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  • 1.
    Boström, Henrik
    et al.
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Vesterberg, Anders
    Scania CV AB, Sweden.
    Predicting with Confidence from Survival Data2019In: Conformal and Probabilistic Prediction and Applications / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni Smirnov, 2019, p. 123-141Conference paper (Refereed)
    Abstract [en]

    Survival modeling concerns predicting whether or not an event will occur before or on a given point in time. In a recent study, the conformal prediction framework was applied to this task, and so-called conformal random survival forest was proposed. It was empirically shown that the error level of this model indeed is very close to the provided confidence level, and also that the error for predicting each outcome, i.e., event or no-event, can be controlled separately by employing a Mondrian approach. The addressed task concerned making predictions for time points as provided by the underlying distribution. However, if one instead is interested in making predictions with respect to some specific time point, the guarantee of the conformal prediction framework no longer holds, as one is effectively considering a sample from another distribution than from which the calibration instances have been drawn. In this study, we propose a modification of the approach for specific time points, which transforms the problem into a binary classification task, thereby allowing the error level to be controlled. The latter is demonstrated by an empirical investigation using both a collection of publicly available datasets and two in-house datasets from a truck manufacturing company.

  • 2.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Sönströd, Cecilia
    Dept. of Information Technology, University of Borås, Sweden.
    Interpretable and Specialized Conformal Predictors2019In: Conformal and Probabilistic Prediction and Applications / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni Smirnov, 2019, p. 3-22Conference paper (Refereed)
    Abstract [en]

    In real-world scenarios, interpretable models are often required to explain predictions, and to allow for inspection and analysis of the model. The overall purpose of oracle coaching is to produce highly accurate, but interpretable, models optimized for a specific test set. Oracle coaching is applicable to the very common scenario where explanations and insights are needed for a specific batch of predictions, and the input vectors for this test set are available when building the predictive model. In this paper, oracle coaching is used for generating underlying classifiers for conformal prediction. The resulting conformal classifiers output valid label sets, i.e., the error rate on the test data is bounded by a preset significance level, as long as the labeled data used for calibration is exchangeable with the test set. Since validity is guaranteed for all conformal predictors, the key performance metric is efficiency, i.e., the size of the label sets, where smaller sets are more informative. The main contribution of this paper is the design of setups making sure that when oracle-coached decision trees, that per definition utilize knowledge about test data, are used as underlying models for conformal classifiers, the exchangeability between calibration and test data is maintained. Consequently, the resulting conformal classifiers retain the validity guarantees. In the experimentation, using a large number of publicly available data sets, the validity of the suggested setups is empirically demonstrated. Furthermore, the results show that the more accurate underlying models produced by oracle coaching also improved the efficiency of the corresponding conformal classifiers.

  • 3.
    Löfström, Tuwe
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Balkow, Jenny
    Univ Boras, Swedish Sch Text, Boras, Sweden.
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    A data-driven approach to online fitting services2018In: Data Science And Knowledge Engineering For Sensing Decision Support / [ed] Liu, J, Lu, J, Xu, Y, Martinez, L & Kerre, EE, World Scientific, 2018, Vol. 11, p. 1559-1566Conference paper (Refereed)
    Abstract [en]

    Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5 % of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8 %.

  • 4.
    Ulfenborg, Benjamin
    et al.
    Högskolan i Skövde, Institutionen för biovetenskap.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Améen, Caroline
    Takara Bio Europe AB, Gothenburg, Sweden.
    Åkesson, Karolina
    Takara Bio Europe AB, Gothenburg, Sweden.
    Andersson, Christian X.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Sartipy, Peter
    Högskolan i Skövde, Institutionen för biovetenskap.
    Synnergren, Jane
    Högskolan i Skövde, Institutionen för biovetenskap.
    A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 6, article id e0179613Article in journal (Refereed)
    Abstract [en]

    The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic-and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

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