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1. Demystifying Large Language Models for Medicine: A Primer

Author

Jin, Qiao, Wan, Nicholas, Leaman, Robert, Tian, Shubo, Wang, Zhizheng, Yang, Yifan, Wang, Zifeng, Xiong, Guangzhi, Lai, Po-Ting, Zhu, Qingqing, Hou, Benjamin, Sarfo-Gyamfi, Maame, Zhang, Gongbo, Gilson, Aidan, Bhasuran, Balu, He, Zhe, Zhang, Aidong, Sun, Jimeng, Weng, Chunhua, Summers, Ronald M., Chen, Qingyu, Peng, Yifan, and Lu, Zhiyong

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials, and answering medical questions. In this primer paper, we propose an actionable guideline to help healthcare professionals more efficiently utilize LLMs in their work, along with a set of best practices. This approach consists of several main phases, including formulating the task, choosing LLMs, prompt engineering, fine-tuning, and deployment. We start with the discussion of critical considerations in identifying healthcare tasks that align with the core capabilities of LLMs and selecting models based on the selected task and data, performance requirements, and model interface. We then review the strategies, such as prompt engineering and fine-tuning, to adapt standard LLMs to specialized medical tasks. Deployment considerations, including regulatory compliance, ethical guidelines, and continuous monitoring for fairness and bias, are also discussed. By providing a structured step-by-step methodology, this tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner., Comment: Under review

Published
2024

2. Environment Scan of Generative AI Infrastructure for Clinical and Translational Science

Author

Idnay, Betina, Xu, Zihan, Adams, William G., Adibuzzaman, Mohammad, Anderson, Nicholas R., Bahroos, Neil, Bell, Douglas S., Bumgardner, Cody, Campion, Thomas, Castro, Mario, Cimino, James J., Cohen, I. Glenn, Dorr, David, Elkin, Peter L, Fan, Jungwei W., Ferris, Todd, Foran, David J., Hanauer, David, Hogarth, Mike, Huang, Kun, Kalpathy-Cramer, Jayashree, Kandpal, Manoj, Karnik, Niranjan S., Katoch, Avnish, Lai, Albert M., Lambert, Christophe G., Li, Lang, Lindsell, Christopher, Liu, Jinze, Lu, Zhiyong, Luo, Yuan, McGarvey, Peter, Mendonca, Eneida A., Mirhaji, Parsa, Murphy, Shawn, Osborne, John D., Paschalidis, Ioannis C., Harris, Paul A., Prior, Fred, Shaheen, Nicholas J., Shara, Nawar, Sim, Ida, Tachinardi, Umberto, Waitman, Lemuel R., Wright, Rosalind J., Zai, Adrian H., Zheng, Kai, Lee, Sandra Soo-Jin, Malin, Bradley A., Natarajan, Karthik, Price II, W. Nicholson, Zhang, Rui, Zhang, Yiye, Xu, Hua, Bian, Jiang, Weng, Chunhua, and Peng, Yifan

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

Published
2024

3. Closing the gap between open source and commercial large language models for medical evidence summarization.

Author

Zhang, Gongbo, Jin, Qiao, Zhou, Yiliang, Wang, Song, Idnay, Betina, Luo, Yiming, Park, Elizabeth, Nestor, Jordan, Spotnitz, Matthew, Soroush, Ali, Campion, Thomas, Lu, Zhiyong, Weng, Chunhua, and Peng, Yifan

Abstract

Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces multiple risk factors, including a lack of transparency and vendor dependency. While open-source LLMs allow better transparency and customization, their performance falls short compared to the proprietary ones. In this study, we investigated to what extent fine-tuning open-source LLMs can further improve their performance. Utilizing a benchmark dataset, MedReview, consisting of 8161 pairs of systematic reviews and summaries, we fine-tuned three broadly-used, open-sourced LLMs, namely PRIMERA, LongT5, and Llama-2. Overall, the performance of open-source models was all improved after fine-tuning. The performance of fine-tuned LongT5 is close to GPT-3.5 with zero-shot settings. Furthermore, smaller fine-tuned models sometimes even demonstrated superior performance compared to larger zero-shot models. The above trends of improvement were manifested in both a human evaluation and a larger-scale GPT4-simulated evaluation.

Published
2024

4. Assessing the Utility of Large Language Models for Phenotype-Driven Gene Prioritization in Rare Genetic Disorder Diagnosis

