Jinmeng Jia
|
Postdoc Fellow, Department of Automation, MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, 100084, China.
Email: jiajinmeng@tsinghua.edu.cn
Tel: (+86) 18102022810
|
Objective
|
A highly motivated individual, trained in both mathematics and biology, with good programming skills and problem solving abilities. Focusing on the causative gene identification and genotype-phenotype correlation analysis for rare diseases with statistical methods and bioinformatics algorithms, clinical data standardization, multi-omics data integration based on text mining and scRNA-seq data analysis in CVDs.
|
Training Experience (recent 5 years)
|
Postdoc Fellew, Tsinghua University
|
2020.8-
|
Postdoc Fellew, Harvard Medical School
|
2019.10-2020.3，suspended (COVID-19)
|
PhD, East China Normal University
|
2016.9-2019.7
|
Major: Biochemistry and Molecular Biology
|
Advisor: Prof. Tieliu Shi
|
Courses: Biochemistry and Molecular Biology, Statistics, Molecular Genetics, Population Ecology
|
M.S, East China Normal University
|
2014.9-2016.7
|
Major: Biomedical Engineering
|
Advisor: Prof. Tieliu Shi
|
Courses: Machine Learning, Biochemistry, Discrete Mathematics, Mathematical Modeling, Biostatistics, Cellular Signal Transduction, Bioinformatics
|
Research Experience (recent 5 years)
|
Infer protein-protein interaction based on a hierarchical vector space model
|
2015-2017
|
•
|
Proposed a Hierarchical Vector Space Model (HVSM) for computing semantic similarity between different genes or their products, which enhances the basic vector space model by introducing the relation between GO terms.
|
•
|
Introduced the concept of a Certainty Factor to calibrate the semantic similarity based on the number of terms annotated to genes.
|
•
|
In charge of the study design, data analysis and manuscript writing.
|
|
|
Investigation of Rare Disease Mechanism based on Multi-omics data integration and Network Analysis
|
2016-2018
|
•
|
Established a standardized knowledge base of rare diseases by integrating multi-omics data.
|
•
|
Supplemented a large number of rare disease-phenotype associations by text-mining nearly 10 million articles in the MEDLINE database.
|
•
|
Built up gene-based disease network and phenotype based disease network for rare diseases and elaborated the role of disease networks in the study of disease molecular mechanisms, disease gene prediction, and clinical diagnostic assistance.
|
•
|
In charge of the study design, text mining, disease network building and manuscript writing.
|
|
|
Machine learning system to support phenotype-based rare disease diagnosis
|
2017-2018
|
•
|
Adopted both phenotypic similarity method and machine learning method to build up four diagnostic models to support rare disease diagnosis.
|
•
|
In charge of the study design, model construction and manuscript writing.
|
|
|
Guideline for the Minimal Information when reporting a proteomics/ metabolomics quality control experiment. (Cooperation Project with EBI)
|
2016-2019
|
•
|
Delivered a set of technical guidelines representing the minimal information required to report and sufficiently support assessment and interpretation of a proteomics/metabolomics experiment.
|
•
|
Built up CV (Controlled Vocabulary) for qcML.
|
•
|
Leading construction of MIAPE-QC (Manuscript is in preparation now).
|
|
|
Calculating phenotype similarity based on molecular function.
|
2017-2020
|
•
|
Quantified the similarity of disease phenotype and molecular function based on gene ontology.
|
|
•
|
Established a phenotypic network based on molecular function.
|
Skills
|
•
|
Programming languages: Proficient in R, Python, Shell.
|
•
|
Bioinformatics/ Data science: Expertise in text mining and natural language processing skills. Expertise in multi-level data integration and standardization, ontology/controlled vocabulary building. Expertise in the usage of machine learning and deep learning algorithms. Experienced in processing and analysis of array and NGS-based transcriptomic data using standard statistical analysis.
|
Academic Conference Attended
|
Poster Presentation
|
April 2015
|
The 8th International Biocuration Conference, Beijing, China.
|
April 2016
|
The 9th International Biocuration Conference, Geneva, Switzerland.
