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2,474 results on '"Fritsche, P."'

1. A Doubly Robust Method to Counteract Outcome-Dependent Selection Bias in Multi-Cohort EHR Studies

Author

Kundu, Ritoban, Shi, Xu, Salvatore, Maxwell, Fritsche, Lars G., and Mukherjee, Bhramar

Subjects
Statistics - Methodology
Abstract

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics or centers with varying selection mechanisms that may depend on the disease or outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to five times lower relative bias and three times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates more closely aligned with national estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure is not available in the external probability sample.

Published
2024

2. Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT

Author

Chakravarty, Arunava, Emre, Taha, Lachinov, Dmitrii, Rivail, Antoine, Scholl, Hendrik, Fritsche, Lars, Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, and Bogunović, Hrvoje

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Predicting future disease progression risk from medical images is challenging due to patient heterogeneity, and subtle or unknown imaging biomarkers. Moreover, deep learning (DL) methods for survival analysis are susceptible to image domain shifts across scanners. We tackle these issues in the task of predicting late dry Age-related Macular Degeneration (dAMD) onset from retinal OCT scans. We propose a novel DL method for survival prediction to jointly predict from the current scan a risk score, inversely related to time-to-conversion, and the probability of conversion within a time interval $t$. It uses a family of parallel hyperplanes generated by parameterizing the bias term as a function of $t$. In addition, we develop unsupervised losses based on intra-subject image pairs to ensure that risk scores increase over time and that future conversion predictions are consistent with AMD stage prediction using actual scans of future visits. Such losses enable data-efficient fine-tuning of the trained model on new unlabeled datasets acquired with a different scanner. Extensive evaluation on two large datasets acquired with different scanners resulted in a mean AUROCs of 0.82 for Dataset-1 and 0.83 for Dataset-2, across prediction intervals of 6,12 and 24 months., Comment: accepted in MICCAI 2024

Published
2024

3. Many-Body Dephasing by Hole Motion in a Spin-Orbit-Coupled Mott Insulator

Author

Ghermaoui, A., Aguilera, M. Bosch, Bouganne, R., Vatré, R., Fritsche, I., Beugnon, J., and Gerbier, F.

Subjects
Condensed Matter - Quantum Gases
Abstract

We use Ramsey interferometry to study spin dynamics in the strongly interacting regime of spin-orbit-coupled quantum gases in one-dimensional optical lattices. We observe an intrinsic many-body dephasing mechanism immune to spin-echo in two-component Mott insulators. We ascribe the dephasing to the motion of hole-like defects in an otherwise inert Mott insulator, the spinless nature of the holes explaining the ineffectiveness of spin echo to restore it. We show that a model of spin-orbit-coupled hardcore bosons can explain quantitatively our experimental observations., Comment: Supplementary Material available as ancillary file

Published
2024

4. Pan‐Cancer Survival Impact of Immune Checkpoint Inhibitors in a National Healthcare System

Author

Miller, Sean R, Schipper, Matthew, Fritsche, Lars G, Jiang, Ralph, Strohbehn, Garth, Ötleş, Erkin, McMahon, Benjamin H, Crivelli, Silvia, Zamora‐Resendiz, Rafael, Ramnath, Nithya, Yoo, Shinjae, Dai, Xin, Sankar, Kamya, Edwards, Donna M, Allen, Steven G, Green, Michael D, and Bryant, Alex K

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Cancer, 7.1 Individual care needs, Good Health and Well Being, Humans, Immune Checkpoint Inhibitors, Male, Female, Aged, Neoplasms, Middle Aged, United States, United States Department of Veterans Affairs, Aged, 80 and over, check point control, clinical cancer research, clinical observations, immune checkpoint inhibitors, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract

BackgroundThe cumulative, health system-wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real-world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system-wide survival impact of ICIs.MethodsWe identified all patients receiving PD-1/PD-L1 or CTLA-4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non-ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer-related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life-years gained system-wide were calculated from the difference in adjusted 5-year restricted mean survival times.ResultsThere were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54-0.58, p

