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DTSTAMP:20241120T082410Z
LOCATION:HG F 30 Audi Max
DTSTART;TZID=Europe/Stockholm:20240604T100700
DTEND;TZID=Europe/Stockholm:20240604T100800
UID:submissions.pasc-conference.org_PASC24_sess158_posC107@linklings.com
SUMMARY:ACMP07 - High Performance Computing Derived Biological Multiplex N
 etwork Uncovers Distinct Pathways Underlying Opioid and Nicotine Addiction
DESCRIPTION:Poster\n\nMatthew Lane (University of Tennessee, Oak Ridge Nat
 ional Laboratory)\n\nLeveraging High-Performance Computing (HPC) for biolo
 gical network generation, key insights into the genetic and epigenetic mec
 hanisms supporting opioid and nicotine addition have been uncovered. Using
  distributed network generation software on the Frontier supercomputer, th
 e authors processed 700 single-cell RNA sequencing (scRNAseq) data sets to
  construct biologically robust multiplex networks. By employing an MPI tas
 k farm as a scheduling method, the network generation software computed ne
 tworks in 3 real-time hours compared to the average 76 day CPU-time, using
  iterative Random Forest (iRF) Leave One Out Prediction (LOOP). Network la
 yers were validated using RWR with k Fold cross validation against indepen
 dently curated GO terms and clustered using MENTOR, an algorithm developed
  for the clustering and visualization of RWR rank order embeddings. The fi
 ndings highlight transcriptional regulation via transcription factors and 
 epigenetic mechanisms implicated in neural development. This research not 
 only illuminates our current understanding of nicotine and opioid addictio
 n, but also demonstrates the importance of HPC network generation and vali
 dation techniques.\n\nSession Chair: Iva Kavcic (Met Office)
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