Author

Kim, Junyoung, Yang, Jingye, Wang, Kai, Weng, Chunhua, and Liu, Cong

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Phenotype-driven gene prioritization is a critical process in the diagnosis of rare genetic disorders for identifying and ranking potential disease-causing genes based on observed physical traits or phenotypes. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models have opened doors to the potential of AI predictions through extensive training on diverse corpora and complex models. This study conducted a comprehensive evaluation of five large language models, including two Generative Pre-trained Transformers series, and three Llama2 series, assessing their performance across three key metrics: task completeness, gene prediction accuracy, and adherence to required output structures. Various experiments explored combinations of models, prompts, input types, and task difficulty levels. Our findings reveal that even the best-performing LLM, GPT-4, achieved an accuracy of 16.0%, which still lags behind traditional bioinformatics tools. Prediction accuracy increased with the parameter/model size. A similar increasing trend was observed for the task completion rate, with complicated prompts more likely to increase task completeness in models smaller than GPT-4. However, complicated prompts are more likely to decrease the structure compliance rate, but no prompt effects on GPT-4. Compared to HPO term-based input, LLM was also able to achieve better than random prediction accuracy by taking free-text input, but slightly lower than with the HPO input. Bias analysis showed that certain genes, such as MECP2, CDKL5, and SCN1A, are more likely to be top-ranked, potentially explaining the variances observed across different datasets. This study provides valuable insights into the integration of LLMs within genomic analysis, contributing to the ongoing discussion on the utilization of advanced LLMs in clinical workflows., Comment: 56 pages, 6 figures, 6 tables, 2 supplementary tables

Published
2024

5. A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study

Author

Park, Jimyung, You, Seng Chan, Jeong, Eugene, Weng, Chunhua, Park, Dongsu, Roh, Jin, Lee, Dong Yun, Cheong, Jae Youn, Choi, Jin Wook, Kang, Mira, and Park, Rae Woong

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundAlthough electronic health records (EHRs) have been widely used in secondary assessments, clinical documents are relatively less utilized owing to the lack of standardized clinical text frameworks across different institutions. ObjectiveThis study aimed to develop a framework for processing unstructured clinical documents of EHRs and integration with standardized structured data. MethodsWe developed a framework known as Staged Optimization of Curation, Regularization, and Annotation of clinical text (SOCRATex). SOCRATex has the following four aspects: (1) extracting clinical notes for the target population and preprocessing the data, (2) defining the annotation schema with a hierarchical structure, (3) performing document-level hierarchical annotation using the annotation schema, and (4) indexing annotations for a search engine system. To test the usability of the proposed framework, proof-of-concept studies were performed on EHRs. We defined three distinctive patient groups and extracted their clinical documents (ie, pathology reports, radiology reports, and admission notes). The documents were annotated and integrated into the Observational Medical Outcomes Partnership (OMOP)-common data model (CDM) database. The annotations were used for creating Cox proportional hazard models with different settings of clinical analyses to measure (1) all-cause mortality, (2) thyroid cancer recurrence, and (3) 30-day hospital readmission. ResultsOverall, 1055 clinical documents of 953 patients were extracted and annotated using the defined annotation schemas. The generated annotations were indexed into an unstructured textual data repository. Using the annotations of pathology reports, we identified that node metastasis and lymphovascular tumor invasion were associated with all-cause mortality among colon and rectum cancer patients (both P=.02). The other analyses involving measuring thyroid cancer recurrence using radiology reports and 30-day hospital readmission using admission notes in depressive disorder patients also showed results consistent with previous findings. ConclusionsWe propose a framework for hierarchical annotation of textual data and integration into a standardized OMOP-CDM medical database. The proof-of-concept studies demonstrated that our framework can effectively process and integrate diverse clinical documents with standardized structured data for clinical research.

Published
2021
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6. Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

Author

Cho, Sylvia, Ensari, Ipek, Weng, Chunhua, Kahn, Michael G, and Natarajan, Karthik

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundThere is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse. ObjectiveThis study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research. MethodsThe literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well. ResultsA total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy. ConclusionsOur study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

Published
2021
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7. GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

Author

Wu, Da, Yang, Jingye, Liu, Cong, Hsieh, Tzung-Chien, Marchi, Elaine, Blair, Justin, Krawitz, Peter, Weng, Chunhua, Chung, Wendy, Lyon, Gholson J., Krantz, Ian D., Kalish, Jennifer M., and Wang, Kai

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia, Quantitative Biology - Genomics
Abstract

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data., Comment: Significant revisions

Published
2023

8. Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness

Author

Zhang, Gongbo, Jin, Qiao, McInerney, Denis Jered, Chen, Yong, Wang, Fei, Cole, Curtis L., Yang, Qian, Wang, Yanshan, Malin, Bradley A., Peleg, Mor, Wallace, Byron C., Lu, Zhiyong, Weng, Chunhua, and Peng, Yifan

Subjects
Computer Science - Artificial Intelligence
Abstract

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Published
2023

9. Large Language Models for Granularized Barrett's Esophagus Diagnosis Classification

Author

Kefeli, Jenna, Soroush, Ali, Diamond, Courtney J., Zylberberg, Haley M., May, Benjamin, Abrams, Julian A., Weng, Chunhua, and Tatonetti, Nicholas

Subjects
Computer Science - Computation and Language
Abstract

Diagnostic codes for Barrett's esophagus (BE), a precursor to esophageal cancer, lack granularity and precision for many research or clinical use cases. Laborious manual chart review is required to extract key diagnostic phenotypes from BE pathology reports. We developed a generalizable transformer-based method to automate data extraction. Using pathology reports from Columbia University Irving Medical Center with gastroenterologist-annotated targets, we performed binary dysplasia classification as well as granularized multi-class BE-related diagnosis classification. We utilized two clinically pre-trained large language models, with best model performance comparable to a highly tailored rule-based system developed using the same data. Binary dysplasia extraction achieves 0.964 F1-score, while the multi-class model achieves 0.911 F1-score. Our method is generalizable and faster to implement as compared to a tailored rule-based approach.

