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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research

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Journal of Pathology Informatics 2020年第1期11卷 1-7页

作者： Durant, Thomas J.S. Gong, Guannan Price, Nathan Schulz, Wade Department of Laboratory Medicine Yale University School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation Yale New Haven Hospital New Haven CT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University School of Medicine New Haven CT United States Department of Information Technology Yale New Haven Health New Haven CT United States

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification. © 2020 Journal

关键词： Biobanking biomedical research biospecimen science clinical specimens real-time identification translational research

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Cell2Sentence: Teaching Large Language Models the Language of biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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帕博利珠单抗治疗伴脑转移NSCLC患者的一项非随机、开放Ⅱ期试验的长期随访结果和生物标志物分析

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中国肺癌杂志 2021年第9期24卷 I0007-I0016页

作者： Sarah B GOLDBERG Kurt A SCHALPER Scott N GETTINGER Amit MAHAJAN Roy S HERBST Anne C CHANG Rogerio LILENBAUM Frederick H WILSON Sacit Bulent OMAY James B YU Lucia JILAVEANU Thuy TRAN Kira PAVLIK Elin ROWEN Heather GERRSH Annette KOMLO Richa GUPTA Hailey WYATT Matthew RIBEIRO Yuval KLUGER Geyu ZHOU Wei WEI Veronica L CHANG Harriet M KLUGER 董晓荣(翻译/校对) Department of Medicine(Medical Oncology) Yale School of MedicineNew HavenCTUSA Department of Pathology Yale School of MedicineNew HavenCTUSA Department of Radiology Yale School of MedicineNew HavenCTUSA Department of Neurosurgery Yale School of MedicineNew HavenCTUSA Department of Therapeutic Radiology Yale School of MedicineNew HavenCTUSA Interdepartmental Program in Computational Biology and Bioinformatics Yale School of MedicineNew HavenCTUSA Department of Biostatistics Yale School of Public HealthNew HavenCTUSA 华中科技大学同济医学院附属协和医院胸部肿瘤科

背景与目的我们开展了一项帕博利珠单抗用于伴未治疗脑转移的非小细胞肺癌(non-small cell lung cancer,NSCLC)或黑色素瘤患者的疗效和安全性的II期试验,旨在评估程序性死亡受体1(programmed cell death 1,PD-1)抑制剂在中枢神经系统(ce... 详细信息

背景与目的我们开展了一项帕博利珠单抗用于伴未治疗脑转移的非小细胞肺癌(non-small cell lung cancer,NSCLC)或黑色素瘤患者的疗效和安全性的II期试验,旨在评估程序性死亡受体1(programmed cell death 1,PD-1)抑制剂在中枢神经系统(central nervous system,CNS)中的疗效。中期结果已发表,现报道对NSCLC队列的更新分析结果。方法这是一项开放性、单中心、II期试验。纳入标准:年龄≥18岁,诊断为晚期NSCLC并伴有≥1个5 mm-20 mm脑转移病灶,既往从未治疗或之前放疗后进展,无神经系统症状,不需要激素治疗且美国东部肿瘤协作组(Eastern Cooperative Oncology Group,ECOG)<2分。患者每2周接受一次帕博利珠单抗(10 mg/kg)治疗。队列1为程序性死亡配体1(programmed cell death ligand 1,PD-L1)≥1%的患者,队列2为PD-L1<1%或未评估的患者。主要终点是脑转移患者缓解比例。所有经治患者均纳入疗效与安全性终点的分析。该研究已结束入组,并于***登记注册,注册号为NCT02085070。结果2014年3月31日-2018年5月21日,共42例患者接受治疗。中位随访时间为8.3个月(IQR:4.5个月-26.2个月)。队列1的37例患者中11例有脑转移缓解[29.7%(95%CI:15.9%-47.0%)]。队列2未观察到缓解。治疗相关的3级-4级不良事件(adverse events,AEs)包括2例肺炎、1例全身症状、1例结肠炎、1例肾上腺皮质功能不全、1例高血糖症和1例低钾血症。6例(14%)患者发生了治疗相关的严重不良事件,包括肺炎、急性肾损伤、低钾血症和肾上腺皮质功能不全。没有观察到治疗相关死亡病例。结论帕博利珠单抗治疗PD-L1≥1%的NSCLC伴脑转移患者有效,且对所有纳入的未经治疗的脑转移患者安全。需要进一步探索免疫治疗用于NSCLC合并CNS转移。

