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378 results on '"Zhu, Dakai"'

1. Validating LLM-Generated Programs with Metamorphic Prompt Testing

Author

Wang, Xiaoyin and Zhu, Dakai

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract

The latest paradigm shift in software development brings in the innovation and automation afforded by Large Language Models (LLMs), showcased by Generative Pre-trained Transformer (GPT), which has shown remarkable capacity to generate code autonomously, significantly reducing the manual effort required for various programming tasks. Although, the potential benefits of LLM-generated code are vast, most notably in efficiency and rapid prototyping, as LLMs become increasingly integrated into the software development lifecycle and hence the supply chain, complex and multifaceted challenges arise as the code generated from these language models carry profound questions on quality and correctness. Research is required to comprehensively explore these critical concerns surrounding LLM-generated code. In this paper, we propose a novel solution called metamorphic prompt testing to address these challenges. Our intuitive observation is that intrinsic consistency always exists among correct code pieces but may not exist among flawed code pieces, so we can detect flaws in the code by detecting inconsistencies. Therefore, we can vary a given prompt to multiple prompts with paraphrasing, and to ask the LLM to acquire multiple versions of generated code, so that we can validate whether the semantic relations still hold in the acquired code through cross-validation. Our evaluation on HumanEval shows that metamorphic prompt testing is able to detect 75 percent of the erroneous programs generated by GPT-4, with a false positive rate of 8.6 percent.

Published
2024

2. Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach

Author

Zheng, Xi, Mok, Aloysius K., Piskac, Ruzica, Lee, Yong Jae, Krishnamachari, Bhaskar, Zhu, Dakai, Sokolsky, Oleg, and Lee, Insup

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Robotics
Abstract

The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations. This convergence has accelerated the development and deployment of a range of real-world applications, such as autonomous vehicles, delivery drones, service robots, and telemedicine procedures. However, the software development life cycle (SDLC) for AI-infused CPS diverges significantly from traditional approaches, featuring data and learning as two critical components. Existing verification and validation techniques are often inadequate for these new paradigms. In this study, we pinpoint the main challenges in ensuring formal safety for learningenabled CPS.We begin by examining testing as the most pragmatic method for verification and validation, summarizing the current state-of-the-art methodologies. Recognizing the limitations in current testing approaches to provide formal safety guarantees, we propose a roadmap to transition from foundational probabilistic testing to a more rigorous approach capable of delivering formal assurance.

Published
2023

3. PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models

Author

Zhang, Yuntong, Xu, Jingye, Xie, Mimi, Wang, Wei, Ye, Keying, Wang, Jing, and Zhu, Dakai

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5% error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time.

Published
2023

4. Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning

Author

Zhang, Yuntong, Xu, Jingye, Xie, Mimi, Zhu, Dakai, Song, Houbing, and Wang, Wei

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and potential future use in healthcare. To address the above issues, we first collected a large dataset of PPG signals and HRV ground truth. With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV. Evaluation results show that our method had errors between 3.5% to 25.7% and outperformed signal-processing-only and ML-only methods. We also explored different ML models, which showed that Decision Trees and Multi-level Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds of KB and inference time less than 1ms. Hence, they are more suitable for small embedded devices and potentially enable the future use of PPG-based HRV monitoring in healthcare.

Published
2023

7. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer.

Author

Byun, Jinyoung, Han, Younghun, Li, Yafang, Xia, Jun, Long, Erping, Choi, Jiyeon, Xiao, Xiangjun, Zhu, Meng, Zhou, Wen, Sun, Ryan, Bossé, Yohan, Song, Zhuoyi, Schwartz, Ann, Lusk, Christine, Rafnar, Thorunn, Stefansson, Kari, Zhang, Tongwu, Zhao, Wei, Pettit, Rowland W, Liu, Yanhong, Li, Xihao, Zhou, Hufeng, Walsh, Kyle M, Gorlov, Ivan, Gorlova, Olga, Zhu, Dakai, Rosenberg, Susan M, Pinney, Susan, Bailey-Wilson, Joan E, Mandal, Diptasri, de Andrade, Mariza, Gaba, Colette, Willey, James C, You, Ming, Anderson, Marshall, Wiencke, John K, Albanes, Demetrius, Lam, Stephan, Tardon, Adonina, Chen, Chu, Goodman, Gary, Bojeson, Stig, Brenner, Hermann, Landi, Maria Teresa, Chanock, Stephen J, Johansson, Mattias, Muley, Thomas, Risch, Angela, Wichmann, H-Erich, Bickeböller, Heike, Christiani, David C, Rennert, Gad, Arnold, Susanne, Field, John K, Shete, Sanjay, Le Marchand, Loic, Melander, Olle, Brunnstrom, Hans, Liu, Geoffrey, Andrew, Angeline S, Kiemeney, Lambertus A, Shen, Hongbing, Zienolddiny, Shanbeh, Grankvist, Kjell, Johansson, Mikael, Caporaso, Neil, Cox, Angela, Hong, Yun-Chul, Yuan, Jian-Min, Lazarus, Philip, Schabath, Matthew B, Aldrich, Melinda C, Patel, Alpa, Lan, Qing, Rothman, Nathaniel, Taylor, Fiona, Kachuri, Linda, Witte, John S, Sakoda, Lori C, Spitz, Margaret, Brennan, Paul, Lin, Xihong, McKay, James, Hung, Rayjean J, and Amos, Christopher I