Published
2023

10. Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

Author

Yang, Jingye, Liu, Cong, Deng, Wendy, Wu, Da, Weng, Chunhua, Zhou, Yunyun, and Wang, Kai

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence
Abstract

We hypothesize that large language models (LLMs) based on the transformer architecture can enable automated detection of clinical phenotype terms, including terms not documented in the HPO. In this study, we developed two types of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT as its pre-trained model, and PhenoGPT, a GPT-based model that can be initialized from diverse GPT models, including open-source versions such as GPT-J, Falcon, and LLaMA, as well as closed-source versions such as GPT-3 and GPT-3.5. We compared our methods with PhenoTagger, a recently developed HPO recognition tool that combines rule-based and deep learning methods. We found that our methods can extract more phenotype concepts, including novel ones not characterized by HPO. We also performed case studies on biomedical literature to illustrate how new phenotype information can be recognized and extracted. We compared current BERT-based versus GPT-based models for phenotype tagging, in multiple aspects including model architecture, memory usage, speed, accuracy, and privacy protection. We also discussed the addition of a negation step and an HPO normalization layer to the transformer models for improved HPO term tagging. In conclusion, PhenoBCBERT and PhenoGPT enable the automated discovery of phenotype terms from clinical notes and biomedical literature, facilitating automated downstream tasks to derive new biological insights on human diseases.

Published
2023

11. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Author

Lennon, Niall J., Kottyan, Leah C., Kachulis, Christopher, Abul-Husn, Noura S., Arias, Josh, Belbin, Gillian, Below, Jennifer E., Berndt, Sonja I., Chung, Wendy K., Cimino, James J., Clayton, Ellen Wright, Connolly, John J., Crosslin, David R., Dikilitas, Ozan, Velez Edwards, Digna R., Feng, QiPing, Fisher, Marissa, Freimuth, Robert R., Ge, Tian, Glessner, Joseph T., Gordon, Adam S., Patterson, Candace, Hakonarson, Hakon, Harden, Maegan, Harr, Margaret, Hirschhorn, Joel N., Hoggart, Clive, Hsu, Li, Irvin, Marguerite R., Jarvik, Gail P., Karlson, Elizabeth W., Khan, Atlas, Khera, Amit, Kiryluk, Krzysztof, Kullo, Iftikhar, Larkin, Katie, Limdi, Nita, Linder, Jodell E., Loos, Ruth J. F., Luo, Yuan, Malolepsza, Edyta, Manolio, Teri A., Martin, Lisa J., McCarthy, Li, McNally, Elizabeth M., Meigs, James B., Mersha, Tesfaye B., Mosley, Jonathan D., Musick, Anjene, Namjou, Bahram, Pai, Nihal, Pesce, Lorenzo L., Peters, Ulrike, Peterson, Josh F., Prows, Cynthia A., Puckelwartz, Megan J., Rehm, Heidi L., Roden, Dan M., Rosenthal, Elisabeth A., Rowley, Robb, Sawicki, Konrad Teodor, Schaid, Daniel J., Smit, Roelof A. J., Smith, Johanna L., Smoller, Jordan W., Thomas, Minta, Tiwari, Hemant, Toledo, Diana M., Vaitinadin, Nataraja Sarma, Veenstra, David, Walunas, Theresa L., Wang, Zhe, Wei, Wei-Qi, Weng, Chunhua, Wiesner, Georgia L., Yin, Xianyong, and Kenny, Eimear E.

Published
2024
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12. Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

Author

Robinson, Jamie, Carroll, Robert, Bastarache, Lisa, Chen, Qingxia, Pirruccello, James, Mou, Zongyang, Wei, Wei-Qi, Connolly, John, Mentch, Frank, Crane, Paul, Hebbring, Scott, Crosslin, David, Gordon, Adam, Rosenthal, Elisabeth, Stanaway, Ian, Hayes, M, Wei, Wei, Petukhova, Lynn, Namjou-Khales, Bahram, Zhang, Ge, Safarova, Mayya, Walton, Nephi, Still, Christopher, Bottinger, Erwin, Loos, Ruth, Murphy, Shawn, Jackson, Gretchen, Abumrad, Naji, Kullo, Iftikhar, Jarvik, Gail, Larson, Eric, Weng, Chunhua, Roden, Dan, Khera, Amit, and Denny, Joshua

Subjects
Humans, Phenomics, Electronic Health Records, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Genomics, Genetic Predisposition to Disease, Obesity, Phenotype, Cost of Illness
Abstract

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Published
2022

15. Natural language processing to identify lupus nephritis phenotype in electronic health records

Author

Deng, Yu, Pacheco, Jennifer A., Chung, Anh, Mao, Chengsheng, Smith, Joshua C., Zhao, Juan, Wei, Wei-Qi, Barnado, April, Weng, Chunhua, Liu, Cong, Cordon, Adam, Yu, Jingzhi, Tedla, Yacob, Kho, Abel, Ramsey-Goldman, Rosalind, Walunas, Theresa, and Luo, Yuan

Subjects
Computer Science - Machine Learning
Abstract

Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data. We developed four algorithms: a rule-based algorithm using only structured data (baseline algorithm) and three algorithms using different NLP models. The three NLP models are based on regularized logistic regression and use different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components respectively. The baseline algorithm and the best performed NLP algorithm were external validated on a dataset from Vanderbilt University Medical Center (VUMC). Our best performing NLP model incorporating features from both structured data, regular expression concepts, and mapped CUIs improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.62 vs 0.96) datasets compared to the baseline lupus nephritis algorithm.

