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DTSTAMP:20210916T132529Z
LOCATION:Jean-Jacques Rousseau
DTSTART;TZID=Europe/Stockholm:20210706T140000
DTEND;TZID=Europe/Stockholm:20210706T160000
UID:submissions.pasc-conference.org_PASC21_sess139@linklings.com
SUMMARY:Guilt-by-Association: Using Network Models to Decipher Complex Pat
 terns
DESCRIPTION:Minisymposium\n\nA common research problem in diverse domains 
 is the extraction of combinatorial patterns from large datasets. For examp
 le, most human diseases arise due to the interactions of multiple genetic 
 factors and lifestyle choices; and weather events, such as tornados, manif
 est due to the complex interactions of a host of meteorological states. Da
 ta in such domains is rapidly being gathered, yet identification of these 
 high-dimensional patterns remains difficult due to the combinatorial explo
 sion of the number of groups to be considered. One practical approach leve
 rages the concept of guilt-by-association and models the data as a network
 . These networks typically represent factors as nodes and relationships be
 tween pairs of factors as edges between the corresponding nodes. A key ben
 efit of network modeling is that the computation of simple pair-wise relat
 ionships can yield knowledge about unknown high-dimensional relationships.
  Network models have been widely employed, yet several challenges impede t
 heir full potential. This minisymposium focuses on these challenges, discu
 sses current state-of-the-art approaches, and also presents promising dire
 ctions for future research, such as the computations of 3-way relationship
 s.\n\nThe Good, the Bad, and the Ugly of Network Modeling\n\nClimer\n\nMan
 y datasets are comprised of features for a set of objects, such as genetic
  markers for a set of individuals or weather conditions for a set of clima
 te events. A common research problem when analyzing such datasets is to id
 entify combinations of objects that are intercorrelated, e.g. a pattern of
  ...\n\n---------------------\nPredicting Genotype-Specific Gene Regulator
 y Networks\n\nWeighill, Ben Guebila, Glass, Quackenbush, Platig\n\nThe maj
 ority of disease-associated genetic variants are thought to have regulator
 y effects, including disruption of transcription factor (TF) binding and a
 lteration of downstream gene expression. Identifying the way in which each
  person’s genotype affects their individual gene regulatory netwo...
 \n\n---------------------\nGenerating Large Networks with the CoMet Combin
 atorial Metrics Application\n\nJoubert\n\nIn this talk we describe CoMet, 
 an application used to identify networks of similarity relationships betwe
 en elements in large quantities of data. It is applicable to problems in m
 any science domains and can be used for problems representable in terms of
  vector similarity measures. CoMet has been ru...\n\n---------------------
 \nEmbracing Complexity: Explainable-AI and Network-Based Filtering For Mec
 hanistic Understanding of Biological Systems\n\nJacobson, Sullivan, Kainer
 , Walker, Cliff...\n\nOne of the primary goals in Systems Biology is to fi
 nd genetic and omics architectures that are responsible for phenotypes or 
 diseases.  The signal from such studies using traditional methods is 
 subject to significant false positive and false negative rates.  Howe
 ver, information from exis...\n\n\nDomain: CS and Math, Emerging Applicati
 ons, Chemistry and Materials, Climate and Weather, Life Sciences
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