@RBoiarsky Interesting work showing the benefit of gene2vec! Small question: Which gene2vec model did you use? Du et al https://t.co/U8ctAu2uzK (cited from scBERT) or Zou et al https://t.co/Z9NEvppo5T
Gene2vec: distributed representation of genes based on co-expression https://t.co/kfPNVALpYO
RT @zhizhid: @leetx1010 @MarkGerstein @BMCBioinfo Congratulations to your work. Wished you would have cited more recent literature such as…
A milestone in adopting deep learning models for biological systems and accounting for their self-regulatory nature. https://t.co/cKZUYrv3FU
RT @zhizhid: @leetx1010 @MarkGerstein @BMCBioinfo Congratulations to your work. Wished you would have cited more recent literature such as…
@leetx1010 @MarkGerstein @BMCBioinfo Congratulations to your work. Wished you would have cited more recent literature such as our work https://t.co/SQNXHO13V2
This is important. Word embedding techniques applied to gene co-expression patterns. I.e. NLP-ish tech being brought to bear. More will come of this. https://t.co/2y15kzgyRU
Gene2vec: distributed representation of genes based on co-expression. https://t.co/KA1vbtPtpQ
Gene2vec: distributed representation of genes based on co-expression https://t.co/GJzn23vDel #bmcgenomics
Gene2vec: distributed representation of genes based on co-expression https://t.co/Xszj47faL7 #bmcgenomics