Published
2021

16. Progress toward a universal biomedical data translator

Author

Fecho, Karamarie, Thessen, Anne E, Baranzini, Sergio E, Bizon, Chris, Hadlock, Jennifer J, Huang, Sui, Roper, Ryan T, Southall, Noel, Ta, Casey, Watkins, Paul B, Williams, Mark D, Xu, Hao, Byrd, William, Dančík, Vlado, Duby, Marc P, Dumontier, Michel, Glusman, Gustavo, Harris, Nomi L, Hinderer, Eugene W, Hyde, Greg, Johs, Adam, Su, Andrew I, Qin, Guangrong, Zhu, Qian, Dougherty, Jennifer, Huang, Conrad, Magis, Andrew, Smith, Brett, Celebi, Remzi, Chen, Zhehuan, Azevedo, Ricardo De Miranda, Emonet, Vincent, Lee, Jay, Weng, Chunhua, Yilmaz, Arif, Kim, Keum Joo, Santos, Eugene, Tonstad, Lucas, Veenhuis, Luke, Yakaboski, Chase, Acevedo, Liliana, Carrell, Steven, Deutsch, Eric, Glen, Amy, Hoffman, Andrew, Koslicki, David, Kvarfordt, Lindsey, Liu, Zheng, Liu, Shaopeng, Ma, Chunyu, Mendoza, Luis, Muluka, Arun Teja, Womack, Finn, Wood, Erica, Roach, Jared, Goel, Prateek, Weber, Rosina, Williams, Andrew, Gormley, Joseph, Zisk, Tom, Hanspers, Kristina, Hoatlin, Maureen, Pico, Alexander, Riutta, Anders, Callaghan, Jackson, Xu, Colleen, Ahalt, Stanley C, Balhoff, Jim, Edwards, Stephen, Haaland, Perry, Knowles, Michael, Krishnamurthy, Ashok, Mandal, Meisha, Peden, David B, Pfaff, Emily, Schurman, Shepherd, Shrivastava, Shalki, Yi, Hong, Reilly, Jason, Kanwar, Richa, Cox, Steven, Vaidya, Gaurav, Wang, Max, Alkanaq, Ahmed, Costanzo, Maria, Koesterer, Ryan, Flannick, Jason, Burtt, Noel, Kluge, Alexandria, Rubin, Irit, Strasser, Michael Michi, Chung, Lawrence, Kang, Jimin, Mantilla, Michelle, Muller, Sandrine, Persaud, Bria, Wei, Qi, Baumgartner, Andrew, Dai, Cheng, and Duvvuri, Venkata

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Biomedical Data Translator Consortium, Cardiorespiratory Medicine and Haematology, Oncology and Carcinogenesis, Other Medical and Health Sciences, General Clinical Medicine, Cardiovascular medicine and haematology, Pharmacology and pharmaceutical sciences
Abstract

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Published
2022

19. Strong protective effect of the APOL1 p.N264K variant against G2-associated focal segmental glomerulosclerosis and kidney disease

Author

Gupta, Yask, Friedman, David J., McNulty, Michelle T., Khan, Atlas, Lane, Brandon, Wang, Chen, Ke, Juntao, Jin, Gina, Wooden, Benjamin, Knob, Andrea L., Lim, Tze Y., Appel, Gerald B., Huggins, Kinsie, Liu, Lili, Mitrotti, Adele, Stangl, Megan C., Bomback, Andrew, Westland, Rik, Bodria, Monica, Marasa, Maddalena, Shang, Ning, Cohen, David J., Crew, Russell J., Morello, William, Canetta, Pietro, Radhakrishnan, Jai, Martino, Jeremiah, Liu, Qingxue, Chung, Wendy K., Espinoza, Angelica, Luo, Yuan, Wei, Wei-Qi, Feng, Qiping, Weng, Chunhua, Fang, Yilu, Kullo, Iftikhar J., Naderian, Mohammadreza, Limdi, Nita, Irvin, Marguerite R., Tiwari, Hemant, Mohan, Sumit, Rao, Maya, Dube, Geoffrey K., Chaudhary, Ninad S., Gutiérrez, Orlando M., Judd, Suzanne E., Cushman, Mary, Lange, Leslie A., Lange, Ethan M., Bivona, Daniel L., Verbitsky, Miguel, Winkler, Cheryl A., Kopp, Jeffrey B., Santoriello, Dominick, Batal, Ibrahim, Pinheiro, Sérgio Veloso Brant, Oliveira, Eduardo Araújo, Simoes e Silva, Ana Cristina, Pisani, Isabella, Fiaccadori, Enrico, Lin, Fangming, Gesualdo, Loreto, Amoroso, Antonio, Ghiggeri, Gian Marco, D’Agati, Vivette D., Magistroni, Riccardo, Kenny, Eimear E., Loos, Ruth J. F., Montini, Giovanni, Hildebrandt, Friedhelm, Paul, Dirk S., Petrovski, Slavé, Goldstein, David B., Kretzler, Matthias, Gbadegesin, Rasheed, Gharavi, Ali G., Kiryluk, Krzysztof, Sampson, Matthew G., Pollak, Martin R., and Sanna-Cherchi, Simone