Subjects
Humans, Lung Neoplasms, Genetic Predisposition to Disease, RNA-Binding Proteins, DNA-Binding Proteins, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Human Genome, Cancer, Genetics, Lung, 2.1 Biological and endogenous factors, Aetiology, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Published
2022

10. uPredict: A User-Level Profiler-Based Predictive Framework for Single VM Applications in Multi-Tenant Clouds

Author

Moradi, Hamidreza, Wang, Wei, Fernandez, Amanda, and Zhu, Dakai

Subjects
Computer Science - Performance
Abstract

Most existing studies on performance prediction for virtual machines (VMs) in multi-tenant clouds are at system level and generally require access to performance counters in Hypervisors. In this work, we propose uPredict, a user-level profiler-based performance predictive framework for single-VM applications in multi-tenant clouds. Here, three micro-benchmarks are specially devised to assess the contention of CPUs, memory and disks in a VM, respectively. Based on measured performance of an application and micro-benchmarks, the application and VM-specific predictive models can be derived by exploiting various regression and neural network based techniques. These models can then be used to predict the application's performance using the in-situ profiled resource contention with the micro-benchmarks. We evaluated uPredict extensively with representative benchmarks from PARSEC, NAS Parallel Benchmarks and CloudSuite, on both a private cloud and two public clouds. The results show that the average prediction errors are between 9.8% to 17% for various predictive models on the private cloud with high resource contention, while the errors are within 4% on public clouds. A smart load-balancing scheme powered by uPredict is presented and can effectively reduce the execution and turnaround times of the considered application by 19% and 10%, respectively., Comment: 14 pages

Published
2019

19. Rare deleterious germline variants and risk of lung cancer

Author

Liu, Yanhong, Xia, Jun, McKay, James, Tsavachidis, Spiridon, Xiao, Xiangjun, Spitz, Margaret R., Cheng, Chao, Byun, Jinyoung, Hong, Wei, Li, Yafang, Zhu, Dakai, Song, Zhuoyi, Rosenberg, Susan M., Scheurer, Michael E., Kheradmand, Farrah, Pikielny, Claudio W., Lusk, Christine M., Schwartz, Ann G., Wistuba, Ignacio I., Cho, Michael H., Silverman, Edwin K., Bailey-Wilson, Joan, Pinney, Susan M., Anderson, Marshall, Kupert, Elena, Gaba, Colette, Mandal, Diptasri, You, Ming, de Andrade, Mariza, Yang, Ping, Liloglou, Triantafillos, Davies, Michael P. A., Lissowska, Jolanta, Swiatkowska, Beata, Zaridze, David, Mukeria, Anush, Janout, Vladimir, Holcatova, Ivana, Mates, Dana, Stojsic, Jelena, Scelo, Ghislaine, Brennan, Paul, Liu, Geoffrey, Field, John K., Hung, Rayjean J., Christiani, David C., and Amos, Christopher I.

Published
2021
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25. Protein-altering germline mutations implicate novel genes related to lung cancer development

