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pattern recognition in bioinformatics 1

丛书名： Lecture Notes in Computer Science

1000年

作者： Marco Loog Marcel J. T. Reinders Dick Ridder Lodewyk Wessels

ISBN: (数字)9783642248559

ISBN: (纸本)9783642248542

This book constitutes the refereed proceedings of the 6th International Conference on pattern recognition in bioinformatics, PRIB 2011, held in Delft, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers cover the wide range of possible applications of bioinformatics in pattern recognition: novel algorithms to handle traditional pattern recognition problems such as (bi)clustering, classification and feature selection; applications of (novel) pattern recognition techniques to infer and analyze biological networks and studies on specific problems such as biological image analysis and the relation between sequence and structure. They are organized in the following topical sections: clustering, biomarker selection and classification, network inference and analysis, image analysis, and sequence, structure, and interactions.

关键词： Computational Biology/bioinformatics Health Informatics pattern recognition Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity Artificial Intelligence

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Irregular measurement times in estimating time-varying treatment effects: Categorizing biases and comparing adjustment methods

arXiv

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arXiv 2025年

作者： Kant, Wouter M.R. Krijthe, Jesse H. Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Pattern recognition & Bioinformatics Group EEMCS Delft University of Technology Delft Netherlands

To estimate the causal effect of treatments that vary over time from observational data, one must adjust for time-varying confounding. A common procedure to address confounding is the use of inverse probability of treatment weighting methods. However, the timing of covariate measurements is often irregular, which may introduce additional confounding bias as well as selection bias into the causal effect estimate. Two reweighting methods have been proposed to adjust for these biases: time-as-confounder and reweighting by measurement time. However, it is currently not well understood in which situations these irregularly timed measurements induce bias, and how the available reweighting methods compare to each other in different situations. In this work, we provide a complete inventarization of all possible backdoor paths through which bias is induced. Based on these paths, we distinguish three categories of confounding bias by measurement time: direct confounding (DC), confounding through measured variables (CMV), and confounding through unmeasured variables (CUV). These categories differ in the assumptions and reweighting methods necessary to adjust for bias and may occur simultaneously with selection bias. Through simulation studies, we illustrate: 1. Reweighting by measurement time may be used to adjust for selection bias and confounding through measured variables;2. Time-as-confounder may be used to adjust for all categories of confounding bias, but not selection bias;3. In some cases, the use of a combination of both techniques may be used to adjust for both confounding and selection bias. We finally apply the categorization and reweighting methods on the pre-DIVA1 data set. Adjusting for measurement times is crucial in order to avoid bias, and the categorization of biases and techniques that we introduce may help researchers to choose the appropriate analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

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Technologies Supporting Self-Reflection on Social Interactions: A Systematic Review 25

Technologies Supporting Self-Reflection on Social Interactio...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Hao, Chenxu Matej Hrkalovic, Tiffany Balliet, Daniel Hung, Hayley Dudzik, Bernd Delft University of Technology Delft Netherlands Jheronimus Academy of Data Science 's-Hertogenbosch Netherlands Tilburg University Tilburg Netherlands Vrije Universiteit Amsterdam Amsterdam Netherlands Intelligent Systems Delft University of Technology Delft Netherlands Intelligent Systems/Pattern Recognition and Bioinformatics Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713064

As intelligent technology and applications have become an integral part of nearly all aspects of people's daily lives, many intelligent systems have been designed to help people navigate the complex space of social interactions. One prominent strategy for such intelligent support is providing meaningful Ad Hoc Interventions (ADI), e.g., through timely notifications. An alternative is Technology-Supported Reflection (TSR), e.g., by offering information about activities in one's past for personal insights. In contrast to straight-up interventions, the aim of the latter strategy is not to directly augment human skills but instead support learning and personal growth over time. However, while TSR has seen widespread interest in applications in some areas, such as physical fitness and mental health, its use for improving human social interactions has not yet been systematically explored. Concretely, it is currently unclear 1) what forms of self-reflection systems intend to support, 2) how their different technological components (e.g., data collection, information integration) are involved in providing support, and 3) what common limitations and design challenges they face. In this article, we present the results of a systematic literature review focusing on these questions to provide a structured foundation for targeted research. Concretely, we identified and analysed a collection of 23 relevant papers, each describing a system deploying TSR to support humans with elements of social *** constructed a framework with a set of features to comprehensively describe and analyze the systems that support self-reflection, including their application domains, how they fit into the existing design framework, how they facilitate learning through reflection, how adaptive they are to individual users, and how they were evaluated. Finally, we propose a direction for designing systems that support individual's social interactions through self-reflection in an adaptive manne

