| 1. |
- Ahlberg, Ernst, et al.
(författare)
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Using conformal prediction to prioritize compound synthesis in drug discovery
- 2017
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Ingår i: Proceedings of Machine Learning Research. - Stockholm : Machine Learning Research. ; , s. 174-184
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Konferensbidrag (refereegranskat)abstract
- The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.
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| 2. |
- Linusson, Henrik, et al.
(författare)
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On the calibration of aggregated conformal predictors
- 2017
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Ingår i: Proceedings of Machine Learning Research. - : Machine Learning Research. ; , s. 154-173
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Konferensbidrag (refereegranskat)abstract
- Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical validity is conditional on additional assumptions that require further clarification. In this paper, we show why validity is not automatic for aggregated conformal predictors, and provide a revised definition of aggregated conformal predictors that gains approximate validity conditional on properties of the underlying learning algorithm.
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| 3. |
- Attoff, Kristina, et al.
(författare)
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Whole genome microarray analysis of neural progenitor C17.2 cells during differentiation and validation of 30 neural mRNA biomarkers for estimation of developmental neurotoxicity
- 2017
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Ingår i: PLOS ONE. - San Francisco : Public Library of Science. - 1932-6203. ; 12:12
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Tidskriftsartikel (refereegranskat)abstract
- Despite its high relevance, developmental neurotoxicity (DNT) is one of the least studied forms of toxicity. Current guidelines for DNT testing are based on in vivo testing and they require extensive resources. Transcriptomic approaches using relevant in vitro models have been suggested as a useful tool for identifying possible DNT-generating compounds. In this study, we performed whole genome microarray analysis on the murine progenitor cell line C17.2 following 5 and 10 days of differentiation. We identified 30 genes that are strongly associated with neural differentiation. The C17.2 cell line can be differentiated into a co-culture of both neurons and neuroglial cells, giving a more relevant picture of the brain than using neuronal cells alone. Among the most highly upregulated genes were genes involved in neurogenesis (CHRDL1), axonal guidance (BMP4), neuronal connectivity (PLXDC2), axonogenesis (RTN4R) and astrocyte differentiation (S100B). The 30 biomarkers were further validated by exposure to non-cytotoxic concentrations of two DNT-inducing compounds (valproic acid and methylmercury) and one neurotoxic chemical possessing a possible DNT activity (acrylamide). Twenty-eight of the 30 biomarkers were altered by at least one of the neurotoxic substances, proving the importance of these biomarkers during differentiation. These results suggest that gene expression profiling using a predefined set of biomarkers could be used as a sensitive tool for initial DNT screening of chemicals. Using a predefined set of mRNA biomarkers, instead of the whole genome, makes this model affordable and high-throughput. The use of such models could help speed up the initial screening of substances, possibly indicating alerts that need to be further studied in more sophisticated models.
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| 4. |
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| 5. |
- Eklund, Martin, et al.
(författare)
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The application of conformal prediction to the drug discovery process
- 2015
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Ingår i: Annals of Mathematics and Artificial Intelligence. - : Springer Science+Business Media B.V.. - 1012-2443 .- 1573-7470. ; 74:1-2, s. 117-132
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Tidskriftsartikel (refereegranskat)abstract
- QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the conformal predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that conformal predictions are highly useful in the drug discovery process.
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| 6. |
- Forreryd, Andy, et al.
(författare)
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Predicting skin sensitizers with confidence : Using conformal prediction to determine applicability domain of GARD
- 2018
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Ingår i: Toxicology in Vitro. - : Elsevier. - 0887-2333 .- 1879-3177. ; 48, s. 179-187
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Tidskriftsartikel (refereegranskat)abstract
- GARD - Genomic Allergen Rapid Detection is a cell based alternative to animal testing for identification of skin sensitizers. The assay is based on a biomarker signature comprising 200 genes measured in an in vitro model of dendritic cells following chemical stimulations, and consistently reports predictive performances similar to 90% for classification of external test sets. Within the field of in vitro skin sensitization testing, definition of applicability domain is often neglected by test developers, and assays are often considered applicable across the entire chemical space. This study complements previous assessments of model performance with an estimate of confidence in individual classifications, as well as a statistically valid determination of the applicability domain for the GARD assay. Conformal prediction was implemented into current GARD protocols, and a large external test dataset (n = 70) was classified at a confidence level of 85%, to generate a valid model with a balanced accuracy of 88%, with none of the tested chemical reactivity domains identified as outside the applicability domain of the assay. In conclusion, results presented in this study complement previously reported predictive performances of GARD with a statistically valid assessment of uncertainty in each individual prediction, thus allowing for classification of skin sensitizers with confidence.
