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dc.contributor.advisorShen, Rongkun
dc.contributor.authorGalbier, Lucas
dc.date.accessioned2021-09-08T14:16:28Z
dc.date.available2021-09-08T14:16:28Z
dc.date.issued5/9/2017
dc.identifier.urihttp://hdl.handle.net/20.500.12648/6667
dc.description.abstractMicroRNAs are very short non-coding RNAs. Since microRNAs play important roles in many biological process, the research of microRNAs is a burgeoning field with much promise. Due to the high cost of experimental approaches, many computational techniques and algorithms have been implemented to study microRNAs. However, current methods for determining the targets for miRNAs are far from accurate. To address this issue, we developed algorithms that produced profiles of miRNA recognition elements and features such binding energy threshold and conservation score. These profiles will be used to train a machine learning algorithm for miRNA target prediction.
dc.subjectSenior Honors Thesis
dc.subjectBiology
dc.subjectMicroRNA
dc.subjectMachine Learning
dc.titleBuilding Profiles for miRNA Target Prediction
dc.typethesis
refterms.dateFOA2021-09-08T14:16:28Z
dc.description.institutionSUNY Brockport
dc.description.departmentBiology
dc.source.statuspublished
dc.description.publicationtitleSenior Honors Theses
dc.contributor.organizationThe College at Brockport
dc.languate.isoen_US


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