| 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. |
- Johansson, Ulf, et al.
(författare)
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Evaluating Ensembles on QSAR Classification
- 2009
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Konferensbidrag (refereegranskat)abstract
- Novel, often quite technical algorithms, for ensembling artificial neural networks are constantly suggested. Naturally, when presenting a novel algorithm, the authors, at least implicitly, claim that their algorithm, in some aspect, represents the state-of-the-art. Obviously, the most important criterion is predictive performance, normally measured using either accuracy or area under the ROC-curve (AUC). This paper presents a study where the predictive performance of two widely acknowledged ensemble techniques; GASEN and NegBagg, is compared to more straightforward alternatives like bagging. The somewhat surprising result of the experimentation using, in total, 32 publicly available data sets from the medical domain, was that both GASEN and NegBagg were clearly outperformed by several of the straightforward techniques. One particularly striking result was that not applying the GASEN technique; i.e., ensembling all available networks instead of using the subset suggested by GASEN, turned out to produce more accurate ensembles.
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| 3. |
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| 4. |
- Johansson, Ulf, et al.
(författare)
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The Trade-Off between Accuracy and Comprehensibility for Predictive In Silico Modeling
- 2011
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Ingår i: Future Medicinal Chemistry. - : Future Science. - 1756-8919 .- 1756-8927. ; 3:6, s. 647-663
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Tidskriftsartikel (refereegranskat)abstract
- Background: Accuracy concerns the ability of a model to make correct predictions, while interpretability concerns to what degree the model allows for human understanding. Models exhibiting the former property are many times more complex and opaque, while interpretable models may lack the necessary accuracy. The trade-off between accuracy and interpretability for predictive in silico modeling is investigated. Method: A number of state-of-the-art methods for generating accurate models are compared with state-of-the-art methods for generating transparent models. Conclusion: Results on 16 biopharmaceutical classification tasks demonstrate that, although the opaque methods generally obtain higher accuracies than the transparent ones, one often only has to pay a quite limited penalty in terms of predictive performance when choosing an interpretable model.
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| 5. |
- Johansson, Ulf, et al.
(författare)
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Trade-off between accuracy and interpretability for predictive in silico modeling
- 2011
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Ingår i: Future Medicinal Chemistry. - 1756-8919 .- 1756-8927. ; 3:6, s. 647-663
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Tidskriftsartikel (refereegranskat)abstract
- Background: Accuracy concerns the ability of a model to make correct predictions, while interpretability concerns to what degree the model allows for human understanding. Models exhibiting the former property are many times more complex and opaque, while interpretable models may lack the necessary accuracy. The trade-off between accuracy and interpretability for predictive in silico modeling is investigated. Method: A number of state-of-the-art methods for generating accurate models are compared with state-of-the-art methods for generating transparent models. Conclusion: Results on 16 biopharmaceutical classification tasks demonstrate that, although the opaque methods generally obtain higher accuracies than the transparent ones, one often only has to pay a quite limited penalty in terms of predictive performance when choosing an interpretable model.
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| 6. |
- Johansson, Ulf, et al.
(författare)
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Using Feature Selection with Bagging and Rule Extraction in Drug Discovery
- 2010
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Konferensbidrag (refereegranskat)abstract
- This paper investigates different ways of combining feature selection with bagging and rule extraction in predictive modeling. Experiments on a large number of data sets from the medicinal chemistry domain, using standard algorithms implemented in theWeka data mining workbench, show that feature selection can lead to significantly improved predictive performance.When combining feature selection with bagging, employing the feature selection on each bootstrap obtains the best result.When using decision trees for rule extraction, the effect of feature selection can actually be detrimental, unless the transductive approach oracle coaching is also used. However, employing oracle coaching will lead to significantly improved performance, and the best results are obtainedwhen performing feature selection before training the opaque model. The overall conclusion is that it can make a substantial difference for the predictive performance exactly how feature selection is used in conjunction with other techniques.
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| 7. |
- Karlgren, Maria, et al.
(författare)
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Classification of Inhibitors of Hepatic Organic Anion Transporting Polypeptides (OATPs) : Influence of Protein Expression on Drug - Drug Interactions
- 2012
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Ingår i: Journal of Medicinal Chemistry. - : American Chemical Society (ACS). - 0022-2623 .- 1520-4804. ; 55:10, s. 4740-4763
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Tidskriftsartikel (refereegranskat)abstract
- The hepatic organic anion transporting polypeptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug-drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1, The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors.
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| 8. |
- 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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| 9. |
- Norinder, Ulf, et al.
(författare)
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Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction
- 2022
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Ingår i: Journal of Management Analytics. - : Informa UK Limited. - 2327-0012 .- 2327-0039. ; 9:1, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- In this investigation, we have shown that the combination of deep learning, including natural language processing, and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions, i.e. the model and the test set are from the same review category or cross-category predictions, i.e. using a model of another review category for predicting the test set. The similar results from in- and cross-category predictions indicate high degree of generalizability across product review categories. The investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures.
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| 10. |
- Sönströd, Cecilia, et al.
(författare)
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Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes
- 2008
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Ingår i: Proceedings of the Seventh International Conference on Machine Learning and Applications. - : IEEE. - 9780769534954 ; , s. 559-564
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Konferensbidrag (refereegranskat)abstract
- When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into interesting relationships in the domain. It is then desirable that machine learning techniques have the ability to obtain small and transparent models, where the user can control the tradeoff between accuracy, comprehensibility and coverage. In this study, three different decision list algorithms are evaluated on a data set concerning the interaction of molecules with a human gene that regulates heart functioning (hERG). The results show that decision list algorithms can obtain predictive performance not far from the state-of-the-art method random forests, but also that algorithms focusing on accuracy alone may produce complex decision lists that are very hard to interpret. The experiments also show that by sacrificing accuracy only to a limited degree, comprehensibility (measured as both model size and classification complexity) can be improved remarkably.
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