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HPV-CCDC106 integration alters local chromosome architecture and hijacks an enhancer by three-dimensional genome structure remodeling in cervical cancer

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Journal of Genetics and Genomics 2020年第8期47卷 435-448页

作者： Canhui Cao Ping Hong Xingyu Huang Da Lin Gang Cao Liming Wang Bei Feng Ping Wu Hui Shen Qian Xu Ci Ren Yifan Meng Wenhua Zhi Ruidi Yu Juncheng Wei Wencheng Ding Xun Tian Qinghua Zhang Wei Li Qinglei Gao Gang Chen Kezhen Li Wing-Kin Sung Zheng Hu Hui Wang Guoliang Li Peng Wu Department of Gynecologic Oncology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Cancer Biology Research Center(Key Laboratory of the Ministry of Education) Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan430070China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big DataCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China State Key Laboratory of Agricultural Microbiology Huazhong Agricultural UniversityWuhan430070China Bio-Medical Center Huazhong Agricultural UniversityWuhan430070China College of Veterinary Medicine Huazhong Agricultural UniversityWuhan430070China Department of Obstetrics and Gynecology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China Department of Obstetrics and Gynecology Key Laboratory for Molecular Diagnosis of Hubei ProvinceThe Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430010China Department of Computational and Systems Biology Genome Institute of SingaporeSingapore Department of Gynecological Oncology The First Affiliated Hospital of Sun Yat-sen UniversityGuangzhou510080China

Integration of human papillomavirus(HPV)DNA into the human genome is a reputed key driver of cervical ***,the effects of HPV integration on chromatin structural organization and gene expression are largely *** studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome *** then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome,RNA,chromatin immunoprecipitation and high-throughput chromosome conformation capture(Hi-C)sequencing to identify the mechanisms of HPV integration in cervical *** analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities,with correlation found between three-dimensional(3D)structural change and gene ***,HPV integration divided one topologically associated domain(TAD)into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106,with a decrease in PEG3 expression and an increase in CCDC106 *** expression dysregulation was further confirmed using 10 samples from our cohort,which exhibited the same HPV-CCDC106 *** summary,we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure ***,this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.

关键词： Cervical cancer HPV integration Fusion gene Hi-C Enhancer TAD

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Deep reinforcement learning for large-scale epidemic control

arXiv

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arXiv 2020年

作者： Libin, Pieter Moonens, Arno Verstraeten, Timothy Perez-Sanjines, Fabian Hens, Niel Lemey, Philippe Nowé, Ann Artificial Intelligence Lab Department of Computer Science Vrije Universiteit Brussel Brussels Belgium KU Leuven University of Leuven Department of Microbiology and Immunology Rega Institute for Medical Research Clinical and Epidemiological Virology Leuven Belgium Interuniversity Institute of Biostatistics and Statistical Bioinformatics Data Science Institute Hasselt University Hasselt Belgium

Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epidemics are non-linear and complex processes. For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza. Firstly, we construct a new epidemiological meta-population model, with 379 patches (one for each administrative district in Great Britain), that adequately captures the infection process of pandemic influenza. Our model balances complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable. Secondly, we set up a ground truth such that we can evaluate the performance of the "Proximal Policy Optimization" algorithm to learn in a single district of this epidemiological model. Finally, we consider a large-scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established. This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space. Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Author Correction: mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis

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Genome biology 2023年第1期24卷 84页

作者： Yanyan Zeng Jing Li Chaochun Wei Hongyu Zhao Tao Wang Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University Shanghai China. Department of Biostatistics Yale University New Haven CT USA. hongyu.zhao@yale.edu. SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University Shanghai China. hongyu.zhao@yale.edu. Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University Shanghai China. neowangtao@***. SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University Shanghai China. neowangtao@***. Department of Statistics School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China. neowangtao@***. Joint International Research Laboratory of Metabolic & Developmental Sciences Shanghai Jiao Tong University Shanghai China. neowangtao@***.

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Auto score-survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

arXiv

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arXiv 2021年

作者： Xie, Feng Ning, Yilin Yuan, Han Goldstein, Benjamin Alan Ong, Marcus Eng Hock Liu, Nan Chakraborty, Bibhas Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Statistics and Applied Probability National University of Singapore Singapore Singapore