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| 7. |
- Jesús Naveja, J., et al.
(författare)
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Chemical space, diversity and activity landscape analysis of estrogen receptor binders
- 2018
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Ingår i: RSC Advances. - : Royal Society of Chemistry. - 2046-2069. ; 8:67, s. 38229-38237
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the structure-activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.
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| 8. |
- Kensert, Alexander, et al.
(författare)
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Evaluating parameters for ligand-based modeling with random forest on sparse data sets
- 2018
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Ingår i: Journal of Cheminformatics. - : BMC. - 1758-2946. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints (p <= 0.05), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint's radius.
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| 9. |
- Lindh, Martin, 1981-, et al.
(författare)
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Predicting the Rate of Skin Penetration Using an Aggregated Conformal Prediction Framework
- 2017
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Ingår i: Molecular Pharmaceutics. - Washington : American Chemical Society (ACS). - 1543-8384 .- 1543-8392. ; 14:5, s. 1571-1576
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Tidskriftsartikel (refereegranskat)abstract
- Skin serves as a drug administration route, and skin permeability of chemicals is of significant interest in the pharmaceutical and cosmetic industries. An aggregated conformal prediction (ACP) framework was used to build models, for predicting the permeation rate (log K-p) of chemical compounds through human skin. The conformal prediction method gives as an output the prediction range at a given level of confidence for each compound, which enables the user to make a more informed decision when, for example, suggesting the next compound to prepare, Predictive models were built using;both the random forest and the support vector machine methods and were based on experimentally derived permeability data on 211 diverse compounds. The derived models were of similar predictive quality as compared to earlier published models but have the extra advantage of not only presenting a single predicted value for each, compound but also a reliable, individually assigned prediction range. The models use calculated descriptors and can quickly predict the skin permeation rate of new compounds.
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| 10. |
- Ljunggren, Stefan, et al.
(författare)
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Alterations in high-density lipoprotein proteome and function associated with persistent organic pollutants
- 2017
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Ingår i: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 98, s. 204-211
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing body of evidence that persistent organic pollutants (POPs) may increase the risk for cardiovascular disease (CVD), but the mechanisms remain unclear. High- density lipoprotein (HDL) acts protective against CVD by different processes, andwe have earlier found that HDL from subjects with CVD contains higher levels of POPs than healthy controls. In the present study, we have expanded analyses on the same individuals living in a contaminated community and investigated the relationship between the HDL POP levels and protein composition/ function. HDL from17 subjectswas isolated by ultracentrifugation. HDL protein composition, using nanoliquid chromatography tandemmass spectrometry, and antioxidant activity were analyzed. The associations of 16 POPs, including polychlorinated biphenyls (PCBs) and organochlorine pesticides, with HDL proteins/functionswere investigated by partial least square and multiple linear regression analysis. Proteomic analyses identified 118 HDL proteins, of which ten were significantly (p b 0.05) and positively associated with the combined level of POPs or with highly chlorinated PCB congeners. Among these, cholesteryl ester transfer protein and phospholipid transfer protein, as well as the inflammatory marker serum amyloid A, were found. The serum paraoxonase/arylesterase 1 activity was inversely associated with POPs. Pathway analysis demonstrated that up- regulated proteinswere associatedwith biological processes involving lipoproteinmetabolism, while down- regulated proteinswere associatedwith processes such as negative regulation of proteinases, acute phase response, platelet degranulation, and complement activation. These results indicate an association between POP levels, especially highly chlorinated PCBs, and HDL protein alterations that may result in a less functional particle. Further studies are needed to determine causality and the importance of other environmental factors. Nevertheless, this study provides a first insight into a possible link between exposure to POPs and risk of CVD.
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