https://github.com/shmohammadi86/NetDECODE

https://github.com/shmohammadi86/NetDECODE

Last synced: 24 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/shmohammadi86/NetDECODE
• Owner: shmohammadi86
• Created: 2019-06-23T22:18:39.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-02-21T15:01:33.000Z (over 2 years ago)
• Last Synced: 2024-02-24T13:33:09.507Z (4 months ago)
• Language: C++
• Size: 75.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome_single_cell - NetDECODE - [R] - We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene's local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors. (Software packages / RNA-seq)
• awesome-single-cell - NetDECODE - [R] - We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene's local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors. (Software packages / Feature (Gene) imputation)
• awesome-single-cell - NetDECODE - [R] - We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene's local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors. (Software packages / RNA-seq)

README

        
# Network DECODE (NetDECODE)