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Title:
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction | Genome Biology
Description:
Background Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
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Keywords {š}
models, rnaseq, gene, expression, prediction, clinical, neuroblastoma, pubmed, article, data, google, scholar, genes, additional, file, cas, performances, analysis, endpoint, cancer, microarrays, endpoints, patients, microarray, research, usa, microarraybased, validation, model, university, aceview, fig, degs, features, center, information, profiles, reads, performance, study, transcript, genome, results, predictive, figure, central, expressed, expressionbased, table, based,
Topics {āļø}
retrospective siopen/cog/gpoh study gov/repository/acedb/software/magic gene expression-based classifiers gene expression profiling fƶrdergesellschaft kinderkrebs-neuroblastom-forschung gene expression-based prediction gene expression-based classification high-throughput shotgun sequencing bldg 38a/room 8s808 danielle thierry-mieg major clinico-genetic subtypes microarray-based expression data rt-qpcr-based assays [6 gene-level analysis failed prognostic multi-gene signature expression profiling technology color gene-expression analyses article download pdf comprehensive cdna-supported gene jean thierry-mieg high-risk patient outcome outperform microarray-based models rna-seq expression data clinico-genetic neuroblastoma subgroups prognostic microrna/mrna signature kaplan-meier survival estimates rna-seq outperforms microarrays rna-seq-based classifiers rna-seq data compares microarray-based predictive models current high-risk patients gene expression analysis rna-seq-based models rna-seq based models rna-seq processing pipelines gene expression profiles internal cross-validation processes rna-seq processing pipeline rna-seq data processing supplementary figures s1ās13 supplementary tables s1ās9 raw rna-seq data gene expression measurements gene expression levels gene expression models transcript-level expression values maqc-iii project utilizing expression-based classifiers complex expression patterns expression profiling
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headline:Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
description:Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
datePublished:2015-06-25T00:00:00Z
dateModified:2015-06-25T00:00:00Z
pageStart:1
pageEnd:12
license:http://creativecommons.org/licenses/by/4.0/
sameAs:https://doi.org/10.1186/s13059-015-0694-1
keywords:
Overall Survival
Neuroblastoma
Prediction Performance
Clinical Endpoint
Gene Expression Model
Animal Genetics and Genomics
Human Genetics
Plant Genetics and Genomics
Microbial Genetics and Genomics
Bioinformatics
Evolutionary Biology
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headline:Comparison of RNA-seq and microarray-based models for clinical endpoint prediction
description:Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.
datePublished:2015-06-25T00:00:00Z
dateModified:2015-06-25T00:00:00Z
pageStart:1
pageEnd:12
license:http://creativecommons.org/licenses/by/4.0/
sameAs:https://doi.org/10.1186/s13059-015-0694-1
keywords:
Overall Survival
Neuroblastoma
Prediction Performance
Clinical Endpoint
Gene Expression Model
Animal Genetics and Genomics
Human Genetics
Plant Genetics and Genomics
Microbial Genetics and Genomics
Bioinformatics
Evolutionary Biology
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type:PostalAddress
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address:
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type:PostalAddress
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name:Zirui Dong
affiliation:
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone
address:
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
type:PostalAddress
type:Organization
name:Ke K. Zhang
affiliation:
name:University of North Dakota School of Medicine
address:
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type:PostalAddress
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name:Ye Yin
affiliation:
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone
address:
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
type:PostalAddress
type:Organization
name:Chen Zhao
affiliation:
name:Fudan University
address:
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
type:PostalAddress
type:Organization
name:Yuanting Zheng
affiliation:
name:Fudan University
address:
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name:Russell D. Wolfinger
affiliation:
name:SAS Institute Inc.
address:
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type:PostalAddress
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name:Tieliu Shi
affiliation:
name:East China Normal University, Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences
address:
name:East China Normal University, Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, Shanghai, China
type:PostalAddress
type:Organization
name:Linda H. Malkas
affiliation:
name:Beckman Research Institute, City of Hope Comprehensive Cancer Center
address:
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type:Organization
name:Frank Berthold
affiliation:
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address:
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
type:PostalAddress
type:Organization
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty
address:
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
type:PostalAddress
type:Organization
name:Jun Wang
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address:
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type:PostalAddress
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name:University of Copenhagen
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name:King Abdulaziz University
address:
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type:Organization
name:University of Copenhagen
address:
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affiliation:
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address:
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type:PostalAddress
type:Organization
name:Leming Shi
affiliation:
name:Fudan University
address:
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type:PostalAddress
type:Organization
name:National Center for Toxicological Research, U.S. Food and Drug Administration
address:
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
type:PostalAddress
type:Organization
email:[email protected]
name:Zhiyu Peng
affiliation:
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address:
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type:PostalAddress
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name:BGI-Guangzhou, Guangzhou Higher Education Mega Center
address:
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type:PostalAddress
type:Organization
email:[email protected]
name:Matthias Fischer
affiliation:
name:University Childrenās Hospital of Cologne
address:
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
type:PostalAddress
type:Organization
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty
address:
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
type:PostalAddress
type:Organization
email:[email protected]
PostalAddress:
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
name:NIH/NCBI, Bethesda, USA
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:NIH/NCBI, Bethesda, USA
name:Eli Lilly and Company Research Informatics, Lilly Corporate Center, Indianapolis, USA
name:Fondazione Bruno Kessler (FBK), Trento Povo, Italy
name:AbbVie Inc., Global Pharmaceutical R&D, Souderton, USA
name:GlaxoSmithKline, Discovery Analytics, Collegeville, USA
name:Department of Internal Medicine, Rush University Cancer Center, Chicago, USA
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
name:SAS Institute Inc., Cary, USA
name:Marshfield Clinic Research Foundation, Biomedical Informatics Research Center, Marshfield, USA
name:Thomson Reuters IP & Science, Carlsbad, USA
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:Center for Genomics and Division of Microbiology & Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, USA
name:Department of Biomedical Engineering, GeorgiaTech and Emory University, Atlanta, USA
name:Department of Internal Medicine, Rush University Cancer Center, Chicago, USA
name:Fondazione Edmund Mach, CRI-CBC, San Michele allāAdige, Italy
name:Childrenās Hospital Los Angeles, Los Angeles, USA
name:Department of Pediatric Hematology-Oncology, Molecular Oncology, Felsenstein Medical Research Center, Schneider Childrenās Medical Center of Israel, Petach Tikva, Israel
name:SAS Institute Inc., Cary, USA
name:Thomson Reuters IP & Science, Carlsbad, USA
name:Marshfield Clinic Research Foundation, Center of Human Genetics, Marshfield, USA
name:Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
name:Fondazione Bruno Kessler (FBK), Trento Povo, Italy
name:SAS Institute Inc., Cary, USA
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:University of Nottingham, Childrenās Brain Tumour Research Centre, Queenās Medical Centre, University of Nottingham, Nottingham, UK
name:Marshfield Clinic Research Foundation, Biomedical Informatics Research Center, Marshfield, USA
name:Marshfield Clinic Research Foundation, Center of Human Genetics, Marshfield, USA
name:Department of Internal Medicine, Rush University Cancer Center, Chicago, USA
name:Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, WOC1 RM400S, HFM-210, Rockville, USA
name:Thomson Reuters IP & Science, Carlsbad, USA
name:Department of Internal Medicine, Rush University Cancer Center, Chicago, USA
name:AbbVie Inc., Global Pharmaceutical Research and Development, North Chicago, USA
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:University of Arkansas at Little Rock, UALR/UAMS Joint Bioinformatics Graduate Program, Little Rock, USA
name:Eli Lilly and Company, Discovery Statistics, Lilly Corporate Center, Indianapolis, USA
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Department of Pathology, University of Valencia, Medical School, Valencia, Spain
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
name:Department of Translational Genomics, University of Cologne, Cologne, Germany
name:Department of Biomedical Engineering, GeorgiaTech and Emory University, Atlanta, USA
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:SAS Institute Inc., Cary, USA
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:Neuroblastoma Laboratory, Onco/Hematology Laboratory, SDB Department, University of Padua, Pediatric Research Institute, Padua, Italy
name:Department of Pediatrics and Genetics, Ghent University, Center for Medical Genetics, Ghent University, Ghent, Belgium
name:Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, USA
name:Harvard Medical School, Massachusetts General Hospital, Boston, USA
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Stanford University, Stanford Genome Technology Center, Palo Alto, USA
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Eli Lilly and Company Research Informatics, Lilly Corporate Center, Indianapolis, USA
name:Marshfield Clinic Research Foundation, Biomedical Informatics Research Center, Marshfield, USA
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:Department of Pathology, University of North Dakota School of Medicine, Grand Forks, USA
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
name:SAS Institute Inc., Cary, USA
name:East China Normal University, Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, Shanghai, China
name:Department of Molecular & Cellular Biology, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, USA
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:Department of Biology, University of Copenhagen, Copenhagen, Denmark
name:King Abdulaziz University, Jeddah, Saudi Arabia
name:Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, China
name:National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
name:BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Shenzhen, China
name:BGI-Guangzhou, Guangzhou Higher Education Mega Center, Guangzhou, China
name:Department of Pediatric Oncology and Hematology, University Childrenās Hospital of Cologne, Cologne, Germany
name:University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Cologne, Germany
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- Income figures for https://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Shahab%20Asgharzadeh%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en
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- https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Richard%20G.%20Grundy's revenue stream
- What's the total monthly financial gain of https://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Richard%20G.%20Grundy%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en?
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- https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Yan%20Li income
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- What is the monthly revenue of https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Xin%20X.%20Lu?
- How much does https://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Xin%20X.%20Lu%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en net monthly?
- https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Heng%20Luo's total income per month
- Monthly income for https://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Heng%20Luo%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en
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- Learn how profitable https://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Xiwen%20Ma%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en is on a monthly basis
- https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Baitang%20Ning's total income per month
- Revenue of https://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Baitang%20Ning%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en
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- How much does https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Martin%20Peifer rake in every month?
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