Background: Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on clinician's knowledge, suggesting an unmet need for a robust and efficient generic score-generating method. Methods: AutoScore was previously developed as an interpretable machine learning score generator, integrated both machine learning and point-based scores in the strong discriminability and accessibility. We have further extended it to time-to-event data and developed AutoScore-Survival, for automatically generating time-to-event scores with right-censored survival data. Random survival forest provides an efficient solution for selecting variables, and Cox regression was used for score weighting. We implemented our proposed method as an R package. We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i.e., Cox) and the random survival forest. Results: The AutoScore-Survival-derived scoring model was more parsimonious than survival models built using traditional variable selection methods (e.g., penalized likelihood approach and stepwise variable selection), and its performance was comparable to survival models using the same set of variables. Although AutoScore-Survival achieved a comparable integrated area under the curve of 0.782 (95% CI: 0.767-0.794), the integer-valued time-to-event scores generated are favorable in clinical applications because they are easier to compute and interpret. Conclusions: Our proposed AutoScore-Survival provides an automated, robust and easy-to-use machine learning-based clinical score generator to studies of time-to-event outcomes. It provides a systematic guideline to facilitate the future development of time-to-event scores for clinical applications. © 2021, CC BY.

关键词： Intensive care units

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Climate-influenced vector-borne diseases in Africa:a call to empower the next generation of African researchers for sustainable solutions

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Infectious Diseases of Poverty 2024年第2期13卷 83-92页

作者： Judicaël Obame-Nkoghe Adjoavi Esse Agossou Gerald Mboowa Basile Kamgang Cyril Caminade Dawn C.Duke Andrew Karanja Githeko Obed M.Ogega Nestor Engone Elloué Fatou Bintou Sarr DieudonnéNkoghe Pierre Kengne Nicaise T.Ndam Christophe Paupy Moses Bockarie Patricks Voua Otomo Molecular and Cellular Biology Laboratory(LabMC) Biology DepartmentUniversity of Science and Technology of MasukuBP 901FrancevilleGabon Ecology and Health Research Unit Interdisciplinary Center for Medical Research of FrancevilleBP 769FrancevilleGabon Department of Zoology and Entomology Faculty of Natural and Agricultural SciencesUniversity of the Free StatePrivate Bag x13Phuthaditjhaba 9866Republic of South Africa Laboratory of Pharmacology and Improved Traditional Medicines Department of Animal PhysiologyFaculty of Science and TechnologyUniversity of Abomey-CalaviBP 526CotonouBenin The African Center of Excellence in Bioinformatics and Data-Intensive Sciences Infectious Diseases InstituteCollege of Health SciencesMakerere UniversityP.O Box 22418KampalaUganda Africa Centers for Disease Control and Prevention African Union CommissionRoosevelt StreetP.O.Box 3243W21 K19 Addis AbabaEthiopia Centre for Research in Infectious Diseases P.O.Box 13591YaoundéCameroon Earth System Physics Department The Abdus Salam International Centre for Theoretical PhysicsTriesteItaly Africa Research Excellence Fund 99 Charterhouse StreetLondon EC1M 6HRUK Community Health Support Program P.O.Box 872Kisumu 40100Kenya The African Academy of Sciences NairobiKenya Center for Phylosophical Studies and Research(CERP) Omar Bongo University(UOB)BP 13131LibrevilleGabon UMRED Health Training and Research UnitUniversity of Iba Der Thiam of ThièsBP 967ThièsSenegal National Parasitic Diseases Control Program Ministry of HealthLibrevilleGabon MIVEGEC Univ.MontpellierCNRSIRDMontpellierFrance MERIT IRDParis CitéUniversity75006 ParisFrance Department of Parasitology Noguchi Memorial Institute for Medical ResearchUniversity of GhanaLG 54AccraGhana School of Community Health Sciences Njala UniversityBoSierra Leone

We look at the link between climate change and vector-borne diseases in low-and middle-income countries in *** large endemicity and escalating threat of diseases such as malaria and arboviral diseases,intensified by climate change,disproportionately affects vulnerable communities *** highlight the urgency of prioritizing research and development,advocating for robust scientific inquiry to promote adaptation strategies,and the vital role that the next generation of African research leaders will play in addressing these *** significant challenges such as funding shortages within countries,various pan-African-oriented funding bodies such as the African Academy of sciences,the Africa Research Excellence Fund,the Wellcome Trust,the *** Institutes of Health,and the Bill and Melinda Gates Foundation as well as initiatives such as the African Research Initiative for Scientific Excellence and the Pan-African Mosquito Control Association,have empowered(or are empowering)these researchers by supporting capacity building activities,including continental and global networking,skill development,mentoring,and African-led *** article underscores the urgency of increased national investment in research,proposing the establishment of research government agencies to drive evidence-based *** between governments and scientific communities,sustained by pan-African funding bodies,is *** these efforts,African nations are likely to enhance the resilience and adaptive capacity of their systems and communities by navigating these challenges effectively,fostering scientific excellence and implementing transformative solutions against climate-sensitive vector-borne diseases.