Published
2024

8. Carrageenan and insulin resistance in humans: a randomised double-blind cross-over trial

Author

Wagner, Robert, Buettner, Janine, Heni, Martin, Fritsche, Louise, Kullmann, Stephanie, Wagmüller, Moritz, Peter, Andreas, Preissl, Hubert, Machann, Jürgen, Jumpertz von Schwartzenberg, Reiner, Birkenfeld, Andreas L., Pape, Ulrich-Frank, van Hall, Gerrit, Plomgaard, Peter, Häring, Hans-Ulrich, Fritsche, Andreas, Thompson, Kelsey N., Klein, Reinhild, and Stefan, Norbert

Published
2024
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9. Using weakest application conditions to rank graph transformations for graph repair

Author

Fritsche, Lars, Lauer, Alexander, Kratz, Maximilian, Schürr, Andy, and Taentzer, Gabriele

Subjects
Computer Science - Software Engineering, 68R10, D.2.4
Abstract

When using graphs and graph transformations to model systems, consistency is an important concern. While consistency has primarily been viewed as a binary property, i.e., a graph is consistent or inconsistent with respect to a set of constraints, recent work has presented an approach to consistency as a graduated property. This allows living with inconsistencies for a while and repairing them when necessary. For repairing inconsistencies in a graph, we use graph transformation rules with so-called {\em impairment-indicating and repair-indicating application conditions} to understand how much repair gain certain rule applications would bring. Both types of conditions can be derived from given graph constraints. Our main theorem shows that the difference between the number of actual constraint violations before and after a graph transformation step can be characterized by the difference between the numbers of violated impairment-indicating and repair-indicating application conditions. This theory forms the basis for algorithms with look-ahead that rank graph transformations according to their potential for graph repair. An evaluation shows that graph repair can be well supported by rules with these new types of application conditions in terms of effectiveness and scalability., Comment: 46 pages, 24 Figures, new, mor efficient method for constructing application conditions, theoretical comparison to other concepts of consistency, extended evaluation

Published
2024

10. Regularization-Based Efficient Continual Learning in Deep State-Space Models

Author

Zhang, Yuanhang, Lin, Zhidi, Sun, Yiyong, Yin, Feng, and Fritsche, Carsten

Subjects
Computer Science - Machine Learning
Abstract

Deep state-space models (DSSMs) have gained popularity in recent years due to their potent modeling capacity for dynamic systems. However, existing DSSM works are limited to single-task modeling, which requires retraining with historical task data upon revisiting a forepassed task. To address this limitation, we propose continual learning DSSMs (CLDSSMs), which are capable of adapting to evolving tasks without catastrophic forgetting. Our proposed CLDSSMs integrate mainstream regularization-based continual learning (CL) methods, ensuring efficient updates with constant computational and memory costs for modeling multiple dynamic systems. We also conduct a comprehensive cost analysis of each CL method applied to the respective CLDSSMs, and demonstrate the efficacy of CLDSSMs through experiments on real-world datasets. The results corroborate that while various competing CL methods exhibit different merits, the proposed CLDSSMs consistently outperform traditional DSSMs in terms of effectively addressing catastrophic forgetting, enabling swift and accurate parameter transfer to new tasks., Comment: 7 pages, 14 figures

Published
2024

11. Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances

Author

Bryant, Alex K, Zamora‐Resendiz, Rafael, Dai, Xin, Morrow, Destinee, Lin, Yuewei, Jungles, Kassidy M, Rae, James M, Tate, Akshay, Pearson, Ashley N, Jiang, Ralph, Fritsche, Lars, Lawrence, Theodore S, Zou, Weiping, Schipper, Matthew, Ramnath, Nithya, Yoo, Shinjae, Crivelli, Silvia, and Green, Michael D

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Clinical Research, Cancer, Bioengineering, Humans, Artificial Intelligence, Medical Oncology, Neoplasms, Cancer Outcomes Research, Large language models, Observational Data, prognostic factor, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract

PurposeReal world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.MethodsWe performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.ResultsArtificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.ConclusionsDespite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

