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  1. AU="Mohammadamin Edrisi"
  2. AU="Powell, Lauren"
  3. AU="Mohammadzadeh, Amir"
  4. AU="S. V. Subramanian"
  5. AU=Seror Raphaele
  6. AU="Anne K. McGavigan"
  7. AU="Martín-Francés, Laura"
  8. AU="Roy Remmen"
  9. AU="Perlee, Sarah"
  10. AU="Atamanalp, Refik Selim"
  11. AU="Costa, Bruno Buranello"
  12. AU="Kohler, Beatriz"
  13. AU="Tabata, Toshinori"
  14. AU="Sun, Shijing"
  15. AU="Kufeji D."
  16. AU="Kohani, Sayeh"
  17. AU="Wong, John Cm"
  18. AU="Hua LI"
  19. AU="Özkan, Yasemin"
  20. AU=Quirmbach Diana
  21. AU="Corpstein, Clairissa D"
  22. AU="Motel-Klingebiel, Andreas"
  23. AU="Brown, Randy A"
  24. AU="Feng, Yaying"
  25. AU="Lussi, A"
  26. AU="Yeon Susan B"
  27. AU="Abaci, Irem"
  28. AU="Lin, Xiaode"
  29. AU="Mendez, Luis"
  30. AU=Alzahrani Faisal A AU=Alzahrani Faisal A
  31. AU="Heidi G Standke"
  32. AU="Banville, Isabelle"
  33. AU=Morrow Lee E
  34. AU="Cuss, Chad W."
  35. AU="Carter, Paul (Interviewpartner)"
  36. AU=Lubozynski M F
  37. AU="Yves, Ville"
  38. AU="Bayer, Emily A"
  39. AU=Roesch Saskia
  40. AU="Tam, Benjamin"
  41. AU="Mori, Kousuke"
  42. AU="Steuer, Melanie"
  43. AU="Sood Hemant"
  44. AU="Jennifer Schaff"
  45. AU="Maji, Manideepa"
  46. AU=Evans Heather L
  47. AU="Cheng, Shuai"
  48. AU="Zalis, Joshua"

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  1. Artikel ; Online: Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA

    Mohammadamin Edrisi / Xiru Huang / Huw A. Ogilvie / Luay Nakhleh

    Nature Communications, Vol 14, Iss 1, Pp 1-

    2023  Band 15

    Abstract: Abstract Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and ... ...

    Abstract Abstract Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett’s esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.
    Schlagwörter Science ; Q
    Thema/Rubrik (Code) 612
    Sprache Englisch
    Erscheinungsdatum 2023-12-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Methods for copy number aberration detection from single-cell DNA-sequencing data

    Xian F. Mallory / Mohammadamin Edrisi / Nicholas Navin / Luay Nakhleh

    Genome Biology, Vol 21, Iss 1, Pp 1-

    2020  Band 22

    Abstract: Abstract Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring ... ...

    Abstract Abstract Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.
    Schlagwörter Tumor evolution ; Intra-tumor heterogeneity ; Single-cell DNA sequencing ; Copy number aberrations ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Sprache Englisch
    Erscheinungsdatum 2020-08-01T00:00:00Z
    Verlag BMC
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.

    Xian F Mallory / Mohammadamin Edrisi / Nicholas Navin / Luay Nakhleh

    PLoS Computational Biology, Vol 16, Iss 7, p e

    2020  Band 1008012

    Abstract: Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide ... ...

    Abstract Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods-Ginkgo, HMMcopy, and CopyNumber-on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.
    Schlagwörter Biology (General) ; QH301-705.5
    Thema/Rubrik (Code) 310
    Sprache Englisch
    Erscheinungsdatum 2020-07-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: Current progress and open challenges for applying deep learning across the biosciences

    Nicolae Sapoval / Amirali Aghazadeh / Michael G. Nute / Dinler A. Antunes / Advait Balaji / Richard Baraniuk / C. J. Barberan / Ruth Dannenfelser / Chen Dun / Mohammadamin Edrisi / R. A. Leo Elworth / Bryce Kille / Anastasios Kyrillidis / Luay Nakhleh / Cameron R. Wolfe / Zhi Yan / Vicky Yao / Todd J. Treangen

    Nature Communications, Vol 13, Iss 1, Pp 1-

    2022  Band 12

    Abstract: Deep learning has enabled advances in understanding biology. In this review, the authors outline advances, and limitations of deep learning in five broad areas and the future challenges for the biosciences. ...

    Abstract Deep learning has enabled advances in understanding biology. In this review, the authors outline advances, and limitations of deep learning in five broad areas and the future challenges for the biosciences.
    Schlagwörter Science ; Q
    Sprache Englisch
    Erscheinungsdatum 2022-04-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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