| 1. |
- Hedström, Brita, et al.
(författare)
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Visby Innerstad : En användningsplan
- 1973
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Rapport (populärvet., debatt m.m.)abstract
- Sedan lång tid föreligger i stort sett enighet om att bevara innerstadens bebyggelse och att anpassa eventuella nytillskott till det redan bestående. Med den inställningen har förändringsprocessen både dämpats och mildrats men ändå inte bragts att avstanna. Förändringar sker ständigt om det också huvudsakligen i smått: de många synbart så anspråkslösa byggnadsåtgärderna adderar efterhand ihop sig till något större och mer genomgripande. Långsamt, nästan omärkligt, ändrar innerstaden sitt ansikte.Ändå är det inte själva husen som förändrats mest utan användningen av dem. Ur funktionell synpunkt har 1950 - och 60-talen har varit något av en omstörtning i innerstadens historia: den har förlorat nästan hälften av de boende, en stor del av detaljhandeln och praktiskt taget helt sin gamla roll som skolcentrum. I gengäld har ytterstaden vuxit ut till ett sammanhängande kilometerbrett bälte. Till stor del av denna funktionella förändring en följd av beslutet att bevara innerstadens bebyggelse. Vad som inte fått plats inom den gamla ramen har etablerats utandör den.Föreliggande arbete vill ge en översiktlig bild av förändringsförloppen, sedda i ett långt tidsperspektiv men med tonvikt på dagsläget. Bebyggelsen tas upp till utförlig granskning men också användningen av den. Det är just samspelet mellan husen och de funtkioner, de fyller, som kan sägas utgöra bokens huvudtema. I de flesta fall är detta sammanhang hus-användning alldeles konfliktfritt och föranleder därför inte heller någon diskussion. Vad som behandlas är de relativt få problematiska fallen, hus som borde rustas upp för att fylla sin uppgift, hus som är olämpligt nyttjade eller inte använda alls. En serie sådana fall tas upp till systematisk genomgång; samtidigt berörs också de trafik - och miljömässiga konsekvenserna. Bokens syfte är alltså klart: den ger ett underlag av fakta för arbetet med att jämka samman byggnader och användningsformer. I den meningen kan skriften kallas en anvädningsplan för Visby innanför murarna.Arkitekturskolanas arbete har bedrivitis parallellt med den kommunala Innerstadskommitténs verksamhet. Något organiserat samarbete har inte förekommit med de informella kontakterna har varit både täta och goda. Att likheterna mellan Innerstadskommittén och Arkitekturskolans slutsatser blivit så pass stora, kan tillskrivas en gemensam helhetssyn.En av Arkitekturskolans elever, arkitekt Lars-Ingvar Larsson, har tidigare självständigt genomfört en undersökning av förändringar i innerstaden 1945-70- Denna studie publicerats separat och bör uppfattas som ett komplement till den hör föreliggande.Förutom de i innehållsförteckningen nämnda har ytterligare några aktivt medverkat i arbetet. Studiet av trafikfrågorna i innerstaden, i hamnen och öster om ringmuren leddes av Åke Claesson, I fältstudier och diskussioner medverkande Göran Månsson.Arkitekturskolan har fått god hjälp av ett antal initierade personer i Visby. Särskild tacksamhet är vi skyldiga byggnadsnämnden ordförande Henning Jacobson, kommunalrådet C B Stenström, stadsarkitekten Måns Hagbergm f. länsbostadsdorektören Åke Malmberg och landsantikvarien Gunnar Svahnström. I boken publiceringskostnaderna har ekonomiskt bidrag lämnats av Gotlands kommun och Riksantikvarieämbetet.Boken har redigerats av Sture Balgård och Ann Mari Westerlind med hjälp av Henrik O Andersson, Bo Ek, Göran Lindahl, Fredrik von Platen, John Sjöström Gunnar Westerlind och Hans Wetterfors.Skeppsholmen, Stockholm, sommaren 1973.Arkitekturskolans lärare och elever.
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| 2. |
- 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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| 3. |
- Arvidsson McShane, Staffan, 1990-, et al.
(författare)
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CPSign : Conformal Prediction for Cheminformatics Modeling
- 2024
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Ingår i: Journal of Cheminformatics. - : BioMed Central (BMC). - 1758-2946. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptors are also supported. The main modeling methodology is support vector machines (SVMs), but additional modeling methods are supported via an extension mechanism, e.g. DeepLearning4J models. We also describe features for visualizing results from conformal models including calibration and efficiency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approaches including random forest, and a directed message-passing neural network. The results show that CPSign produces robust predictive performance with comparative predictive efficiency, with superior runtime and lower hardware requirements compared to neural network based models. CPSign has been used in several studies and is in production-use in multiple organizations. The ability to work directly with chemical input files, perform descriptor calculation and modeling with SVM in the conformal prediction framework, with a single software package having a low footprint and fast execution time makes CPSign a convenient and yet flexible package for training, deploying, and predicting on chemical data. CPSign can be downloaded from GitHub at https://github.com/arosbio/cpsign.
