. Sphingomyelin was a significant regressor of urinary albumin (P < 0.0001) in multivariate analysis with kidney function, glycemic control, body mass, blood pressure, triglycerides and HDL cholesterol. Kidney injury, sphingolipids and excess fatty acids have been linked in animal models-our exploratory approach provides independent support for this relationship in human patients with diabetes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0343-y) contains supplementary material, which is available to authorized users.

Bayesian variable selection in searching for additive and dominant effects in genome-wide data.

Although complex diseases and traits are thought to have multifactorial genetic basis, the common methods in genome-wide association analyses test each variant for association independent of the others. This computational simplification may lead to reduced power to identify variants with small effect sizes and requires correcting for multiple hypothesis tests with complex relationships. However, advances in computational methods and increase in computational resources are enabling the computation of models that adhere more closely to the theory of multifactorial inheritance. Here, a Bayesian variable selection and model averaging approach is formulated for searching for additive and dominant genetic effects. The approach considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait. We first characterize the behavior of the approach in simulations. The results indicate a gain in the causal variant identification performance when additive and dominant variation are simulated, with a negligible loss of power in purely additive case. An application to the analysis of high- and low-density lipoprotein cholesterol levels in a dataset of 3895 Finns is then presented, demonstrating the feasibility of the approach at the current scale of single-nucleotide polymorphism data. We describe a Markov chain Monte Carlo algorithm for the computation and give suggestions on the specification of prior parameters using commonly available prior information. An open-source software implementing the method is available at http://www.lce.hut.fi/research/mm/bmagwa/ and https://github.com/to-mi/.

Arterial stiffness and vascular complications in patients with type 1 diabetes: the Finnish Diabetic Nephropathy (FinnDiane) Study.

INTRODUCTION/AIMS: While patients with type 1 diabetes (T1D) are known to suffer from early cardiovascular disease (CVD), we examined associations between arterial stiffness and diabetic complications in a large patient group with T1D. METHODS: This study included 807 subjects (622 T1D and 185 healthy volunteers (age 40.6 ± 0.7 versus 41.6 ± 1.2 years; P = NS)). Arterial stiffness was measured by pulse wave analysis from each participant. Furthermore, information on diabetic retinopathy, nephropathy, and CVD was collected. The renal status was verified from at least two out of three urine collections. RESULTS: Patients with T1D without signs of diabetic nephropathy had stiffer arteries measured as the augmentation index (AIx) than age-matched control subjects (17.3% ± 0.6% versus 10.0% ± 1.2%; P < 0.001). Moreover, AIx (OR 1.08; 95% CI 1.03-1.13; P = 0.002) was associated with diabetic laser-treated retinopathy in patients with normoalbuminuria in a multivariate logistic regression analysis. The same was true for AIx and diabetic nephropathy (1.04 (1.01-1.08); P = 0.004) as well as AIx and CVD (1.06 (1.00-1.12); P = 0.01) in patients with T1D. CONCLUSIONS: Arterial stiffness was associated with microvascular and macrovascular complications in patients with T1D.

The interplay between lipoprotein phenotypes, adiponectin, and alcohol consumption.

CONTEXT AND OBJECTIVE: Lipoproteins are involved in the pathophysiology of several metabolic diseases. Here we focus on the interplay between lipoprotein metabolism and adiponectin with the extension of alcohol intake. DESIGN AND SUBJECTS: Eighty-three low-to-moderate and 80 heavy alcohol drinkers were studied. Plasma adiponectin, other biochemical and extensive lipoprotein data were measured. Self-organizing maps were applied to characterize lipoprotein phenotypes and their interrelationships with biochemical measures and alcohol consumption. RESULTS: Alcohol consumption and plasma adiponectin had a strong positive association. Heavy alcohol consumption was associated with decreased low-density lipoprotein cholesterol (LDL-C). Nevertheless, two distinct lipoprotein phenotypes were identified, one with elevated high-density lipoprotein cholesterol (HDL-C) and decreased very-low-density lipoprotein triglycerides (VLDL-TG) together with low prevalence of metabolic syndrome, and the other vice versa. The HDL particles were enlarged in both phenotypes related to the heavy drinkers. The low-to-moderate alcohol drinkers were characterized with high LDL-C and C-enriched LDL particles. CONCLUSIONS: The analyses per se illustrated the multi-faceted and non-linear nature of lipoprotein metabolism. The heavy alcohol drinkers were characterized either by an anti-atherogenic lipoprotein phenotype (with also the highest adiponectin concentrations) or by a phenotype with pro-atherogenic and metabolic syndrome-like features. Clinically this underlines the need to distinguish the differing individual risk for lipid-related metabolic disturbances also in heavy alcohol drinkers.

Metabolic diversity of progressive kidney disease in 325 patients with type 1 diabetes (the FinnDiane Study).

Type 1 diabetic patients with varying severity of kidney disease were investigated to create multimetabolite models of the disease process. Urinary albumin excretion rate was measured for 3358 patients with type 1 diabetes. Prospective records were available for 1051 patients, of whom 163 showed progression of albuminuria (8.3-year follow-up), and 162 were selected as stable controls. At baseline, serum lipids, lipoprotein subclasses, and low-molecular weight metabolites were quantified by NMR spectroscopy (325 samples). The data were analyzed by the self-organizing map. In cross-sectional analyses, patients with no complications had low serum lipids, less inflammation, and better glycemic control, whereas patients with advanced kidney disease had high serum cystatin-C and sphingomyelin. These phenotype extremes shared low unsaturated fatty acids (UFAs) and phospholipids. Prospectively, progressive albuminuria was associated with high UFAs, phospholipids, and IDL and LDL lipids. Progression at longer duration was associated with high HDL lipids, whereas earlier progression was associated with poor glycemic control, increased saturated fatty acids (SFAs), and inflammation. Diabetic kidney disease consists of diverse metabolic phenotypes: UFAs, phospholipids, IDL, and LDL may be important in the subclinical phase, high SFAs and low HDL suggest accelerated progression, and the sphingolipid pathway in advanced kidney injury deserves further research.