title: Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation.
abstract: BACKGROUND
  Alzheimer's Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD.
  
  
  METHODS
  We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods.
  
  
  RESULTS
  Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy.
  
  
  CONCLUSIONS
  These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders.
authors.affiliation: King's College London, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK.
authors.forename: Ines
authors.initials: I
authors.lastname: Greco
authors.forename: Nicola
authors.initials: N
authors.lastname: Day
authors.forename: Joanna
authors.initials: J
authors.lastname: Riddoch-Contreras
authors.forename: Jane
authors.initials: J
authors.lastname: Reed
authors.forename: Hilkka
authors.initials: H
authors.lastname: Soininen
authors.forename: Iwona
authors.initials: I
authors.lastname: Kłoszewska
authors.forename: Magda
authors.initials: M
authors.lastname: Tsolaki
authors.forename: Bruno
authors.initials: B
authors.lastname: Vellas
authors.forename: Christian
authors.initials: C
authors.lastname: Spenger
authors.forename: Patrizia
authors.initials: P
authors.lastname: Mecocci
authors.forename: Lars-Olof
authors.initials: LO
authors.lastname: Wahlund
authors.forename: Andrew
authors.initials: A
authors.lastname: Simmons
authors.forename: Julie
authors.initials: J
authors.lastname: Barnes
authors.forename: Simon
authors.initials: S
authors.lastname: Lovestone
mesh_terms: D000544:Alzheimer Disease / Q000378:metabolism*; D015415:Biomarkers / Q000378:metabolism*; D006801:Humans; D016247:Information Storage and Retrieval
chemical_list: D015415:Biomarkers