关键词： Climate change Vector-borne diseases Africa Environmental justice Research and development Pan-African funding initiatives

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Quantile regression on inactivity time

arXiv

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arXiv 2020年

作者： Balmert, Lauren C. Li, Ruosha Peng, Limin Jeong, Jong-Hyeon Feinberg School of Medicine Northwestern University Chicago United States Department of Biostatistics and Data Science University of Texas Health Science Center Huston United States Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Atlanta United States Department of Biostatistics Graduate School of Public Health University of Pittsburgh Pittsburgh United States

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical tests

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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Analysis of machine learning strategies for prediction of passing undergraduate admission test

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International Journal of Information Management data Insights 2022年第2期2卷

作者： Walid, Md. Abul Ala Ahmed, S.M. Masum Zeyad, Mohammad Galib, S. M. Saklain Nesa, Meherun Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU) Gopalganj 8100 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering and Technology (KUET) Khulna 9203 Bangladesh Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh (ABCD Laboratory Bangladesh) Chattogram 4226 Bangladesh Faculty of Engineering University of Mons (UMONS) Bd Dolez 31 Mons 7000 Belgium School of Engineering and Physical Sciences (EPS) Heriot-Watt University (HWU) EH14 4AS Scotland Edinburgh United Kingdom Department of Energy Engineering University of the Basque Country (UPV/EHU) Ingeniero Torres Quevedo Plaza 1 Biscay Bilbao 48013 Spain School of Science & Technology International Hellenic University (IHU) 14th km Thessaloniki – N. Moudania Thessaloniki Thermi 57001 Greece Department of Biomedical Engineering Khulna University of Engineering and Technology (KUET) Khulna 9203 Bangladesh

This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model. © 2022 The Author(s)

关键词： Adaboost Balanced dataset Machine Learning Precision Support Vector Machines (SVM)

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Landscape of Immune Cell States and Ecosystems in Patients with Myelodysplastic Syndrome to Refine Prognostic Assessment and Predict Treatment Response. a Study By i4MDS Consortium

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Blood 2024年 144卷 665-665页

作者： Elena Riva Michela Calvi Matteo Zampini Lorenzo Dall'Olio Alessandra Merlotti Antonio Russo Giulia Maggioni Lara Orlandi Alessandro Frigo Francesca Ficara Laura Crisafulli Elisabetta Sauta Saverio D'Amico Enrico Lugli Alessia Campagna Marta Ubezio Cristina Astrid Tentori Gabriele Todisco Luca Lanino Alessandro Buizza Matteo Giovanni Della Porta IRCCS Humanitas Research Hospital Rozzano Italy Department of Medical Biotechnologies and Translational Medicine University of Milan Milan Italy Department of Biomedical Sciences Humanitas University Milan Italy Data Science and Bioinformatics Laboratory IRCCS Institute of Neurological Sciences of Bologna Bologna Italy Department of Physics and Astronomy DIFA University of Bologna Bologna Italy IRCCS Istituto delle Scienze Neurologiche di Bologna Bologna Italy Institute for Genetic and Biomedical Research Milan Unit CNR Milan Italy Department of Biomedical Sciences Humanitas University Pieve Emanuele Italy

BACKGROUND. Biological heterogeneity in Myelodysplastic Syndromes (MDS) is partly driven by tumor-associated genomic lesions, but increasing evidence suggests that immune tumor microenvironment plays a significant role. However, dissecting these cell states and understanding their clinical relevance on a large scale remains challenging. AIMS. In this study, conducted by i4MDS consortium, we characterized the immune ecosystems in a prospective cohort of MDS patients. Specific objectives were: 1) to analyze the contribution of immune ecosystems in refining patients' prognosis; 2) to define specific immune profiles to predict probability of response to hypomethylating agents (HMA); and 3) to develop a panel for immune monitoring in clinical practice. METHODS. We prospectively studied 286 MDS patients at Humanitas Cancer Center Milan, Italy (evaluated at diagnosis and at multiple time points throughout the disease's natural history). T lymphocytes, Natural Killer (NK) and myeloid cells were evaluated in bone marrow (BM) and peripheral blood (PB) by extensive multi-color flow cytometry (PMID:38756352). Phenograph was used to analyze immune cell subset distribution and phenotype, while HDBSCAN identified clusters of patients with homogeneous immune features (defined as ecosystems). Each ecosystem was further characterized by integrating RNA-seq data from CD34+ progenitors. A DURAClone dry pre-formulated antibody panel was designed for implementation in the clinical work-up of patients. RESULTS. We identified five immune ecosystems in the BM, each characterized by varying functionality and maturation stages of immune cells. Two clusters exhibited features of a healthy-like immune system: one with an expanded pool of immature and plastic Naïve T cells and CD56bright NK cells (referred to as “Naïve” ecosystem); and another enriched with memory T cells and functionally activated T and NK cells, termed “Memory, activated”. The other three clusters showed progressive levels of

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