Published
2024

13. Advanced Model Consistency Restoration with Higher-Order Short-Cut Rules

Author

Fritsche, Lars, Kosiol, Jens, Lauer, Alexander, Möller, Adrian, and Schürr, Andy

Subjects
Computer Science - Software Engineering
Abstract

Sequential model synchronisation is the task of propagating changes from one model to another correlated one to restore consistency. It is challenging to perform this propagation in a least-changing way that avoids unnecessary deletions (which might cause information loss). From a theoretical point of view, so-called short-cut (SC) rules have been developed that enable provably correct propagation of changes while avoiding information loss. However, to be able to react to every possible change, an infinite set of such rules might be necessary. Practically, only small sets of pre-computed basic SC rules have been used, severely restricting the kind of changes that can be propagated without loss of information. In this work, we close that gap by developing an approach to compute more complex required SC rules on-the-fly during synchronisation. These higher-order SC rules allow us to cope with more complex scenarios when multiple changes must be handled in one step. We implemented our approach in the model transformation tool eMoflon. An evaluation shows that the overhead of computing higher-order SC rules on-the-fly is tolerable and at times even improves the overall performance. Above that, completely new scenarios can be dealt with without the loss of information.

Published
2023
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15. Sex-specific molecular signature of mouse podocytes in homeostasis and in response to pharmacological challenge with rapamycin

Author

Al-Diab, Ola, Sünkel, Christin, Blanc, Eric, Catar, Rusan Ali, Ashraf, Muhammad Imtiaz, Zhao, Hongfan, Wang, Pinchao, Rinschen, Markus M., Fritsche-Guenther, Raphaela, Grahammer, Florian, Bachmann, Sebastian, Beule, Dieter, Kirwan, Jennifer A., Rajewsky, Nikolaus, Huber, Tobias B., Gürgen, Dennis, and Kusch, Angelika

Published
2024
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21. Gut microbiota dysbiosis is associated with altered tryptophan metabolism and dysregulated inflammatory response in COVID-19

Author

Essex, Morgan, Millet Pascual-Leone, Belén, Löber, Ulrike, Kuhring, Mathias, Zhang, Bowen, Brüning, Ulrike, Fritsche-Guenther, Raphaela, Krzanowski, Marta, Fiocca Vernengo, Facundo, Brumhard, Sophia, Röwekamp, Ivo, Anna Bielecka, Agata, Lesker, Till Robin, Wyler, Emanuel, Landthaler, Markus, Mantei, Andrej, Meisel, Christian, Caesar, Sandra, Thibeault, Charlotte, Corman, Victor M., Marko, Lajos, Suttorp, Norbert, Strowig, Till, Kurth, Florian, Sander, Leif E., Li, Yang, Kirwan, Jennifer A., Forslund, Sofia K., and Opitz, Bastian

Published
2024
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24. A genome-wide association study provides insights into the genetic etiology of 57 essential and non-essential trace elements in humans

Author

Moksnes, Marta R., Hansen, Ailin F., Wolford, Brooke N., Thomas, Laurent F., Rasheed, Humaira, Simić, Anica, Bhatta, Laxmi, Brantsæter, Anne Lise, Surakka, Ida, Zhou, Wei, Magnus, Per, Njølstad, Pål R., Andreassen, Ole A., Syversen, Tore, Zheng, Jie, Fritsche, Lars G., Evans, David M., Warrington, Nicole M., Nøst, Therese H., Åsvold, Bjørn Olav, Flaten, Trond Peder, Willer, Cristen J., Hveem, Kristian, and Brumpton, Ben M.

