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A deep-learning algorithm to classify skin lesions from mpox virus infection

NATURE MEDICINE 2023年第3期29卷 738-+页

作者： Thieme, Alexander H. H. Zheng, Yuanning Machiraju, Gautam Sadee, Chris Mittermaier, Mirja Gertler, Maximilian Salinas, Jorge L. Srinivasan, Krithika Gyawali, Prashnna Carrillo-Perez, Francisco Capodici, Angelo Uhlig, Maximilian Habenicht, Daniel Loeser, Anastassia Kohler, Maja Schuessler, Maximilian Kaul, David Gollrad, Johannes Ma, Jackie Lippert, Christoph Billick, Kendall Bogoch, Isaac Hernandez-Boussard, Tina Geldsetzer, Pascal Gevaert, Olivier Stanford Univ Dept Med Stanford CA 94305 USA Stanford Univ Stanford Ctr Biomed Informat Res BMIR Dept Biomed Data Sci Stanford CA 94305 USA Charite Univ Med Berlin Dept Radiat Oncol Berlin Germany Charite Univ Med Berlin Berlin Inst Hlth BIH Biomed Innovat Acad BIH Charite Digital Clinician Scientist Program Berlin Germany Stanford Univ Dept Biomed Data Sci Stanford CA USA Charite Univ Med Berlin Dept Infect Dis & Resp Med Berlin Germany Charite Univ Med Berlin Inst Trop Med & Int Hlth Berlin Germany Stanford Univ Dept Med Div Infect Dis & Geog Med Stanford CA USA Univ Granada Dept Architecture & Comp Technol ATC Granada Spain Univ Bologna Dept Biomed & Neuromotor Sci Alma Mater Studiorum Bologna Italy Justus Liebig Univ Giessen Dept Med Giessen Germany Tech Univ Berlin Berlin Germany Univ Med Ctr Schleswig Holstein Dept Radiotherapy Lubeck Germany Heidelberg Univ Hosp Heidelberg Inst Global Hlth Heidelberg Germany Univ Basel Ctr Cognit & Decis Sci Dept Psychol Basel Switzerland Fraunhofer Heinrich Hertz Inst Dept Artificial Intelligence Berlin Germany Univ Potsdam Hasso Plattner Inst Digital Hlth & Machine Learning Potsdam Germany Icahn Sch Med Mt Sinai Hasso Plattner Inst Digital Hlth Mt Sinai New York NY USA Univ Hlth Network Toronto Western Hosp Div Dermatol Toronto ON Canada Univ Hlth Network Toronto Gen Hosp Div Infect Dis Toronto ON Canada Stanford Univ Dept Surg Stanford CA USA Stanford Univ Dept Med Div Primary Care & Populat Hlth Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

A deep-learning algorithm was developed to identify skin lesions caused by the mpox virus and was then implemented in a web-based app designed for patient use. Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.

关键词： Public health Viral infection

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Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations

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EPMA JOURNAL 2020年第3期11卷 505-515页

作者： Goldstein, Elisha Yeghiazaryan, Kristina Ahmad, Ashar Giordano, Frank A. Froehlich, Holger Golubnitschaja, Olga Weizmann Inst Sci Dept Bioinformat Machine Learning Res Grp Rehovot Israel Rheinische Friedrich Wilhelms Univ Bonn State NRW Israel Program Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn IT Dept Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat AI & Data Sci D-53754 St Augustin Germany Rheinische Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr IT D-53115 Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn Dept Radiat Oncol Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn Dept Radiat Oncol Predict Prevent & Personalised 3P Med Venusberg Campus 1 D-53127 Bonn Germany

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from "just end of the life" care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, "comet assay" patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin-all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of para

