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SUMMARY:Integration of Bioinformatics and Machine Learning to characterise
  Fusobacterium nucleatum's pathogenicity - Zihan Tian (Johns Hopkins Unive
 rsity)-ztian12@jh.edu
DTSTART:20250814T173000Z
DTEND:20250814T180000Z
UID:TALK235129@talks.cam.ac.uk
CONTACT:Pietro Lio
DESCRIPTION:Fusobacterium nucleatum has been found to be associated with c
 ancer lesions in both oral and colon cancers. Although important studies h
 ave dissected the clinical aspects of its remarkable pathogenicity\, there
  is a lack of molecular studies. Our computational work based on bioinform
 atics and machine learning methodologies has identified a resourceful path
 ogenicity island. The study has involved the analysis of genome-based comp
 ositional bias\, promoter maps\, codon adaptation index\, protein structur
 e\, and characterized these genomic regions on the basis of predicting bas
 e compositional bias\, promoter mapping\, protein abundances\, and interac
 tions\, metabolic model to characterize these regions. Although most of th
 e currently in use pathogenicity islands finder software detect the presen
 ce of three pathogenicity islands\, our analysis suggests that only one is
  present.\nFurthermore\, we have investigated and discussed the metabolic 
 advantages of pathogenicity\, particularly iron ion scavenging activity.\n
 Our work has two immediate and important benefits: the improved understand
 ing of the biological processes that shape the pathogenicity and evolution
  of Fusobacterium nucleatum at the molecular level and the improved abilit
 y to integrate and automate the state-of-the-art bioinformatics tools and 
 machine learning approaches in the inference of the mechanistic interpreta
 bility of a pathogenic phenotype.
LOCATION:Computer Laboratory\, William Gates Building\, Room FW26.
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