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Articles by Erping Long
LEME: open large language models for ophthalmology with advanced reasoning and clinical validation
Abstract Large language models (LLMs) offer potential to alleviate the growing public health burden of eye disease; however, few models have been tailored for ophthalmology or evaluated against clinically relevant benchmarks and real-world workflows.
From raw audio to structure: an agent-based pipeline that boosts medical LLM performance
Abstract Large language models (LLMs) are increasingly applied in clinical communication, yet their reliability depends on high-quality conversational corpora. Real-world doctor–patient recordings are frequently degraded by noise, transcription errors, speaker overlap, and fragmented dialogue structure, limiting their usability for downstream model training.
A foundational model encodes deep phenotyping data and enables diverse downstream applications
Abstract Deep phenotyping data are essential for understanding disease mechanisms and individual health trajectories. However, its unprecedented scale and complexity pose significant analytical challenges that conventional approaches struggle to address. Here, we introduce ukbFound, a foundation model that encoded thousands of individual-level traits into language-like sequences.
Model confrontation and collaboration: A debate intelligence framework for enhancing medical reasoning in large language models
Keywords large language models medical AI benchmarks medical reasoning Introduction Medical reasoning is fundamental to decision-making in clinical practice.1 In recent years, large language models (LLMs) tailored for healthcare have achieved state-of-the-art (SOTA) performance on a range of medical benchmarks.2,3,4 However, these models typically rely on a single-LLM architecture, which inherently lacks mechanisms for external validation, cross-perspective critique, and self-correction5—key...
Single-cell transcriptomic profiling reveals cell type heterogeneity between HFpEF and HFrEF - Communications Biology
Abstract Heart failure with preserved ejection fraction (HFpEF) as a high heterogeneity clinical syndrome, is commonly associated with diastolic dysfunction, and has no effective therapy, which is obviously distinct from Heart failure with reduced ejection fraction (HFrEF). Currently the differences of cell type heterogeneity between HFpEF and HFrEF remain largely unknown. Here we illustrate an atlas consisting of 21,747 cardiac cells, including both HFpEF and HFrEF.
Translational fidelity and longevity are genetically linked - Nature Communications
Abstract Aging is a series of adverse changes over time that increases mortality risk. Several hypotheses have been proposed to explain aging, including Leslie Orgel’s Error-Catastrophe Theory, which asserts that translation errors erode the translational machinery, become self-amplifying, and eventually lead to death. Evidence for the theory is scarce, especially regarding intra-specific fidelity-longevity correlations.
Mapping chromatin interactions at melanoma susceptibility loci uncovers distant cis-regulatory gene targets
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1Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA 2Laboratory of Genetic Susceptibility, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA 3Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA 4Genome Modification Core, Frederick National Lab for Cancer Research, Frederick, MD, USA 5Institute of...
By Rohit Thakur, Mai Xu, Hayley Sowards, Joshuah Yon, Lea Jessop, Timothy Myers, Tongwu Zhang, Raj Chari, Erping Long, Thomas Rehling, Rebecca C Hennessey, Karen Funderburk, Jinhu Yin, Mitchell J Machiela, Matthew Johnson, Andrew Wells, Alessandra Chesi, Struan F.A. Grant, Mark Iles, Maria Landi, Matthew Law, Jiyeon Choi, Kevin Brown
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The American Journal of Human Genetics
Verified
An evolving understanding of multiple causal variants underlying genetic association signals
Keywords GWAS multiple causal variants linkage disequilibrium functional variant GWAS follow-up functional studies Get full text access Log in, subscribe or purchase for full access. References 1. Visscher, P.M. ∙ Wray, N.R. ∙ Zhang, Q. ... 10 years of GWAS discovery: Biology, function, and translation Am. J. Hum. Genet. 2017; 101:5-22 2. Abdellaoui, A. ∙ Yengo, L. ∙ Verweij, K.J.H. ... 15 years of GWAS discovery: Realizing the promise Am. J. Hum. Genet. 2023; 110:179-194 3. Schaid, D.J. ∙ Chen, W.
Author Correction: Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes - Nature Communications
Correction to: Nature Communications https://doi.org/10.1038/s41467-024-52356-9, published online 12 September 2024 The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1-18 file, in which specific legends for each Supplementary Data table were missing. The HTML has been updated to include a corrected version of Supplementary Data 1-18.
Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes - Nature Communications
Abstract Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation.
High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility
Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants.
Ambient ultraviolet radiation and ocular melanoma incidence in the United States, 2000−2019 - Eye
Abstract Ocular melanoma is a rare, but deadly cancer. This large cancer registry study examines the associations between solar ultraviolet radiation (UVR) and incidence of different anatomical sites of ocular melanoma by sex, age, laterality, and race and ethnicity. Incidence data were derived from 21 cancer registries in the US for the years 2000–2019. Satellite-based UVR estimates were linked to county of residence at diagnosis.
Evidence for the role of selection for reproductively advantageous alleles in human aging
Abstract The antagonistic pleiotropy hypothesis posits that natural selection for pleiotropic mutations that confer earlier or more reproduction but impair the post-reproductive life causes aging. This hypothesis of the evolutionary origin of aging is supported by case studies but lacks unambiguous genomic evidence. Here, we genomically test this hypothesis using the genotypes, reproductive phenotypes, and death registry of 276,406 U.K. Biobank participants.
Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes
Abstract Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation.
Evidence for the role of selection for reproductively advantageous alleles in human aging
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Early detection of visual impairment in young children using a smartphone-based deep learning system - Nature Medicine
Abstract Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Although certain features of visually impaired children, such as facial appearance and ocular movements, can assist ophthalmic practice, applying these features to real-world screening remains challenging.
Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity
The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci.
Artificial intelligence manages congenital cataract with individualized prediction and telehealth computing
Abstract A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits.
Discrimination of the behavioural dynamics of visually impaired infants via deep learning
Abstract Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear.
Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study
To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.
Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists.
An artificial intelligence platform for the multihospital collaborative management of congenital cataracts
Abstract Using artificial intelligence (AI) to prevent and treat diseases is an ultimate goal in computational medicine. Although AI has been developed for screening and assisted decision-making in disease prevention and management, it has not yet been validated for systematic application in the clinic.
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