Lee Lancashire: all publications

• Publications

  • Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. Artificial intelligence in medicine (2008) (Epub 15 Apr 2008) PubMed ID:(18420392 )

  • Quantitative proteomic analysis demonstrates post-transcriptional regulation of embryonic stem cell differentiation to hematopoiesis. (2007) (Epub 27 Nov 2007) PubMed ID:(18045800 )

  • Boateng J, Lancashire L, Ahmad M, Davy R, Matharoo-Ball B, Velloso C, Roberts J, Teale P, Yu Yang S, Rees R, Ball G, Goldspink G, Creaser C . The use of proteomic and bioinformatics techniques for the detection of protein biomarkers following growth hormone administration The internet journal of genomics and proteomics. 2 (2007)

  • Matharoo-Ball B, Ratcliffe L, Lancashire L, Ugurel S, Miles A, Weston D, Rees R, Schadendorf D, Ball G, Creaser C. Diagnostic biomarkers differentiating metastatic melanoma patients from healthy controls identified by an integrated MALDI-TOF mass spectrometry/bioinformatic approach Proteomics- Clinical Applications 1 , 605-620 (2007)

  • Lancashire L, Ugurel S, Creaser C, Schadendorf D, Rees R, Ball G. Utilizing Artificial Neural Networks To Elucidate Serum Biomarker Patterns Which Discriminate Between Clinical Stages In Melanoma IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (2005)

  • Lancashire LJ, Mian S, Ellis IO, Rees RC, Ball GR. Current developments in the analysis of proteomic data: Artificial neural network data mining techniques for the identification of proteomic biomarkers related to breast cancer Current Proteomics 2 , 15-29 (2005)

  • Lancashire LJ, Mian S, Rees RC, Ball GR. Preliminary Artificial Neural Network Analysis of SELDI Mass Spectrometry Data For The Classification Of Melanoma Tissue 17th European Simulation Multiconference , 131-135 (2003)

  • Characterization of Biomarkers in Polycystic Ovary Syndrome (PCOS) Using Multiple Distinct Proteomic Platforms. 6 (8) , 3321-3328 (Epub 29 Jun 2007) PubMed ID:(17602513 )

  • Iversen C, Lancashire L, Waddington M, Forsythe S, Ball G. Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data. BMC microbiology 6 , 28 (Epub 13 Mar 2006) PubMed ID:(16533390 )

  • Lancashire L, Schmid O, Shah H, Ball G. Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis. Bioinformatics (Oxford, England) 21 (10) , 2191-9 (Epub 03 Mar 2005) PubMed ID:(15746279 )

Results