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Chen Meng

Researcher at Technische Universität München

Publications -  55
Citations -  3005

Chen Meng is an academic researcher from Technische Universität München. The author has contributed to research in topics: Proteomics & Proteome. The author has an hindex of 16, co-authored 51 publications receiving 1911 citations. Previous affiliations of Chen Meng include Royal Institute of Technology.

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The target landscape of clinical kinase drugs

TL;DR: A comprehensive analysis of 243 kinase inhibitors that are either approved for use or in clinical trials provides an open-access resource of target summaries that could help researchers develop better drugs, understand how existing drugs work, and design more effective clinical trials.
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A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

TL;DR: A quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs and that protein expression is often more stable across tissues than that of transcripts.
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Global Proteome Analysis of the NCI-60 Cell Line Panel

TL;DR: A quantitative proteome and kinome profile of the NCI-60 panel covering, in total, 10,350 proteins (including 375 protein kinases) and including a core cancer proteome of 5,578 proteins that were consistently quantified across all tissue types is presented.
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Dimension reduction techniques for the integrative analysis of multi-omics data

TL;DR: This work explores dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase the understanding of biological systems in normal physiological function and disease.
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A multivariate approach to the integration of multi-omics datasets

TL;DR: Multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets, is described, an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals.