Arash Davari Serej

@CancerEvolution

Single Cell Analysis▪️Microenvironment & Heterogeneity of Tumor▪️BioMed Process Mining▪️Cancer Evolution▪️scRNA-seq▫️Looking for new opportunities

Pavia, Italy
Vrijeme pridruživanja: rujan 2010.

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  1. Prikvačeni tweet

    “There are five important things for living a successful and fulfilling life: never stop dreaming, never stop believing, never give up, never stop trying, and never stop learning.” ― Roy Bennett -------- My Infographic educational background (Mini-CV) Design by:

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  2. Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation | Fig. Steady-State scRNA-Seq Datasets of CD4+ T Cells from LT and NLT Ref:

    Figure 1. Steady-State scRNA-Seq Datasets of CD4+ T Cells from LT and NLT (A) Experimental design for scRNA-seq data collection. (B) t-SNE representing all Treg and Tmem cells that passed quality control. (C) Genes defining the identity of Treg and Tmem cells in lymphoid and non-lymphoid tissues. Colon and skin were individually compared with their corresponding draining lymph node and spleen cells.
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  3. Single-cell genomic approaches for developing the next generation of immunotherapies. | ⁦ by ⁦ Ref:

    challenges in understanding the cellular effects of immunotherapies
    Single-cell analysis as an engine for driving drug development
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  4. Mapping human cell phenotypes to genotypes with single-cell genomics. Fig. Human organ maps can resolve disease phenotypes Ref:

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  5. Analyses of single‐cell MAPK/ERK signaling dynamics in response to temporally controlled EGF, NGF, FGF2 stimulations show that FGF2 evokes distinct signaling dynamics compared to EGF/NGF. A mathematical model ... Ref:

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  6. Mapping human cell phenotypes to genotypes with single-cell genomics. Fig. Human organoids to recapitulate human phenotypes in vitro Ref:

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  7. 2. velj

    Mapping human cell phenotypes to genotypes with single-cell genomics. Fig. Genetic manipulation toolkit to link phenotype to genotype by using stem cells Ref:

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  8. 1. velj

    Quantitative Proteomics of the Cancer Cell Line Encyclopedia Quantified the proteomes of 375 cell lines from diverse lineages in the CCLE Ref:

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  9. 1. velj

    Metabolic landscape of the tumor microenvironment at single cell resolution. Fig. Landscape of metabolic gene expression at single-cell level. Ref:

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  10. 1. velj

    Single-Cell Transcriptomic Atlas of Primate Ovarian Aging Ref:

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  11. 1. velj

    A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods. Fig. Motivation and overview of benchmark evaluation of scRNA-seq imputation methods Ref:

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  12. 1. velj

    In Situ RNA Sequencing (ISS) technology will be applied to spatially map cell types for international Human Cell Atlas initiative. Fig. CARTANA ISS technology for high throughput spatial transcriptomic analysis. Ref:

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  13. 1. velj

    Metabolic landscape of the tumor microenvironment at single cell resolution. Fig. Landscape of metabolic gene expression at single-cell level. Ref:

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  14. 31. sij

    A generic roadmap for RNA-seq computational analyses. The major analysis steps are listed above the lines for pre-analysis, core analysis and advanced analysis. Ref: A survey of best practices for RNA-seq data analysis ()

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  15. proslijedio/la je Tweet
    31. sij

    An excellent course for anyone wanting to learn about brain tumors: Great speakers, great students, great venue. .

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  16. 31. sij

    Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs Ref:

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  17. proslijedio/la je Tweet
    31. sij

    This led us to evaluating many downstream analyses across diff experimental platforms & datasets, incl the wonderful CellBench benchmarking dataset from et al () () e.g. for unsup clustering

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  18. 31. sij

    Single-cell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov "Coronavirus specifically likes to infect Asian male" Fig. Single-cell analysis of normal human lung. Ref:

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  19. 31. sij

    A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons. Ref:

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  20. 31. sij

    A mouse tissue atlas of small non-coding RNA. 473 new small RNAs ! Tissue specific and sexually dimorphic! Ref:

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  21. 31. sij

    Modeling metabolic variation with single-cell expression data Fig. Tissue and cell type context-specific metabolic models constructed using Tabula Muris scRNA-seq dataset ​(Schaum et al., 2018) Ref:

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