Published
2023
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21. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Author

Pacheco, Jennifer A., Rasmussen, Luke V., Wiley, Jr., Ken, Person, Thomas Nate, Cronkite, David J., Sohn, Sunghwan, Murphy, Shawn, Gundelach, Justin H., Gainer, Vivian, Castro, Victor M., Liu, Cong, Mentch, Frank, Lingren, Todd, Sundaresan, Agnes S., Eickelberg, Garrett, Willis, Valerie, Furmanchuk, Al’ona, Patel, Roshan, Carrell, David S., Deng, Yu, Walton, Nephi, Satterfield, Benjamin A., Kullo, Iftikhar J., Dikilitas, Ozan, Smith, Joshua C., Peterson, Josh F., Shang, Ning, Kiryluk, Krzysztof, Ni, Yizhao, Li, Yikuan, Nadkarni, Girish N., Rosenthal, Elisabeth A., Walunas, Theresa L., Williams, Marc S., Karlson, Elizabeth W., Linder, Jodell E., Luo, Yuan, Weng, Chunhua, and Wei, WeiQi

Published
2023
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22. Author Correction: Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Author

Liu, Lili, Khan, Atlas, Sanchez-Rodriguez, Elena, Zanoni, Francesca, Li, Yifu, Steers, Nicholas, Balderes, Olivia, Zhang, Junying, Krithivasan, Priya, LeDesma, Robert A., Fischman, Clara, Hebbring, Scott J., Harley, John B., Moncrieffe, Halima, Kottyan, Leah C., Namjou-Khales, Bahram, Walunas, Theresa L., Knevel, Rachel, Raychaudhuri, Soumya, Karlson, Elizabeth W., Denny, Joshua C., Stanaway, Ian B., Crosslin, David, Rauen, Thomas, Floege, Jürgen, Eitner, Frank, Moldoveanu, Zina, Reily, Colin, Knoppova, Barbora, Hall, Stacy, Sheff, Justin T., Julian, Bruce A., Wyatt, Robert J., Suzuki, Hitoshi, Xie, Jingyuan, Chen, Nan, Zhou, Xujie, Zhang, Hong, Hammarström, Lennart, Viktorin, Alexander, Magnusson, Patrik K. E., Shang, Ning, Hripcsak, George, Weng, Chunhua, Rundek, Tatjana, Elkind, Mitchell S. V., Oelsner, Elizabeth C., Barr, R. Graham, Ionita-Laza, Iuliana, Novak, Jan, Gharavi, Ali G., and Kiryluk, Krzysztof

Published
2023
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30. Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing

Author

Linder, Jodell E., Tao, Ran, Chung, Wendy K., Kiryluk, Krzysztof, Liu, Cong, Weng, Chunhua, Connolly, John J., Hakonarson, Hakon, Harr, Margaret, Leppig, Kathleen A., Jarvik, Gail P., Veenstra, David L., Aufox, Sharon, Chisholm, Rex L., Gordon, Adam S., Hoell, Christin, Rasmussen-Torvik, Laura J., Smith, Maureen E., Holm, Ingrid A., Miller, Erin M., Prows, Cynthia A., Elskeally, Omar, Kullo, Iftikhar J., Lee, Christopher, Jose, Sheethal, Manolio, Teri A., Rowley, Robb, Padi-Adjirackor, Nana Addo, Wilmayani, Ni Ketut, City, Brittany, Wei, Wei-Qi, Wiesner, Georgia L., Rahm, Alanna Kulchak, Williams, Janet L., Williams, Marc S., and Peterson, Josh F.

Published
2023
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31. Charting the life course: Emerging opportunities to advance scientific approaches using life course research

Author

Hanson, Heidi A, Leiser, Claire L, Bandoli, Gretchen, Pollock, Brad H, Karagas, Margaret R, Armstrong, Daniel, Dozier, Ann, Weiskopf, Nicole G, Monaghan, Maureen, Davis, Ann M, Eckstrom, Elizabeth, Weng, Chunhua, Tobin, Jonathan N, Kaskel, Frederick, Schleiss, Mark R, Szilagyi, Peter, Dykes, Carrie, Cooper, Dan, and Barkin, Shari L

Subjects
Public Health, Health Sciences, Generic health relevance, Good Health and Well Being, Translational life course research, complexity science, life course research priorities, life course methods, life course and lifespan, cytomegalovirus
Abstract

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.

Published
2021

35. Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

Author

Ong, Edison, Wang, Lucy L, Schaub, Jennifer, O’Toole, John F, Steck, Becky, Rosenberg, Avi Z, Dowd, Frederick, Hansen, Jens, Barisoni, Laura, Jain, Sanjay, de Boer, Ian H, Valerius, M Todd, Waikar, Sushrut S, Park, Christopher, Crawford, Dana C, Alexandrov, Theodore, Anderton, Christopher R, Stoeckert, Christian, Weng, Chunhua, Diehl, Alexander D, Mungall, Christopher J, Haendel, Melissa, Robinson, Peter N, Himmelfarb, Jonathan, Iyengar, Ravi, Kretzler, Matthias, Mooney, Sean, and He, Yongqun

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Kidney Disease, Networking and Information Technology R&D (NITRD), Generic health relevance, Renal and urogenital, Good Health and Well Being, Atlases as Topic, Big Data, Biological Ontologies, Humans, Kidney Diseases, Phenotype, Precision Medicine, Kidney Precision Medicine Project, Urology & Nephrology, Clinical sciences
Abstract

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient's phenotype. The development of two new ontologies - the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation - will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

Published
2020

36. Charting the life course: Emerging opportunities to advance scientific approaches using life course research.

Author

Hanson, Heidi A, Leiser, Claire L, Bandoli, Gretchen, Pollock, Brad H, Karagas, Margaret R, Armstrong, Daniel, Dozier, Ann, Weiskopf, Nicole G, Monaghan, Maureen, Davis, Ann M, Eckstrom, Elizabeth, Weng, Chunhua, Tobin, Jonathan N, Kaskel, Frederick, Schleiss, Mark R, Szilagyi, Peter, Dykes, Carrie, Cooper, Dan, and Barkin, Shari L

Subjects
Translational life course research, complexity science, cytomegalovirus, life course and lifespan, life course methods, life course research priorities, Generic health relevance
Abstract

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.

Published
2020

38. Returning integrated genomic risk and clinical recommendations: The eMERGE study

Author

Gordon, Adam, Sobowale, Agboade, Allworth, Aimee, Patel, Akshar, DiVietro, Alanna, Strong, Alanna, Sherafati, Alborz, Sherfati, Alborz, Bick, Alex, Miller, Alexandra, Chandel, Alka, Rosenthal, Alyssa, Khera, Amit, Kontorovich, Amy, Beck, Andrew, Beck, Andy, Espinoza, Angelica, Lewis, Anna, Prince, Anya, Khan, Atlas, Iverson, Ayuko, Khales, Bahram Namjou, Benoit, Barbara, Hernan, Becca, Kallman, Ben, Kerman, Ben, Shoemaker, Ben, Satterfield, Benjamin, Devine, Beth, Etheridge, Bethany, Goff, Blake, Freimuth, Bob, Grundmeier, Bob, Collier, Brenae, Mutai, Brenda, Harnett, Brett, Chang, Brian, Piening, Brian, Davis, Brittney, Korf, Bruce, Patterson, Candace, Demetriou, Carmen, Ta, Casey, Hammack, Catherine, Nelson, Catrina, Gascoigne, Caytie, Dorn, Chad, Moretz, Chad, Kachulis, Chris, Hoell, Christie, Cowles, Christine, Lange, Christoph, Weng, Chunhua, Prows, Cindy, Brokamp, Cole, Liu, Cong, Scherr, Courtney, Gonzalez, Crystal, Ramirez, Cynthia, Shimbo, Daichi, Roden, Dan, Schaid, Daniel, Kaufman, Dave, Crosslin, David, Kochan, David, Veenstra, David, Singh, Davinder, Karavite, Dean, Abrams, Debbie, Absher, Devin, Edwards, Digna Velez, Haverfield, Eden, Morales, Eduardo, Esplin, Edward, Malolepsza, Edyta, Alipour, Ehsan, Kenny, Eimear, Rosenthal, Elisabeth, Duvall, Eliza, McNally, Elizabeth, Bhoj, Elizabeth, Cohn, Elizabeth, Hibler, Elizabeth, Karlson, Elizabeth, Clayton, Ellen, Chesnut, Emily, DeFranco, Emily, Gallagher, Emily, Soper, Emily, Perez, Emma, Cash, Erin, Berner, Eta, Wang, Fei, Wehbe, Firas, Ricci, Francisco, Mentch, Frank, Shaibi, Gabriel, Jarvik, Gail, Hahn, George, Hripcsak, George, Wiesner, Georgia, Belbin, Gillian, Davogustto, Gio, Nadkarni, Girish, Qiu, Haijun, Hakonarson, Hakon, Bangash, Hana, Beasley, Hannah, Liu, Hao, Aungst, Heide, Tiwari, Hemant, Duckham, Hillary, Thomas, Hope, Kullo, Iftikhar, Holm, Ingrid, Allen, Isabelle, Ionita-Laza, Iuliana, Hellwege, Jacklyn, Petrzelka, Jacob, Odgis, Jacqueline, Narula, Jahnavi, Petrzelka, Jake, Patel, Jalpa, Cimino, James, Meigs, James, Snyder, James, Olson, Janet, Zahner, Janet, Pennington, Jeff, Pacheco, Jen, Pacheco, Jennifer Allen, Morse, Jennifer, Corsmo, Jeremy, Thayer, Jeritt, Cimino, Jim, Chen, Jingheng, Fournier, Jocelyn, Jackson, Jodell, Glessner, Joe, Pacyna, Joel, Smith, Johanna, Connolly, John, Lynch, John, Shelley, John, Mosley, Jonathan, Nestor, Jordan, Smoller, Jordan, Alsip, Jorge, Kannry, Joseph, Sutton, Joseph, Peterson, Josh, Smith, Joshua, Galasso, Julia, Smith, Julia, Wynn, Julia, Gundelach, Justin, Starren, Justin, Choi, Karmel, Mittendorf, Kate, Anderson, Katherine, Bonini, Katherine, Leppig, Kathleen, Muenzen, Kathleen, Larkin, Katie, Stuttgen, Kelsey, Wiley, Ken, Nguyen, Kenny, Dufendach, Kevin, Atkins, Kiley, Sawicki, Konrad, Norland, Kristjan, Kiryluk, Krzysztof, Beskow, Laura, Rasmussen-Torvik, Laura, Kottyan, Leah, Hsu, Li, Tian, Lifeng, Mahanta, Lisa, Martin, Lisa, Wang, Lisa, Gomez, Lizbeth, Thompson, Lorenzo, Orlando, Lori, Richter, Lucas, Rasmussen, Luke, Petukhova, Lynn, Seabolt, Lynn, O’Brien, Madison, Harden, Maegan, Fullerton, Malia, Harr, Margaret, Beasley, Mark, Guindo, Marta, Horike, Martha, Horike-Pyne, Martha, Abdalla, Marwah, Hamed, Marwan, Terry, Mary Beth, Maradik, Mary, Wyatt, Matt, Davis, Matthew, Lebo, Matthew, Smith, Maureen, Rosario, Maya del, Sabatello, Maya, Behr, Meckenzie, Roy-Puckelwartz, Meg, Habrat, Mel, Myers, Melanie, Yetisgen, Meliha, Iris, Merve, DaSilva, Michael, Preuss, Michael, McGowan, Michelle, Shi, Mingjian, Perera, Minoli, Thomas, Minta, Elkind, Mitch, Abbass, Mohammad, Saadatagah, Mohammad, Hess, Molly, Maradik, Molly, Vaitinadin, Nataraja “RJ”, Vaitinadin, Nataraja, Muthu, Naveen, Netherly, Neil, Lennon, Niall, Shang, Ning, Limdi, Nita, Forrest, Noah, Romero, Noheli, Robinson, Nora, Abul-Husn, Noura, Elsekaily, Omar, Dikilitas, Ozan, Kovatch, Patricia, Davis, Patrick, Appelbaum, Paul, Francaviglia, Paul, O’Reilly, Paul, Chandler, Paulette, Caraballo, Pedro, Tarczy-Hornoch, Peter, Shum, Pierre, Marathe, Priya, Murali, Priyanka, Feng, Qiping, Wells, Quinn, Atchley, Rachel, Narla, Radhika, Barton, Rene, Sterling, Rene, Chisholm, Rex, Green, Richard, Sharp, Richard, Peters, Riki, Kukafka, Rita, Rowley, Robb, Freimuth, Robert, Green, Robert, Winter, Robert, Mueller, Roger, Loos, Ruth, Irvin, Ryan, Suckiel, Sabrina, Hussain, Sajjad, Sharba, Samer, Aronson, Sandy, Jones, Sarah, Knerr, Sarah, Nigbur, Scott, Weiss, Scott, Mooney, Sean, Terek, Shannon, Aufox, Sharon, Nirenberg, Sharon, Murphy, Shawn, O’Byrne, Sheila, Wang (Sam) Choi, Shing, Aguilar, Sienna, Bland, S.T., Rodrigues, Stefanie, Ledbetter, Stephanie, Rutledge, Stephanie, Booth, Stuart James, Xian, Su, Trinidad, Susan Brown, Bakken, Suzanne, Schmidlen, Tara, Rakhra-Burris, Tejinder, Manolio, Teri, Mersha, Tesfaye, Walunas, Theresa, Chandereng, Thevaa, May, Thomas, Ge, Tian, Edwards, Todd, Kaszemacher, Tom, Hernandez, Valentina, Willis, Valerie, Desai, Vemi, Desai, Vimi, Lorenzi, Virginia, Gainer, Vivian, Wei, Wei-Qi, Chung, Wendy, Su, Wu-Chen, Chang, Xiao, Zhao, Yiqing, Luo, Yuan, Shen, Yufeng, Linder, Jodell E., Bland, Sarah T., Caraballo, Pedro J., Chisholm, Rex L., Clayton, Ellen Wright, Crosslin, David R., Esplin, Edward D., Forman, Sophie, Freimuth, Robert R., Gordon, Adam S., Harden, Maegan V., Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth W., Labrecque, Sofia, Lennon, Niall J., Limdi, Nita A., Mittendorf, Kathleen F., Murphy, Shawn N., Prows, Cynthia A., Rasmussen, Luke V., Sawicki, Konrad Teodor, Velez Edwards, Digna R., Abul-Husn, Noura S., Below, Jennifer E., Berner, Eta S., Booth, James, Chung, Wendy K., Cimino, James J., Fullerton, Stephanie M., Guiducci, Candace, Habrat, Melissa L., Hain, Heather, Hoell, Christin, Irvin, Marguerite R., Kachulis, Christopher, Kenny, Eimear E., Kullo, Iftikhar J., Manolio, Teri A., McNally, Elizabeth M., Mooney, Sean D., Namjou, Bahram, Perez, Emma F., Puckelwartz, Megan J., Roden, Dan M., Rosenthal, Elisabeth A., Saadatagah, Seyedmohammad, Schaid, Dan J., Schultz, Baergen, Shaibi, Gabriel Q., Sharp, Richard R., Shirts, Brian, Smith, Maureen E., Smoller, Jordan W., Suckiel, Sabrina A., Tiwari, Hemant K., Trinidad, Susan B., Wells, Quinn S., Wiesner, Georgia L., and Peterson, Josh F.

Published
2023
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41. Genome-wide polygenic score to predict chronic kidney disease across ancestries

Author

Khan, Atlas, Turchin, Michael C., Patki, Amit, Srinivasasainagendra, Vinodh, Shang, Ning, Nadukuru, Rajiv, Jones, Alana C., Malolepsza, Edyta, Dikilitas, Ozan, Kullo, Iftikhar J., Schaid, Daniel J., Karlson, Elizabeth, Ge, Tian, Meigs, James B., Smoller, Jordan W., Lange, Christoph, Crosslin, David R., Jarvik, Gail P., Bhatraju, Pavan K., Hellwege, Jacklyn N., Chandler, Paulette, Torvik, Laura Rasmussen, Fedotov, Alex, Liu, Cong, Kachulis, Christopher, Lennon, Niall, Abul-Husn, Noura S., Cho, Judy H., Ionita-Laza, Iuliana, Gharavi, Ali G., Chung, Wendy K., Hripcsak, George, Weng, Chunhua, Nadkarni, Girish, Irvin, Marguerite R., Tiwari, Hemant K., Kenny, Eimear E., Limdi, Nita A., and Kiryluk, Krzysztof

Published
2022
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47. Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitions

Author

Yu, Jingzhi, Pacheco, Jennifer A., Ghosh, Anika S., Luo, Yuan, Weng, Chunhua, Shang, Ning, Benoit, Barbara, Carrell, David S., Carroll, Robert J., Dikilitas, Ozan, Freimuth, Robert R., Gainer, Vivian S., Hakonarson, Hakon, Hripcsak, George, Kullo, Iftikhar J., Mentch, Frank, Murphy, Shawn N., Peissig, Peggy L., Ramirez, Andrea H., Walton, Nephi, Wei, Wei-Qi, and Rasmussen, Luke V.

Published
2022
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48. Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders

Author

Zhang, Xinyuan, Lucas, Anastasia M., Veturi, Yogasudha, Drivas, Theodore G., Bone, William P., Verma, Anurag, Chung, Wendy K., Crosslin, David, Denny, Joshua C., Hebbring, Scott, Jarvik, Gail P., Kullo, Iftikhar, Larson, Eric B., Rasmussen-Torvik, Laura J., Schaid, Daniel J., Smoller, Jordan W., Stanaway, Ian B., Wei, Wei-Qi, Weng, Chunhua, and Ritchie, Marylyn D.

Published
2022
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49. Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Author

Liu, Lili, Khan, Atlas, Sanchez-Rodriguez, Elena, Zanoni, Francesca, Li, Yifu, Steers, Nicholas, Balderes, Olivia, Zhang, Junying, Krithivasan, Priya, LeDesma, Robert A., Fischman, Clara, Hebbring, Scott J., Harley, John B., Moncrieffe, Halima, Kottyan, Leah C., Namjou-Khales, Bahram, Walunas, Theresa L., Knevel, Rachel, Raychaudhuri, Soumya, Karlson, Elizabeth W., Denny, Joshua C., Stanaway, Ian B., Crosslin, David, Rauen, Thomas, Floege, Jürgen, Eitner, Frank, Moldoveanu, Zina, Reily, Colin, Knoppova, Barbora, Hall, Stacy, Sheff, Justin T., Julian, Bruce A., Wyatt, Robert J., Suzuki, Hitoshi, Xie, Jingyuan, Chen, Nan, Zhou, Xujie, Zhang, Hong, Hammarström, Lennart, Viktorin, Alexander, Magnusson, Patrik K. E., Shang, Ning, Hripcsak, George, Weng, Chunhua, Rundek, Tatjana, Elkind, Mitchell S. V., Oelsner, Elizabeth C., Barr, R. Graham, Ionita-Laza, Iuliana, Novak, Jan, Gharavi, Ali G., and Kiryluk, Krzysztof

Published
2022
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