关键词： Predictive preventive personalised medicine (PPPM 3 PM) Liver diseases Liver malignancy Palliative medicine Unsupervised machine learning Multiparametric modelling Patient stratification Hepatocellular carcinoma Colorectal cancer Breast cancer Prostate cancer Metastasis Liquid biopsy Ex vivo Circulating leucocytes Multi-omics Biomarker patterns Multi-level diagnostics Survival Prognosis Metalloproteinase Comet assay Calgranulin A S100 Catalase Superoxide-dismutase 2 SOD-2 Profilin Rho A Thioredoxin Scavenger Antioxidant compounds ROS inhibition Selective internal radiation therapy (SIRT) Trans-arterial chemo-embolisation (TACE) Individual outcomes Covid-19 Viral infection Hepatitis Hypoxia Inflammation Detoxification Impairments Individualised patient profile Redox status Genoprotection Phytochemicals Redox-based therapy Risk mitigation

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BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences

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CELL SYSTEMS 2023年第6期14卷 525-+页

作者： Valeri, Jacqueline A. Soenksen, Luis R. Collins, Katherine M. Ramesh, Pradeep Cai, George Powers, Rani Angenent-Mari, Nicolaas M. Camacho, Diogo M. Wong, Felix Lu, Timothy K. Collins, James J. MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biolog Inspired Engn Boston MA 02115 USA Broad Inst & Harvard Cambridge MA 02142 USA MIT Dept Mech Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci 77 Massachusetts Ave Cambridge MA 02139 USA Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Pluto Biosci Golden CO 80402 USA MIT Res Lab Elect Synthet Biol Grp Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth Cambridge MA 02139 USA

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automat-ically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By auto-mating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

关键词： automated machine learning architecture search hyperparameter optimization biological sequences

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learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization

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PLOS COMPUTATIONAL BIOLOGY 2019年第1期15卷 e1006199-e1006199页

作者： Majarian, Timothy D. Murphy, Robert F. Lakdawala, Seema S. Carnegie Mellon Univ Computat Biol Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci 4400 5th Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Pittsburgh Sch Med Dept Microbiol & Mol Genet Pittsburgh PA 15260 USA Broad Inst Program Med & Populat Genet Cambridge MA USA

Within influenza virus infected cells, viral genomic RNA are selectively packed into progeny virions, which predominantly contain a single copy of 8 viral RNA segments. Intersegmental RNA-RNA interactions are thought to mediate selective packaging of each viral ribonucleoprotein complex (vRNP). Clear evidence of a specific interaction network culminating in the full genomic set has yet to be identified. Using multi-color fluorescence in situ hybridization to visualize four vRNP segments within a single cell, we developed image-based models of vRNP-vRNP spatial dependence. These models were used to construct likely sequences of vRNP associations resulting in the full genomic set. Our results support the notion that selective packaging occurs during cytoplasmic transport and identifies the formation of multiple distinct vRNP sub-complexes that likely form as intermediate steps toward full genomic inclusion into a progeny virion. The methods employed demonstrate a statistically driven, model based approach applicable to other interaction and assembly problems.

关键词： INFLUENZA A virus FLUORESCENCE HYBRIDIZATION RNA NUCLEIC acids TRANSCRIPTOMES NUCLEOPROTEINS

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Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

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CELL REPORTS METHODS 2023年第8期3卷 100565页

