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99 Publications visible to you, out of a total of 99
Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria

Abstract (Expand)

Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due …prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns’ and families’ burden of false positives or over-treatment.

Authors: Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Vincent Heuveline, Stefan Kölker

Date Published: 1st Feb 2023

Publication Type: Journal

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment

Abstract

Not specified

Authors: Julian Schröter, Tal Dattner, Jennifer Hüllein, Alejandra Jayme, Vincent Heuveline, Georg F. Hoffmann, Stefan Kölker, Dominic Lenz, Thomas Opladen, Bernt Popp, Christian P. Schaaf, Christian Staufner, Steffen Syrbe, Sebastian Uhrig, Daniel Hübschmann, Heiko Brennenstuhl

Date Published: 2023

Publication Type: Journal

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning

Abstract (Expand)

Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method …. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.

Authors: Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach, Genoveva Burca, Vincent Heuveline

Date Published: 2023

Publication Type: Journal

Review of Deep Learning Methods for Individual Treatment Effect Estimation with Automatic Hyperparameter Optimization

Abstract (Expand)

<p>Abstract—Estimation of individual treatment effect (ITE) for different types of treatment is a common challenge in therapy assessments, clinical trials and diagnosis. Deep learning methods, …ing methods, namely representation based, adversarial, and variational, have shown promising potential in ITE estimation. However, it was unclear whether the hyperparameters of the originally proposed methods were well optimized for different benchmark datasets. To solve these problems, we created a public code library containing representation-based, adversarial, and variational methods written in TensorFlow. In order to have a broader collection of ITE estimation methods, we have also included neural network based meta-learners. The code library is made accessible for reproducibility and facilitating future works in the field of causal inference. Our results demonstrate that performance of most methods can be improved using automatic hyperparameter optimization. Additionally, we review the methods and compare the performance of the optimized models from our library on publicly available datasets. The potential of hyperparameter optimization may encourage researchers to focus on this aspect when creating new methods for inferring individual treatment effect.</p>

Authors: Andrei Sirazitdinov, Marcus Buchwald, Jürgen Hesser, Vincent Heuveline

Date Published: 6th Dec 2022

Publication Type: Misc

Colorectal cancer incidences in Lynch syndrome: a comparison of results from the prospective lynch syndrome database and the international mismatch repair consortium

Abstract (Expand)

Abstract Objective To compare colorectal cancer (CRC) incidences in carriers of pathogenic variants of the MMR genes in the PLSD and IMRC cohorts, of which only the former included mandatory colonoscopy …of which only the former included mandatory colonoscopy surveillance for all participants. Methods CRC incidences were calculated in an intervention group comprising a cohort of confirmed carriers of pathogenic or likely pathogenic variants in mismatch repair genes ( path_MMR) followed prospectively by the Prospective Lynch Syndrome Database (PLSD). All had colonoscopy surveillance, with polypectomy when polyps were identified. Comparison was made with a retrospective cohort reported by the International Mismatch Repair Consortium (IMRC). This comprised confirmed and inferred path_MMR carriers who were first- or second-degree relatives of Lynch syndrome probands. Results In the PLSD, 8,153 subjects had follow-up colonoscopy surveillance for a total of 67,604 years and 578 carriers had CRC diagnosed. Average cumulative incidences of CRC in path_MLH1 carriers at 70 years of age were 52% in males and 41% in females; for path_MSH2 50% and 39%; for path_MSH6 13% and 17% and for path_PMS2 11% and 8%. In contrast, in the IMRC cohort, corresponding cumulative incidences were 40% and 27%; 34% and 23%; 16% and 8% and 7% and 6%. Comparing just the European carriers in the two series gave similar findings. Numbers in the PLSD series did not allow comparisons of carriers from other continents separately. Cumulative incidences at 25 years were < 1% in all retrospective groups. Conclusions Prospectively observed CRC incidences (PLSD) in path_MLH1 and path_MSH2 carriers undergoing colonoscopy surveillance and polypectomy were higher than in the retrospective (IMRC) series, and were not reduced in path_MSH6 carriers. These findings were the opposite to those expected. CRC point incidence before 50 years of age was reduced in path_PMS2 carriers subjected to colonoscopy, but not significantly so.