Published
2024
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25. Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung

Author

Dörner, Patrick J., Anandakumar, Harithaa, Röwekamp, Ivo, Fiocca Vernengo, Facundo, Millet Pascual-Leone, Belén, Krzanowski, Marta, Sellmaier, Josua, Brüning, Ulrike, Fritsche-Guenther, Raphaela, Pfannkuch, Lennart, Kurth, Florian, Milek, Miha, Igbokwe, Vanessa, Löber, Ulrike, Gutbier, Birgitt, Holstein, Markus, Heinz, Gitta Anne, Mashreghi, Mir-Farzin, Schulte, Leon N., Klatt, Ann-Brit, Caesar, Sandra, Wienhold, Sandra-Maria, Offermanns, Stefan, Mack, Matthias, Witzenrath, Martin, Jordan, Stefan, Beule, Dieter, Kirwan, Jennifer A., Forslund, Sofia K., Wilck, Nicola, Bartolomaeus, Hendrik, Heimesaat, Markus M., and Opitz, Bastian

Published
2024
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35. Understanding the Unique Perspectives of Caretakers of Transition-Aged People with Disabilities: A Case Study to Explore Competitive Integrated Employment Rates for Students in a Job Training Coordination Program

Author

Bambi Jill Fritsche

Abstract

This qualitative case study is situated in the area of educational leadership. It aims to explore possible strategies for increasing community-integrated employment for graduates of a Job Training Coordination Program at OakVeil High School. It is important to consider the lived experiences, hopes, fears, and barriers experienced from a caregiver's perspective so that a Job Training Coordinator can better assist families during the transition from school to a career. There is a gap in employment between people with disabilities and their non-disabled peers. Caregivers' perspectives on participation in community-integrated employment for their children of transition age with disabilities were examined through the investigation of three sub research questions. Open-ended interviews and record reviews were gathered and analyzed through coding. Data analysis resulted in the following themes: Employer and Person-Centered Values for CIE, JTC as CIE Support, Alternate Settings from CIE, and Family Support Services. Key findings were interpreted to consider safety, transportation, self-determination, caregiver support, and caregiver involvement. This study demonstrates how policies, safety at the workplace, and transportation are common barriers to community-integrated employment from the caregiver's perspective. Suggestions are offered to help Job Training Coordinators, special education teachers, job coaches, employers, and caregivers improve the rate of Community-integrated employment. This study also suggests that barriers, support services, personal preference, civil rights, and familial needs are interwoven and recognition of this is necessary for successful employment in the community for people with disabilities. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2024

38. Mediated interactions between Fermi polarons and the role of impurity quantum statistics

Author

Baroni, Cosetta, Huang, Bo, Fritsche, Isabella, Dobler, Erich, Anich, Gregor, Kirilov, Emil, Grimm, Rudolf, Bastarrachea-Magnani, Miguel A., Massignan, Pietro, and Bruun, Georg

Subjects
Condensed Matter - Quantum Gases, Physics - Atomic Physics, Quantum Physics
Abstract

The notion of quasi-particles is essential for understanding the behaviour of complex many-body systems. A prototypical example of a quasi-particle, a polaron, is an impurity strongly interacting with a surrounding medium. Fermi polarons, created in a Fermi sea, provide a paradigmatic realization of this concept. As an inherent and important property such quasi-particles interact with each other via modulation of the medium. While quantum simulation experiments with ultracold atoms have significantly improved our understanding of individual polarons, the detection of their interactions has remained elusive in these systems. Here, we report the unambiguous observation of mediated interactions between Fermi polarons consisting of K impurities embedded in a Fermi sea of Li atoms. Our results confirm two landmark predictions of Landau's Fermi-liquid theory: the shift of the polaron energy due to mediated interactions, linear in the concentration of impurities, and its sign inversion with impurity quantum statistics. For weak to moderate interactions between the impurities and the medium, we find excellent agreement with the static (zero-momentum and energy) predictions of Fermi-liquid theory. For stronger impurity-medium interactions, we show that the observed behaviour at negative energies can be explained by a more refined many-body treatment including retardation and molecule formation

Published
2023
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39. Incorporating External Risk Information with the Cox Model under Population Heterogeneity: Applications to Trans-Ancestry Polygenic Hazard Scores

Author

Wang, Di, Ye, Wen, Zhu, Ji, Xu, Gongjun, Tang, Weijing, Zawistowski, Matthew, Fritsche, Lars G., and He, Kevin

