text: user1026: I have downloaded a gene expression data from GEO database (GSE3268) in which in some of its rows there are duplicate gene symbols.
  For example TP53 exists in two rows with different expression values and different GenBank Accession IDs.
  They have the following Nucleotide Title:
  
  Human p53 cellular tumor antigen mRNA, complete cds
  Homo sapiens tumor protein p53 (TP53), transcript variant 1, mRNA
  
  I want to map these data with edge list of gene symbols to make a network. Which expression value should I consider for such duplicate gene symbols for mapping?
  
  user77: Since this is a microarray (U133A), what you're seeing is that each probe is associated with one or more transcripts. The general strategy when dealing with microarrays is to use RMA (or fRMA) to summarize to probesets and then collapseRows to summarize to gene-level information. Since this is all typically done in R, you can find some further discussion on the bioconductor site. I would suggest going through this process and then only using the result of that in building your network (whether you're happy with sticking to transcript-level quantifications or gene-level quantifications for building your network is up to you).
  
original_text: 1026: <p>I have downloaded a gene expression data from GEO database (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3268" rel="nofollow noreferrer">GSE3268</a>) in which in some of its rows there are duplicate gene symbols.</p>
  <p>For example <strong>TP53</strong> exists in two rows with different expression values and different <em>GenBank Accession</em> IDs.</p>
  <p>They have the following <em>Nucleotide Title</em>:</p>
  <blockquote>
  <p>Human p53 cellular tumor antigen mRNA, complete cds</p>
  <p>Homo sapiens tumor protein p53 (TP53), transcript variant 1, mRNA</p>
  </blockquote>
  <p>I want to map these data with edge list of gene symbols to make a network. Which expression value should I consider for such duplicate gene symbols for mapping?</p>
   77: <p>Since this is a microarray (U133A), what you're seeing is that each probe is associated with one or more transcripts. The general strategy when dealing with microarrays is to use RMA (or fRMA) to summarize to probesets and then <code>collapseRows</code> to summarize to gene-level information. Since this is all typically done in R, you can find some further discussion <a href="https://support.bioconductor.org/p/60214/" rel="noreferrer">on the bioconductor site</a>. I would suggest going through this process and then only using the result of that in building your network (whether you're happy with sticking to transcript-level quantifications or gene-level quantifications for building your network is up to you).</p>
  