Authors: Pål Møller, Toni Seppälä, James G. Dowty, Saskia Haupt, Mev Dominguez-Valentin, Lone Sunde, Inge Bernstein, Christoph Engel, Stefan Aretz, Maartje Nielsen, Gabriel Capella, Dafydd Gareth Evans, John Burn, Elke Holinski-Feder, Lucio Bertario, Bernardo Bonanni, Annika Lindblom, Zohar Levi, Finlay Macrae, Ingrid Winship, John-Paul Plazzer, Rolf Sijmons, Luigi Laghi, Adriana Della Valle, Karl Heinimann, Elizabeth Half, Francisco Lopez-Koestner, Karin Alvarez-Valenzuela, Rodney J. Scott, Lior Katz, Ido Laish, Elez Vainer, Carlos Alberto Vaccaro, Dirce Maria Carraro, Nathan Gluck, Naim Abu-Freha, Aine Stakelum, Rory Kennelly, Des Winter, Benedito Mauro Rossi, Marc Greenblatt, Mabel Bohorquez, Harsh Sheth, Maria Grazia Tibiletti, Leonardo S. Lino-Silva, Karoline Horisberger, Carmen Portenkirchner, Ivana Nascimento, Norma Teresa Rossi, Leandro Apolinário da Silva, Huw Thomas, Attila Zaránd, Jukka-Pekka Mecklin, Kirsi Pylvänäinen, Laura Renkonen-Sinisalo, Anna Lepisto, Päivi Peltomäki, Christina Therkildsen, Lars Joachim Lindberg, Ole Thorlacius-Ussing, Magnus von Knebel Doeberitz, Markus Loeffler, Nils Rahner, Verena Steinke-Lange, Wolff Schmiegel, Deepak Vangala, Claudia Perne, Robert Hüneburg, Aída Falcón de Vargas, Andrew Latchford, Anne-Marie Gerdes, Ann-Sofie Backman, Carmen Guillén-Ponce, Carrie Snyder, Charlotte K. Lautrup, David Amor, Edenir Palmero, Elena Stoffel, Floor Duijkers, Michael J. Hall, Heather Hampel, Heinric Williams, Henrik Okkels, Jan Lubiński, Jeanette Reece, Joanne Ngeow, Jose G. Guillem, Julie Arnold, Karin Wadt, Kevin Monahan, Leigha Senter, Lene J. Rasmussen, Liselotte P. van Hest, Luigi Ricciardiello, Maija R. J. Kohonen-Corish, Marjolijn J. L. Ligtenberg, Melissa Southey, Melyssa Aronson, Mohd N. Zahary, N. Jewel Samadder, Nicola Poplawski, Nicoline Hoogerbrugge, Patrick J. Morrison, Paul James, Grant Lee, Rakefet Chen-Shtoyerman, Ravindran Ankathil, Rish Pai, Robyn Ward, Susan Parry, Tadeusz Dębniak, Thomas John, Thomas van Overeem Hansen, Trinidad Caldés, Tatsuro Yamaguchi, Verónica Barca-Tierno, Pilar Garre, Giulia Martina Cavestro, Jürgen Weitz, Silke Redler, Reinhard Büttner, Vincent Heuveline, John L. Hopper, Aung Ko Win, Noralane Lindor, Steven Gallinger, Loïc Le Marchand, Polly A. Newcomb, Jane Figueiredo, Daniel D. Buchanan, Stephen N. Thibodeau, Sanne W. ten Broeke, Eivind Hovig, Sigve Nakken, Marta Pineda, Nuria Dueñas, Joan Brunet, Kate Green, Fiona Lalloo, Katie Newton, Emma J. Crosbie, Miriam Mints, Douglas Tjandra, Florencia Neffa, Patricia Esperon, Revital Kariv, Guy Rosner, Walter Hernán Pavicic, Pablo Kalfayan, Giovana Tardin Torrezan, Thiago Bassaneze, Claudia Martin, Gabriela Moslein, Aysel Ahadova, Matthias Kloor, Julian R. Sampson, Mark A. Jenkins

Date Published: 1st Dec 2022

Publication Type: Journal

A simple approach for detecting HLA‐A *02 alleles in archival formalin‐fixed paraffin‐embedded tissue samples and an application example for studying cancer immunoediting

Abstract

Not specified

Authors: Johannes Witt, Saskia Haupt, Aysel Ahadova, Lena Bohaumilitzky, Vera Fuchs, Alexej Ballhausen, Moritz Jakob Przybilla, Michael Jendrusch, Toni T. Seppälä, Daniel Fürst, Thomas Walle, Elena Busch, Georg Martin Haag, Robert Hüneburg, Jacob Nattermann, Magnus von Knebel Doeberitz, Vincent Heuveline, Matthias Kloor

Date Published: 25th Oct 2022

Publication Type: Journal

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning

Abstract (Expand)

Abstract Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, …into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition. Author Summary Bees, despite their small brains, possess a rich behavioural repertoire and show significant variations among individuals. In social bees this variability is key to the division of labour that maintains their complex social organizations, and has been linked to the maturation of specific brain areas as a result of development and foraging experience. This makes bees an ideal model for understanding insect cognitive functions and the neural mechanisms that underlie them. However, due to the scarcity of comparative data, the relationship between brain neuro-architecture and behavioural variance remains unclear. To address this problem, we developed an AI-based approach for automated analysis of brain images and analysed an unprecedentedly large dataset of honey bee and bumblebee brains. Through this process, we were able to identify previously undescribed anatomical features that correlate with known behaviours, supporting recent evidence of lateralized behaviour in foraging and pollination. Our method is open-source, easily accessible online, user-friendly, fast, accurate, and robust to different species, enabling large-scale comparative analyses across the animal kingdom. This includes investigating the impact of external stressors such as environmental pollution and climate change on cognitive development, helping us understand the mechanisms underlying the cognitive abilities of animals and the implications for their survival and adaptation.

Authors: Philipp D. Lösel, Coline Monchanin, Renaud Lebrun, Alejandra Jayme, Jacob Relle, Jean-Marc Devaud, Vincent Heuveline, Mathieu Lihoreau

Date Published: 17th Oct 2022

Publication Type: Journal

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