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Polygenic hazard score (PHS) models designed for European ancestry (EUR) individuals provide ample information regarding survival risk discrimination. Incorporating such information can improve the performance of risk discrimination in an internal small-sized non-EUR cohort. However, given that external EUR-based model and internal individual-level data come from different populations, ignoring population heterogeneity can introduce substantial bias. In this paper, we develop a Kullback-Leibler-based Cox model (CoxKL) to integrate internal individual-level time-to-event data with external risk scores derived from published prediction models, accounting for population heterogeneity. Partial-likelihood-based KL information is utilized to measure the discrepancy between the external risk information and the internal data. We establish the asymptotic properties of the CoxKL estimator. Simulation studies show that the integration model by the proposed CoxKL method achieves improved estimation efficiency and prediction accuracy. We applied the proposed method to develop a trans-ancestry PHS model for prostate cancer and found that integrating a previously published EUR-based PHS with an internal genotype data of African ancestry (AFR) males yielded considerable improvement on the prostate cancer risk discrimination.

Published
2023

40. Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration

Author

Holland, Robbie, Leingang, Oliver, Holmes, Christopher, Anders, Philipp, Kaye, Rebecca, Riedl, Sophie, Paetzold, Johannes C., Ezhov, Ivan, Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Fritsche, Lars, Scholl, Hendrik P. N., Sivaprasad, Sobha, Lotery, Andrew J., Rueckert, Daniel, and Menten, Martin J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system., Comment: Submitted to MICCAI2023

Published
2023

41. Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake

Author

Williamson, Alice, Norris, Dougall M, Yin, Xianyong, Broadaway, K Alaine, Moxley, Anne H, Vadlamudi, Swarooparani, Wilson, Emma P, Jackson, Anne U, Ahuja, Vasudha, Andersen, Mette K, Arzumanyan, Zorayr, Bonnycastle, Lori L, Bornstein, Stefan R, Bretschneider, Maxi P, Buchanan, Thomas A, Chang, Yi-Cheng, Chuang, Lee-Ming, Chung, Ren-Hua, Clausen, Tine D, Damm, Peter, Delgado, Graciela E, de Mello, Vanessa D, Dupuis, Josée, Dwivedi, Om P, Erdos, Michael R, Silva, Lilian Fernandes, Frayling, Timothy M, Gieger, Christian, Goodarzi, Mark O, Guo, Xiuqing, Gustafsson, Stefan, Hakaste, Liisa, Hammar, Ulf, Hatem, Gad, Herrmann, Sandra, Højlund, Kurt, Horn, Katrin, Hsueh, Willa A, Hung, Yi-Jen, Hwu, Chii-Min, Jonsson, Anna, Kårhus, Line L, Kleber, Marcus E, Kovacs, Peter, Lakka, Timo A, Lauzon, Marie, Lee, I-Te, Lindgren, Cecilia M, Lindström, Jaana, Linneberg, Allan, Liu, Ching-Ti, Luan, Jian’an, Aly, Dina Mansour, Mathiesen, Elisabeth, Moissl, Angela P, Morris, Andrew P, Narisu, Narisu, Perakakis, Nikolaos, Peters, Annette, Prasad, Rashmi B, Rodionov, Roman N, Roll, Kathryn, Rundsten, Carsten F, Sarnowski, Chloé, Savonen, Kai, Scholz, Markus, Sharma, Sapna, Stinson, Sara E, Suleman, Sufyan, Tan, Jingyi, Taylor, Kent D, Uusitupa, Matti, Vistisen, Dorte, Witte, Daniel R, Walther, Romy, Wu, Peitao, Xiang, Anny H, Zethelius, Björn, Ahlqvist, Emma, Bergman, Richard N, Chen, Yii-Der Ida, Collins, Francis S, Fall, Tove, Florez, Jose C, Fritsche, Andreas, Grallert, Harald, Groop, Leif, Hansen, Torben, Koistinen, Heikki A, Komulainen, Pirjo, Laakso, Markku, Lind, Lars, Loeffler, Markus, März, Winfried, Meigs, James B, Raffel, Leslie J, Rauramaa, Rainer, Rotter, Jerome I, Schwarz, Peter EH, and Stumvoll, Michael