关键词：非小细胞肺癌脑转移免疫治疗帕博利珠单抗

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PS2-108. Infection with Hepatitis C down-regulates the expression of cytokine genes in peripheral blood

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Cytokine 2011年第1期56卷 94-94页

作者： Milton W. Taylor Christopher R. Bolen Steven H. Kleinstein Leonid Brodsky Department of Biology Indiana University Bloomington IN 47401 USA 1Interdepartmental Program in Computational Biology and Bioinformatics Yale University 300 George St. Suite 501 New Haven CT 06511 USA Department of Pathology Yale University School of Medicine New Haven CT USA Institute of Evolution University of Haifa Mount Carmel Haifa 31905 Israel

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Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Research Square

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Research Square 2021年

作者： Freedman, Matthew L. Baca, Sylvan C. Singler, Cassandra Zacharia, Soumya Seo, Ji-Heui Morova, Tunc Hach, Faraz Ding, Yi Schwarz, Tommer Huang, Chia-Chi Flora Kalita, Cynthia Groha, Stefan Pomerantz, Mark M. Wang, Victoria Linder, Simon Sweeney, Christopher J. Zwart, Wilbert Lack, Nathan A. Pasaniuc, Bogdan Takeda, David Y. Gusev, Alexander Department of Medical Oncology Dana-Farber Cancer Institute BostonMA United States Center for Functional Cancer Epigenetics Dana-Farber Cancer Institute BostonMA United States The Eli and Edythe L. Broad Institute CambridgeMA United States Laboratory of Genitourinary Cancer Pathogenesis Center for Cancer Research National Cancer Institute BethesdaMD United States Vancouver Prostate Centre University of British Columbia VancouverBC Canada Bioinformatics Interdepartmental Program UCLA Los AngelesCA United States Division of Genetics Brigham & Women’s Hospital BostonMA United States Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute BostonMA United States Department of Biostatistics Harvard School of Public Health BostonMA United States Division of Oncogenomics Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Laboratory of Chemical Biology Institute for Complex Molecular Systems Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Medicine Koç University Istanbul Turkey Department of Computational Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation. © 2021, CC BY.

关键词： Transcription

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Technology dictates algorithms: Recent developments in read alignment

arXiv

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arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin D. Balliu, Brunilda Koslicki, David Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： bioinformatics

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

关键词：

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Correction to: Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes

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Genome medicine 2021年第1期13卷 139页

作者： David Z Pan Zong Miao Caroline Comenho Sandhya Rajkumar Amogha Koka Seung Hyuk T Lee Marcus Alvarez Dorota Kaminska Arthur Ko Janet S Sinsheimer Karen L Mohlke Nicholas Mancuso Linda Liliana Muñoz-Hernandez Miguel Herrera-Hernandez Maria Teresa Tusié-Luna Carlos Aguilar-Salinas Kirsi H Pietiläinen Jussi Pihlajamäki Markku Laakso Kristina M Garske Päivi Pajukanta Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. Bioinformatics Interdepartmental Program UCLA Los Angeles USA. Computational and Systems Biology Interdepartmental Program UCLA Los Angeles USA. Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland. Department of Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Computational Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Center for Genetic Epidemiology Department of Preventative Medicine Keck School of Medicine University of Southern California Los Angeles USA. Tecnologico de Monterrey Escuela de Medicina y Ciencias de la Salud Ave. Morones Prieto 3000 Monterrey N.L. México 64710. Unidad de Investigación de Enfermedades Metabólicas Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Cirugía Instituto Nacional de Ciencias Médicas y Nutrición Mexico City Mexico. Unidad de Biología Molecular y Medicina Genómica Instituto de Investigaciones Biomédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Obesity Research Unit Research Program for Clinical and Molecular Metabolism Faculty of Medicine University of Helsinki Helsinki Finland. Obesity Center Endocrinology Abdominal Center Helsinki University Central Hospital and University of Helsinki Helsinki Finland. Department of Medicine Endocrinology and Clinical Nutrition Kuopio University Hospital Kuopio Finland. Department of Medicine University of Eastern Finland and Kuopio University Hospital Kuopio Finland. Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. ppajukant

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