SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
about
Advances in understanding tumour evolution through single-cell sequencing.Informatics for cancer immunotherapy.Sensitivity to sequencing depth in single-cell cancer genomics.Deconvolution and phylogeny inference of structural variations in tumor genomic samples.SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error
P2860
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
description
2017 nî lūn-bûn
@nan
2017年の論文
@ja
2017年論文
@yue
2017年論文
@zh-hant
2017年論文
@zh-hk
2017年論文
@zh-mo
2017年論文
@zh-tw
2017年论文
@wuu
2017年论文
@zh
2017年论文
@zh-cn
name
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
@en
type
label
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
@en
prefLabel
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
@en
P2093
P2860
P1433
P1476
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
@en
P2093
Anthony Tzen
Hamim Zafar
Nicholas Navin
P2860
P2888
P356
10.1186/S13059-017-1311-2
P50
P577
2017-09-19T00:00:00Z
P6179
1091863021