|
Oral Presentation
|
April 2016
|
The 9th International Biocuration Conference, Geneva, Switzerland.
|
April 2017
|
The 10th International Biocuration Conference, Stanford University, Palo Alto, USA.
|
May 2018
|
The HUPO-PSI Spring Meeting, Heidelberg, Germany.
|
Host Workshop
|
September 2016
|
The 15th Human Proteome Organization World Congress (HUPO 2016) Taipei, China.
|
September 2017
|
The 16th Human Proteome Organization World Congress (HUPO 2017) Dublin, Ireland.
|
Publications
|
Published
|
•
|
Jia, J., An, Z., Ming, Y., Guo, Y., Li, W., Liang, Y., Guo, D., Li, X., Tai, J., Chen, G. et al. (2018) eRAM: encyclopedia of rare disease annotations for precision medicine. Nucleic acids research, 46, D937-D943. (PMID:29106618)
|
•
|
Jia, J., An, Z., Ming, Y., Guo, Y., Li, W., Li, X., Liang, Y., Guo, D., Tai, J., Chen, G. et al. (2018) PedAM: a database for Pediatric Disease Annotation and Medicine. Nucleic acids research, 46, D977-D983. (PMID:29126123)
|
•
|
Jia, J., Wang R., An, Z. et al. (2018) RDAD: A machine learning system to support phenotype-based rare disease diagnosis. Frontiers in genetics, 9: 587. (PMID:30564269)
|
•
|
Jia, J., and Shi, T. (2017) Towards efficiency in rare disease research: what is distinctive and important? Science China. Life sciences, 60, 686-691. (PMID:28639105)
|
•
|
Zhang, J., Jia, K., Jia, J. and Qian, Y. (2018) An improved approach to infer protein-protein interaction based on a hierarchical vector space model. BMC bioinformatics, 19, 161. (PMID:29699476)
|
•
|
Fu, Y#., Jia, J#., Yue,L., Yang R., Guo Y., Ni X. and Shi, T. (2018) Systematically analyze the pathogenic variations for Acute Intermittent Porphyria. Frontiers in pharmacology. (PMID:31572191)
|
•
|
Shi, J., Ren, M., Jia, J., Tang, M., Guo Y., Ni X. and Shi, T. (2019) Genotype-Phenotype association analysis reveals new pathogenic factors for Osteogenesis imperfecta disease. Frontiers in pharmacology. (PMID:31680973)
|
•
|
Shi, J., Ren, M., Jia, J., Tang, M., Guo Y., Ni X. and Shi, T. (2020) Corrigendum: Genotype-Phenotype association analysis reveals new pathogenic factors for Osteogenesis imperfecta disease. Frontiers in pharmacology. (PMID:32116662)
|
•
|
Ren, M., Shi, J., Jia, J., Guo Y., Ni X. and Shi, T. (2020) Genotype-phenotype correlations of Berardinelli-Seip congenital lipodystrophy and novel candidate genes prediction. Orphanet J Rare Dis. (PMID:31572191)
|
Honors and Awards (recent 5 years)
|
•
|
Outstanding graduates of Shanghai
|
April 2019, Shanghai, China.
|
•
|
Evaluation of scientific research achievements in research group, First Prize (No. 1)
|
February 2019, Shanghai, China.
|
•
|
National Scholarship Award
|
October 2018, Shanghai, China.
|
•
|
Excellent-Youngster Researcher
|
May 2018, Heidelberg, Germany.
|
Presentation title: MIAPE-QC: Minimum information guidelines for a Quality Control experiment in LC-MS/MS.
|
•
|
The Thirteenth National Graduate Student Mathematical Contest in modeling, First Prize
|
October 2017, Chongqing, China.
|
•
|
Evaluation of scientific research achievements in research group, First Prize (No. 1)
|
February 2017, Shanghai, China.
|
•
|
Travel Fellowship Award
|
April 2016, Geneva, Switzerland.
|
Presentation title: Standardized Multi-omics Annotation for Pediatric disease.
|
•
|
Excellent Student
|
December 2015, Shanghai, China.
|