作者： Pietiainen, Vilja Polso, Minttu Migh, Ede Guckelsberger, Christian Harmati, Maria Diosdi, Akos Turunen, Laura Hassinen, Antti Potdar, Swapnil Koponen, Annika Sebestyen, Edina Gyukity Kovacs, Ferenc Kriston, Andras Hollandi, Reka Burian, Katalin Terhes, Gabriella Visnyovszki, Adam Fodor, Eszter Lacza, Zsombor Kantele, Anu Kolehmainen, Pekka Kakkola, Laura Strandin, Tomas Levanov, Lev Kallioniemi, Olli Kemeny, Lajos Julkunen, Ilkka Vapalahti, Olli Buzas, Krisztina Paavolainen, Lassi Horvath, Peter Hepojoki, Jussi Univ Helsinki Inst Mol Med Finland FIMM Helsinki Inst Life Sci HiLIFE Helsinki Finland Biol Res Ctr Inst Biochem Lab Microscop Image Anal & Machine Learning Szeged Hungary Aalto Univ Dept Comp Sci Espoo Finland Finnish Ctr Artificial Intelligence Espoo Finland Queen Mary Univ London Sch Elect Engn & Comp Sci London England Minerva Fdn Inst Med Res Helsinki Finland Univ Helsinki Fac Med Dept Anat Helsinki Finland Single Cell Technol Ltd Szeged Hungary Univ Szeged Fac Med Dept Med Microbiol Szeged Hungary Univ Szeged Fac Med Dept Internal Med 1 Szeged Hungary Univ Phys Educ Inst Sports & Hlth Sci Dept Sports Physiol Budapest Hungary Univ Helsinki Meilahti Infect Dis & Vaccine Res Ctr MeiVac Helsinki Finland Helsinki Univ Hosp Helsinki Finland Univ Helsinki Human Microbiome Res Program Helsinki Finland Univ Turku Inst Biomed Turku Finland Univ Helsinki Medicum Dept Virol Helsinki Finland Karolinska Inst Dept Oncol Pathol Sci Life Lab Stockholm Sweden Univ Szeged Dept Dermatol & Allergol HCEMM USZ Skin Res Grp Szeged Hungary Turku Univ Hosp Turku Finland Univ Helsinki Dept Vet Biosci Helsinki Finland Helsinki Univ Hosp HUS Diagnost Ctr HUSLAB Clin Microbiol Helsinki Finland Univ Szeged Fac Med Fac Sci & Informat Dept Immunol Szeged Hungary Univ Zurich Inst Vet Pathol Vetsuisse Fac Zurich Switzerland

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.

关键词： antibody response COVID-19 high-content imaging immunofluorescence assay machine learning SARS-CoV-2 serology virus cell-based assay mini-IFA CP: Microbiology CP: Imaging

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DeePN2: A deep learning-based non-Newtonian hydrodynamic model

arXiv

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arXiv 2021年

作者： Fang, Lidong Ge, Pei Zhang, Lei Weinan, E. Lei, Huan Department of Computational Mathematics Science and Engineering Michigan State University MI48824 United States School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing100080 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Statistics and Probability Michigan State University MI48824 United States

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN2, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention. © 2021, CC BY.

关键词： Non Newtonian flow

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers machine learning Random Forest

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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

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NATURE COMMUNICATIONS 2024年第1期15卷 1619页

作者： Martindale, Alexander P. L. Ng, Benjamin Ngai, Victoria Kale, Aditya U. di Ruffano, Lavinia Ferrante Golub, Robert M. Collins, Gary S. Moher, David McCradden, Melissa D. Oakden-Rayner, Lauren Rivera, Samantha Cruz Calvert, Melanie Kelly, Christopher J. Lee, Cecilia S. Yau, Christopher Chan, An-Wen Keane, Pearse A. Beam, Andrew L. Denniston, Alastair K. Liu, Xiaoxuan Brighton & Sussex Med Sch Brighton England Birmingham & Midland Eye Ctr Sandwell & West Birmingham NHS Trust Birmingham England Univ Oxford Christ Church Oxford England UCL Med Sch London England Univ Birmingham Inst Inflammat & Ageing Birmingham England Univ Hosp Birmingham NHS Fdn Trust Birmingham England Univ Birmingham Natl Inst Hlth & Care Res NIHR Birmingham Biomed Res Ctr Birmingham England Univ York York Hlth Econ Consortium York England Northwestern Univ Feinberg Sch Med Chicago IL USA Univ Oxford Ctr Stat Med UK EQUATOR Ctr Nuffield Dept Orthopaed Rheumatol & Musculoskelet Oxford England Ottawa Hosp Res Inst Ctr Journalol Clin Epidemiol Program Ottawa ON Canada Hosp Sick Children Dept Bioeth Toronto ON Canada Peter Gilgan Ctr Res & Learning Genet & Genome Biol Res Program Toronto ON Canada Dalla Lana Sch Publ Hlth Div Clin & Publ Hlth Toronto ON Canada Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ Birmingham Birmingham Hlth Partners Ctr Regulatory Sci & Inn Birmingham England Univ Birmingham Inst Appl Hlth Res Coll Med & Dent Sci Ctr Patient Reported Outcomes Res CPROR Birmingham England Univ Birmingham NIHR Appl Res Collaborat ARC West Midlands Birmingham England Univ Birmingham NIHR Blood & Transplant Res Unit BTRU Precis Tran Birmingham England Google Hlth London England Univ Washington Seattle WA USA Univ Oxford Nuffield Dept Womens & Reprod Hlth Oxford England Hlth Data Res UK London England Univ Toronto Womens Coll Hosp Dept Med Toronto ON Canada Moorfields EyeHospital NHS Fdn Trust NIHR Biomed Res Ctr Moorfields London England UCL Inst Ophthalmol London England Harvard TH Chan Sch Publ Hlth Dept Epidemiol Boston MA USA Harvard Med Sch Dept Biomed Informat Boston MA USA