Author

Ji, Xuemei, Mukherjee, Semanti, Landi, Maria Teresa, Bosse, Yohan, Joubert, Philippe, Zhu, Dakai, Gorlov, Ivan, Xiao, Xiangjun, Han, Younghun, Gorlova, Olga, Hung, Rayjean J., Brhane, Yonathan, Carreras-Torres, Robert, Christiani, David C., Caporaso, Neil, Johansson, Mattias, Liu, Geoffrey, Bojesen, Stig E., Le Marchand, Loic, Albanes, Demetrios, Bickeböller, Heike, Aldrich, Melinda C., Bush, William S., Tardon, Adonina, Rennert, Gad, Chen, Chu, Byun, Jinyoung, Dragnev, Konstantin H., Field, John K., Kiemeney, Lambertus FA., Lazarus, Philip, Zienolddiny, Shan, Lam, Stephen, Schabath, Matthew B., Andrew, Angeline S., Bertazzi, Pier A., Pesatori, Angela C., Diao, Nancy, Su, Li, Song, Lei, Zhang, Ruyang, Leighl, Natasha, Johansen, Jakob S., Mellemgaard, Anders, Saliba, Walid, Haiman, Christopher, Wilkens, Lynne, Fernandez-Somoano, Ana, Fernandez-Tardon, Guillermo, Heijden, Erik H. F. M. van der, Kim, Jin Hee, Davies, Michael P. A., Marcus, Michael W., Brunnström, Hans, Manjer, Jonas, Melander, Olle, Muller, David C., Overvad, Kim, Trichopoulou, Antonia, Tumino, Rosario, Goodman, Gary E., Cox, Angela, Taylor, Fiona, Woll, Penella, Wichmann, Erich, Muley, Thomas, Risch, Angela, Rosenberger, Albert, Grankvist, Kjell, Johansson, Mikael, Shepherd, Frances, Tsao, Ming-Sound, Arnold, Susanne M., Haura, Eric B., Bolca, Ciprian, Holcatova, Ivana, Janout, Vladimir, Kontic, Milica, Lissowska, Jolanta, Mukeria, Anush, Ognjanovic, Simona, Orlowski, Tadeusz M., Scelo, Ghislaine, Swiatkowska, Beata, Zaridze, David, Bakke, Per, Skaug, Vidar, Butler, Lesley M., Offit, Kenneth, Srinivasan, Preethi, Bandlamudi, Chaitanya, Hellmann, Matthew D., Solit, David B., Robson, Mark E., Rudin, Charles M., Stadler, Zsofia K., Taylor, Barry S., Berger, Michael F., Houlston, Richard, McLaughlin, John, Stevens, Victoria, Nickle, David C., Obeidat, Ma’en, Timens, Wim, Artigas, María Soler, Shete, Sanjay, Brenner, Hermann, Chanock, Stephen, Brennan, Paul, McKay, James D., and Amos, Christopher I.

Published
2020
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35. Fine mapping of MHC region in lung cancer highlights independent susceptibility loci by ethnicity

Author

Ferreiro-Iglesias, Aida, Lesseur, Corina, McKay, James, Hung, Rayjean J., Han, Younghun, Zong, Xuchen, Christiani, David, Johansson, Mattias, Xiao, Xiangjun, Li, Yafang, Qian, David C., Ji, Xuemei, Liu, Geoffrey, Caporaso, Neil, Scelo, Ghislaine, Zaridze, David, Mukeriya, Anush, Kontic, Milica, Ognjanovic, Simona, Lissowska, Jolanta, Szołkowska, Małgorzata, Swiatkowska, Beata, Janout, Vladimir, Holcatova, Ivana, Bolca, Ciprian, Savic, Milan, Ognjanovic, Miodrag, Bojesen, Stig Egil, Wu, Xifeng, Albanes, Demetrios, Aldrich, Melinda C., Tardon, Adonina, Fernandez-Somoano, Ana, Fernandez-Tardon, Guillermo, Le Marchand, Loic, Rennert, Gadi, Chen, Chu, Doherty, Jennifer, Goodman, Gary, Bickeböller, Heike, Wichmann, H-Erich, Risch, Angela, Rosenberger, Albert, Shen, Hongbing, Dai, Juncheng, Field, John K., Davies, Michael, Woll, Penella, Teare, M. Dawn, Kiemeney, Lambertus A., van der Heijden, Erik H. F. M., Yuan, Jian-Min, Hong, Yun-Chul, Haugen, Aage, Zienolddiny, Shanbeh, Lam, Stephen, Tsao, Ming-Sound, Johansson, Mikael, Grankvist, Kjell, Schabath, Matthew B., Andrew, Angeline, Duell, Eric, Melander, Olle, Brunnström, Hans, Lazarus, Philip, Arnold, Susanne, Slone, Stacey, Byun, Jinyoung, Kamal, Ahsan, Zhu, Dakai, Landi, Maria Teresa, Amos, Christopher I., and Brennan, Paul

Published
2018
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36. Energy Efficient Configuration for QoS in Reliable Parallel Servers

Author

Zhu, Dakai, Melhem, Rami, Mossé, Daniel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Dal Cin, Mario, editor, Kaâniche, Mohamed, editor, and Pataricza, András, editor

Published
2005
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37. A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