关键词： Human computer interaction

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Comparative validation of handheld fractional exhaled nitric oxide measurements

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Expert Review of Medical Devices 2025年

作者： van Deelen, Sanne Tramper-Stranders, Gerdien A. Hendriks, Rudi W. Reinders, Marcel J. T. Braunstahl, Gert-Jan Department of pulmonology Franciscus Gasthuis & Vlietland Rotterdam Netherlands Department of pulmonology Erasmus MC Rotterdam Netherlands Pattern recognition & bioinformatics Delft University of Technology Delft Netherlands Department of pediatrics Franciscus Gasthuis & Vlietland Rotterdam Netherlands

Background: Fractional exhaled nitric oxide (FeNO) is a noninvasive method to determine the degree of airway inflammation. Handheld devices such as the Vivatmo Me are used for home monitoring. Differences were found between the Vivatmo Me and standard measurements with the NIOX VERO. Therefore, we aimed to determine the accuracy of the Vivatmo Me for FeNO measurements. Methods: Adult patients with an appointment for FeNO-measurement according to regular care, were invited to perform the FeNO measurement with both devices. From these measurements the FeNO values were compared, and the device user-friendliness was determined. Results: One hundred and sixty-four patients were included. The number of attempts needed for a successful measurement and the failure rate were higher with the Vivatmo Me. Although the measurements were highly correlated, a significant difference (p © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Asthma asthma-COPD-overlap syndrome chronic obstructive pulmonary disease fractional exhaled nitric oxide handheld devices

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Robust Gram embeddings

Robust Gram embeddings

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2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Kekeç, Taygun Tax, D.M.J. Pattern Recognition and Bioinformatics Laboratory Delft University of Technology Delft2628CD Netherlands

ISBN: (纸本)9781945626258

Word embedding models learn vectorial word representations that can be used in a variety of NLP applications. When training data is scarce, these models risk losing their generalization abilities due to the complexity of the models and the overfitting to finite data. We propose a regularized embedding formulation, called Robust Gram (RG), which penalizes overfitting by suppressing the disparity between target and context embeddings. Our experimental analysis shows that the RG model trained on small datasets generalizes better compared to alternatives, is more robust to variations in the training set, and correlates well to human similarities in a set of word similarity tasks. © 2016 Association for Computational Linguistics

关键词： Embeddings

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Barnes-Hut-SNE 1

Barnes-Hut-SNE

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1st International Conference on Learning Representations, ICLR 2013

作者： van der Maaten, Laurens Pattern Recognition and Bioinformatics Group Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

The paper presents an O(N log N)-implementation of t-SNE — an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data objects, and it uses a variant of the Barnes-Hut algorithm to approximate the forces between the corresponding points in the embedding. Our experiments show that the new algorithm, called Barnes-Hut-SNE, leads to substantial computational advantages over standard t-SNE, and that it makes it possible to learn embeddings of data sets with millions of objects. © 2013 International Conference on Learning Representations, ICLR. All rights reserved.

关键词： Embeddings

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Time-independent disease state identification defines distinct trajectories determined by localised vs systemic inflammation in patients with early rheumatoid arthritis

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Annals of the Rheumatic Diseases 2025年

作者： Steinz, Nils Maarseveen, Tjardo D. van den Akker, Erik B. Cope, Andrew P. Isaacs, John D. Winkler, Aaron R. Huizinga, Tom W. J. Abraham, Yann Knevel, Rachel Department of Rheumatology Leiden University Medical Centre Leiden Netherlands Leiden Computational Biology Centre Leiden University Medical Center Leiden Netherlands The Delft Bioinformatics Lab Pattern Recognition and Bioinformatics Delft University of Technology Delft Netherlands Centre for Rheumatic Diseases School of Immunology and Microbial Sciences Faculty of Life Sciences and Medicine King's College London London United Kingdom Musculoskeletal Unit Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Inflammation and Immunology Research Unit Pfizer Inc Cambridge MA United States Janssen Research and Development Beerse Belgium Translational and Clinical Research Institute Newcastle University Newcastle upon Tyne United Kingdom