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines. The CONSORT-AI extension was developed to provide specific guidance for randomised controlled trials involving Artificial Intelligence (AI) interventions. Here, the authors show that since publication of CONSORT-AI, several AI-specific considerations remain systematically underreported.

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Increasing digital mental health reach and uptake via youth partnerships

Npj mental health research

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Npj mental health research 2023年第1期2卷 9-9页

作者： Colleen Stiles-Shields Giovanni Ramos Adrian Ortega Alexandra M Psihogios Institute for Juvenile Research Department of Psychiatry College of Medicine University of Illinois Chicago Chicago IL USA. Center for Health Equity using Machine Learning & Artificial Intelligence College of Medicine University of Illinois Chicago Chicago IL USA. Montefiore Medical Center University of California Los Angeles Los Angeles CA USA. Clinical Child Psychology Program University of Kansas Lawrence KS USA. Department of Medical Social Sciences Northwestern University Chicago IL USA.

Youth in the United States are facing an unprecedented mental health crisis. Yet, brick-and-mortar mental healthcare, such as face-to-face therapy, is overwhelmingly inaccessible to youth despite research advances in youth mental health. Digital Mental Health tools (DMH), the use of technologies to deliver mental health assessments and interventions, may help to increase mental healthcare accessibility. However, for a variety of reasons, evidence-based DMH have not been successful in reaching youth in real-world settings, particularly those who are most encumbered with access barriers to mental healthcare. This Comment therefore focuses on increasing DMH reach and uptake by young people, particularly among minoritized youth, by engaging in community-based youth partnerships. This idea recognizes and grows from decades' worth of community-based participatory research and youth partnerships successfully conducted by other disciplines (e.g., social work, public health, urban planning, education). Increasing uptake and engagement is an issue that is unlikely to be solved by adult-driven theory and design. As such, we emphasize the necessity of reframing youth input into DMH design and deployment from one-time participants to integral community-based partners. Indeed, recognizing and valuing their expertise to equitably address DMH implementation challenges, youth should help to pose the very questions that they will help to answer throughout the design and implementation planning for DMH moving forward.

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Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer

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COMMUNICATIONS BIOLOGY 2022年第1期5卷 1-13页

作者： Rezaie, Narges Bayati, Masroor Hamidi, Mehrab Tahaei, Maedeh Sadat Khorasani, Sadegh Lovell, Nigel H. Breen, James Rabiee, Hamid R. Alinejad-Rokny, Hamid Univ Calif Irvine Ctr Complex Biol Syst Irvine CA 92697 USA Sharif Univ Technol Dept Comp Engn Bioinformat & Computat Biol Lab Tehran 11365 Iran UNSW Sydney Tyree Inst Hlth Engn Sydney NSW 2052 Australia UNSW Sydney Grad Sch Biomed Engn Sydney NSW 2052 Australia South Australian Hlth & Med Res Inst Adelaide SA 5000 Australia Univ Adelaide Robinson Res Inst Adelaide SA 5006 Australia Univ Adelaide Bioinformat Hub Adelaide SA 5006 Australia UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ Hlth Data Analyt Program Al Eenabled Proc AIP Res Ctr Sydney NSW 2109 Australia

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www. ***/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

关键词： Data integration Breast cancer

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