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Clinical Research, Diabetes, Human Genome, Obesity, Nutrition, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Metabolic and endocrine, Humans, Insulin, Genome-Wide Association Study, Insulin Resistance, Diabetes Mellitus, Type 2, Glucose, Blood Glucose, Meta-Analysis of Glucose and Insulin-related Traits Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P

Published
2023

43. Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Author

Holland, Robbie, Leingang, Oliver, Bogunović, Hrvoje, Riedl, Sophie, Fritsche, Lars, Prevost, Toby, Scholl, Hendrik P. N., Schmidt-Erfurth, Ursula, Sivaprasad, Sobha, Lotery, Andrew J., Rueckert, Daniel, and Menten, Martin J.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal OCT images of 7,912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining.

Published
2022

47. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants

Author

Wang, Anqi, Shen, Jiayi, Rodriguez, Alex A., Saunders, Edward J., Chen, Fei, Janivara, Rohini, Darst, Burcu F., Sheng, Xin, Xu, Yili, Chou, Alisha J., Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Plym, Anna, Sahimi, Ali, Hoffman, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figuerêdo, Jéssica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy P., Schleutker, Johanna, Tammela, Teuvo L. J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher T., Cho, Kelly, Mcmahon, Benjamin H., Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Borge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Røder, Andreas, Stroomberg, Hein V., Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith A., Tilly, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael B., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Jr., Ian M., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet L., Ostrander, Elaine A., Koutros, Stella, Beane Freeman, Laura E., Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan-Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M. 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Published
2023
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48. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H, Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B, Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N, Faro, Valeria Lo, Lopera-Maya, Esteban A, Läll, Kristi, Favé, Marie-Julie, Partanen, Juulia J, Chapman, Sinéad B, Karjalainen, Juha, Kurki, Mitja, Maasha, Mutaamba, Brumpton, Ben M, Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A, Guare, Lindsay A, Gignoux, Christopher R, Graham, Sarah E, Hornsby, Whitney E, Ingold, Nathan, Ismail, Said I, Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y, Moreno-Grau, Sonia, Nam, Kisung, Palta, Priit, Pandit, Anita, Preuss, Michael H, Saad, Chadi, Setia-Verma, Shefali, Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Verma, Anurag, Zawistowski, Matthew, Zhong, Xue, Afifi, Nahla, Al-Dabhani, Kawthar M, Thani, Asma Al, Bradford, Yuki, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H, Damrauer, Scott M, Douville, Nicholas J, Finer, Sarah, Fritsche, Lars G, Fthenou, Eleni, Gonzalez-Arroyo, Gilberto, Griffiths, Christopher J, Guo, Yu, Hunt, Karen A, Ioannidis, Alexander, Jansonius, Nomdo M, Konuma, Takahiro, Lee, Ming Ta Michael, Lopez-Pineda, Arturo, Matsuda, Yuta, Marioni, Riccardo E, Moatamed, Babak, Nava-Aguilar, Marco A, Numakura, Kensuke, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Rojas-Muñoz, Agustin, Shortt, Jonathan A, Straub, Peter, Tao, Ran, Vanderwerff, Brett, Vernekar, Manvi, Veturi, Yogasudha, Barnes, Kathleen C, Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K, Franke, Lude, Gamazon, Eric R, Ganna, Andrea, Gaunt, Tom R, Ge, Tian, Huang, Hailiang, and Huffman, Jennifer

Subjects
Human Genome, Genetics, Biotechnology, Generic health relevance, Good Health and Well Being, Biobank of the Americas, Biobank Japan Project, BioMe, BioVU, CanPath - Ontario Health Study, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, Estonian Biobank, FinnGen, Generation Scotland, Genes & Health Research Team, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, National Biobank of Korea, Penn Medicine BioBank, Qatar Biobank, QSkin Sun and Health Study, Taiwan Biobank, HUNT Study, UCLA ATLAS Community Health Initiative, Uganda Genome Resource, UK Biobank, GWAS, ancestry diversity, biobank, genetic association studies, meta-analysis, phenotype harmonization
Abstract

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Published
2022

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