Author

Zhang, Ruyang, primary, Shen, Sipeng, additional, Wei, Yongyue, additional, Zhu, Ying, additional, Li, Yi, additional, Chen, Jiajin, additional, Guan, Jinxing, additional, Pan, Zoucheng, additional, Wang, Yuzhuo, additional, Zhu, Meng, additional, Xie, Junxing, additional, Xiao, Xiangjun, additional, Zhu, Dakai, additional, Li, Yafang, additional, Albanes, Demetrios, additional, Landi, Maria Teresa, additional, Caporaso, Neil E., additional, Lam, Stephen, additional, Tardon, Adonina, additional, Chen, Chu, additional, Bojesen, Stig E., additional, Johansson, Mattias, additional, Risch, Angela, additional, Bickeböller, Heike, additional, Wichmann, H-Erich, additional, Rennert, Gadi, additional, Arnold, Susanne, additional, Brennan, Paul, additional, McKay, James D., additional, Field, John K., additional, Shete, Sanjay S., additional, Le Marchand, Loic, additional, Liu, Geoffrey, additional, Andrew, Angeline S., additional, Kiemeney, Lambertus A., additional, Zienolddiny-Narui, Shan, additional, Behndig, Annelie, additional, Johansson, Mikael, additional, Cox, Angela, additional, Lazarus, Philip, additional, Schabath, Matthew B., additional, Aldrich, Melinda C., additional, Dai, Juncheng, additional, Ma, Hongxia, additional, Zhao, Yang, additional, Hu, Zhibin, additional, Hung, Rayjean J., additional, Amos, Christopher I., additional, Shen, Hongbing, additional, Chen, Feng, additional, and Christiani, David C., additional

Published
2022
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38. A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

Author

Zhang, Ruyang, Shen, Sipeng, Wei, Yongyue, Zhu, Ying, Li, Yi, Chen, Jiajin, Guan, Jinxing, Pan, Zoucheng, Wang, Yuzhuo, Zhu, Meng, Xie, Junxing, Xiao, Xiangjun, Zhu, Dakai, Li, Yafang, Albanes, Demetrios, Landi, Maria Teresa, Caporaso, Neil E., Lam, Stephen, Tardon, Adonina, Chen, Chu, Bojesen, Stig E., Johansson, Mattias, Risch, Angela, Bickeböller, Heike, Wichmann, H-Erich, Rennert, Gadi, Arnold, Susanne, Brennan, Paul, McKay, James D., Field, John K., Shete, Sanjay S., Le Marchand, Loic, Liu, Geoffrey, Andrew, Angeline S., Kiemeney, Lambertus A., Zienolddiny-Narui, Shan, Behndig, Annelie F., Johansson, Mikael, Cox, Angela, Lazarus, Philip, Schabath, Matthew B., Aldrich, Melinda C., Dai, Juncheng, Ma, Hongxia, Zhao, Yang, Hu, Zhibin, Hung, Rayjean J., Amos, Christopher I., Shen, Hongbing, Chen, Feng, Christiani, David C., Zhang, Ruyang, Shen, Sipeng, Wei, Yongyue, Zhu, Ying, Li, Yi, Chen, Jiajin, Guan, Jinxing, Pan, Zoucheng, Wang, Yuzhuo, Zhu, Meng, Xie, Junxing, Xiao, Xiangjun, Zhu, Dakai, Li, Yafang, Albanes, Demetrios, Landi, Maria Teresa, Caporaso, Neil E., Lam, Stephen, Tardon, Adonina, Chen, Chu, Bojesen, Stig E., Johansson, Mattias, Risch, Angela, Bickeböller, Heike, Wichmann, H-Erich, Rennert, Gadi, Arnold, Susanne, Brennan, Paul, McKay, James D., Field, John K., Shete, Sanjay S., Le Marchand, Loic, Liu, Geoffrey, Andrew, Angeline S., Kiemeney, Lambertus A., Zienolddiny-Narui, Shan, Behndig, Annelie F., Johansson, Mikael, Cox, Angela, Lazarus, Philip, Schabath, Matthew B., Aldrich, Melinda C., Dai, Juncheng, Ma, Hongxia, Zhao, Yang, Hu, Zhibin, Hung, Rayjean J., Amos, Christopher I., Shen, Hongbing, Chen, Feng, and Christiani, David C.

Abstract

Introduction: Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). Methods: Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. Results: With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10−13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10−13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10−13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10−13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10−4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk sc

Published
2022
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48. International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways

Author

Cordell, Heather J, Han, Younghun, Mells, George F, Li, Yafang, Hirschfield, Gideon M, Greene, Casey S, Xie, Gang, Juran, Brian D, Zhu, Dakai, Qian, David C, Floyd, James AB, Morley, Katherine I, Prati, Daniele, Lleo, Ana, Cusi, Daniele, Canadian-US PBC Consortium, Italian PBC Genetics Study Group, UK-PBC Consortium, Gershwin, M Eric, Anderson, Carl A, Lazaridis, Konstantinos N, Invernizzi, Pietro, Seldin, Michael F, Sandford, Richard N, Amos, Christopher I, Siminovitch, Katherine A, Sandford, Richard [0000-0002-7437-0560], and Apollo - University of Cambridge Repository

Subjects
Liver Cirrhosis, Biliary, Humans, Genome-Wide Association Study
Abstract

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined

Published
2020
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