Using metabolomic and transportomic modeling and machine learning to identify putative novel therapeutic targets for antibiotic resistant Pseudomonad infections

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Volumn , Issue , 2014, Pages 314-317

  Larsen, Peter E ^a   Collart, Frank R ^a   Dai, Yang ^b  

^a ARGONNE NATIONAL LABORATORY   (United States)

^b University of Illinois at Chicago   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIBIOTICS; ARTIFICIAL INTELLIGENCE; BACTERIA; COMPUTATIONAL METHODS; HOSPITALS; LEARNING ALGORITHMS;

ANTIMICROBIAL THERAPY; COMPUTATIONAL APPROACH; HIGHLY ACCURATE; HOSPITAL ACQUIRED INFECTION; INFECTIOUS PATHOGENS; LEAVE-ONE-OUT CROSS VALIDATIONS; MOLECULAR MECHANISM; THERAPEUTIC TARGETS;

LEARNING SYSTEMS;

ANTIINFECTIVE AGENT; BACTERIAL PROTEIN;

ANTIBIOTIC RESISTANCE; HUMAN; METABOLISM; METABOLOME; METABOLOMICS; PSEUDOMONAS; PSEUDOMONAS INFECTIONS; SUPPORT VECTOR MACHINE; TRANSPORT AT THE CELLULAR LEVEL;

ANTI-BACTERIAL AGENTS; BACTERIAL PROTEINS; BIOLOGICAL TRANSPORT; DRUG RESISTANCE, BACTERIAL; HUMANS; METABOLOME; METABOLOMICS; PSEUDOMONAS; PSEUDOMONAS INFECTIONS; SUPPORT VECTOR MACHINES;

EID: 84929462450     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EMBC.2014.6943592     Document Type: Conference Paper

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