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| 4. |
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| 5. |
- Eklund, Martin, et al.
(författare)
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Benchmarking Variable Selection in QSAR
- 2012
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Ingår i: Molecular Informatics. - : Wiley. - 1868-1743. ; 31:2, s. 173-179
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Tidskriftsartikel (refereegranskat)abstract
- Variable selection is important in QSAR modeling since it can improve model performance and transparency, as well as reduce the computational cost of model fitting and predictions. Which variable selection methods that perform well in QSAR settings is largely unknown. To address this question we, in a total of 1728 benchmarking experiments, rigorously investigated how eight variable selection methods affect the predictive performance and transparency of random forest models fitted to seven QSAR datasets covering different endpoints, descriptors sets, types of response variables, and number of chemical compounds. The results show that univariate variable selection methods are suboptimal and that the number of variables in the benchmarked datasets can be reduced with about 60?% without significant loss in model performance when using multivariate adaptive regression splines MARS and forward selection.
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| 6. |
- Eklund, Martin, et al.
(författare)
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Choosing Feature Selection and Learning Algorithms in QSAR
- 2014
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Ingår i: J CHEM INF MODEL. - Washington DC : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 54:3, s. 837-843
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Tidskriftsartikel (refereegranskat)abstract
- Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.
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| 7. |
- 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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| 8. |
- Helgee, Ernst Ahlberg, et al.
(författare)
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Evaluation of quantitative structure-activity relationship modeling strategies : local and global models
- 2010
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 50:4, s. 677-689
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Tidskriftsartikel (refereegranskat)abstract
- A thorough comparison between different QSAR modeling strategies is presented. The comparison is conducted for local versus global modeling strategies, risk assessment, and computational cost. The strategies are implemented using random forests, support vector machines, and partial least squares. Results are presented for simulated data, as well as for real data, generally indicating that a global modeling strategy is preferred over a local strategy. Furthermore, the results also show that there is an pronounced risk and a comparatively high computational cost when using the local modeling strategies.
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| 9. |
- McShane, Staffan Arvidsson, et al.
(författare)
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CPSign - Conformal Prediction for Cheminformatics Modeling
- 2024
-
Ingår i: Journal of Cheminformatics. - : BioMed Central (BMC). - 1758-2946. ; 16:1
-
Tidskriftsartikel (refereegranskat)abstract
- Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputsof machine learning models and producing valid prediction intervals. We here present the open source softwareCPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classifcation and regression, and probabilistic predictionwith the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptorsare also supported. The main modeling methodology is support vector machines (SVMs), but additional modelingmethods are supported via an extension mechanism, e.g. DeepLearning4J models. We also describe features for visualizing results from conformal models including calibration and efciency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approachesincluding random forest, and a directed message-passing neural network. The results show that CPSign producesrobust predictive performance with comparative predictive efciency, with superior runtime and lower hardwarerequirements compared to neural network based models. CPSign has been used in several studies and is in production-use in multiple organizations. The ability to work directly with chemical input fles, perform descriptor calculationand modeling with SVM in the conformal prediction framework, with a single software package having a low footprint and fast execution time makes CPSign a convenient and yet fexible package for training, deploying, and predicting on chemical data. CPSign can be downloaded from GitHub at https://github.com/arosbio/cpsign.Scientifc contribution CPSign provides a single software that allows users to perform data preprocessing, modeling and make predictionsdirectly on chemical structures, using conformal and probabilistic prediction. Building and evaluating new modelscan be achieved at a high abstraction level, without sacrifcing fexibility and predictive performance—showcasedwith a method evaluation against contemporary modeling approaches, where CPSign performs on par with a stateof-the-art deep learning based model.
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| 10. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project
- 2019
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Ingår i: Mutagenesis. - : Oxford University Press. - 0267-8357 .- 1464-3804. ; 34:1, s. 33-40
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Tidskriftsartikel (refereegranskat)abstract
- Valid and predictive models for classifying Ames mutagenicity have been developed using conformal prediction. The models are Random Forest models using signature molecular descriptors. The investigation indicates, on excluding not-strongly mutagenic compounds (class B), that the validity for mutagenic compounds is increased for the predictions based on both public and the Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) data while less so when using only the latter data source. The former models only result in valid predictions for the majority, non-mutagenic, class whereas the latter models are valid for both classes, i.e. mutagenic and non-mutagenic compounds. These results demonstrate the importance of data consistency manifested through the superior predictive quality and validity of the models based only on DGM/NIHS generated data compared to a combination of this data with public data sources.
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