Objectives: Patients with rheumatoid arthritis (RA) display different trajectories towards improvement of disease. We aimed to disentangle the heterogeneity of RA disease trajectories from the first clinical visit onwards using graph-based pseudotime analysis. Methods: We studied early patients with RA over 1.5 years in 2 data sets: Leiden (Netherlands), n = 1237, with 5017 visits, and Towards a Cure for Early Rheumatoid Arthritis (TACERA) (United Kingdom), n = 243, with 750 visits. We created a pipeline for time-independent clustering of clinical and haematologic features to identify disease states. Sequence analyses of these states defined the trajectories. We studied the predictability of the trajectories with baseline features. Results: Clustering identified 8 disease states with localised inflammation (joints) and systemic inflammation (erythrocyte sedimentation rate [ESR] or leucocytes) as the main discriminating factors. The disease state sequences consisted of 4 trajectories, which we independently replicated in TACERA: A, high ESR;B, rapid progression from many inflamed joints towards remission;C, high leucocytes;and D, many inflamed joints with poor prognosis. Systemic vs local inflammation patterns showed moderate predictability at baseline (sensitivity of 71% and precision of 0.73 for trajectory A, although lower precision of 0.52 for trajectory B), while other trajectories were less predictable. Trajectories C and D had strong resemblance with B at baseline but deteriorated into less favourable trajectories. Patients in trajectory A were more often female and on average older. The trajectories were not explained by time till disease-modifying antirheumatic drug, baseline disease activity, or symptom duration. The suboptimal trajectories coincided with worse patient-reported outcomes, even when the inflammation was mainly systemic. Conclusions: We identified 4 distinct trajectories in early RA, differentiating RA into localised vs systemic inflammation.

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Some practical issues in analyzing rare variants with NGS data

Some practical issues in analyzing rare variants with NGS da...

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The Global Conference of Chinese Geneticists(2012全球华人遗传学大会)

作者： Xueya Zhou Xuegong Zhang Pattern Recognition and Bioinformatics Tsinghua University

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Online cluster approximation via inequality

Online cluster approximation via inequality

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8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB

作者： Sinha, Shriprakash Technische Universiteit Delft Pattern Recognition and Bioinformatics Lab. EWI Bldg. Mekelweg 4 2628 CD Delft Netherlands

ISBN: (纸本)9783642334115

Given an example-feature set, representing the information context present in a dataset, is it possible to reconstruct the information context in the form of clusters to a certain degree of compromise, if the examples are processed randomly without repetition in a sequential online manner? A general transductive inductive learning strategy which uses constraint based multivariate Chebyshev inequality is proposed. Theoretical convergence in the reconstruction error to a finite value with increasing number of (a) processed examples and (b) generated clusters, respectively, is shown. Upper bounds for these error rates are also proved. Nonparametric estimates of these error from a sample of random sequences of example set, empirically point to a stable number of clusters. © 2012 IFIP International Federation for Information Processing.

关键词： Errors

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Latent variable models for predicting file dependencies in large-scale software development

Latent variable models for predicting file dependencies in l...

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24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Hu, Diane J. Van Der Maaten, Laurens Cho, Youngmin Saul, Lawrence K. Lerner, Sorin Dept. of Computer Science and Engineering University of California San Diego United States Pattern Recognition and Bioinformatics Lab. Delft University of Technology Netherlands

ISBN: (纸本)9781617823800

When software developers modify one or more files in a large code base, they must also identify and update other related files. Many file dependencies can be detected by mining the development history of the code base: in essence, groups of related files are revealed by the logs of previous workflows. From data of this form, we show how to detect dependent files by solving a problem in binary matrix completion. We explore different latent variable models (LVMs) for this problem, including Bernoulli mixture models, exponential family PCA, restricted Boltzmann machines, and fully Bayesian approaches. We evaluate these models on the development histories of three large, open-source software systems: Mozilla Firefox, Eclipse Subversive, and Gimp. In all of these applications, we find that LVMs improve the performance of related file prediction over current leading methods.

关键词： Bayesian networks

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