Geng Li
Nottingham Trent University
31 Papers
464 Citations
Geng Li is an academic researcher from Nottingham Trent University. The author has contributed to research in topics: Cancer & Antigen. The author has an hindex of 16, co-authored 31 publications. Previous affiliations of Geng Li include Huntingdon Life Sciences.
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Papers
An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers.
Graham Ball,Shahid Mian,F. Holding,R. O. Allibone,James Lowe,Safdar Ali,Geng Li,S. McCardle,Ian O. Ellis,Colin S. Creaser,Robert C. Rees +10 more
TL;DR: The data from this initial study suggests that application of ANN-based approaches can identify molecular ion patterns which strongly associate with disease grade and that its application to larger cohorts of patient material could potentially facilitate the rapid identification of validated biomarkers having significant clinical (i.e. diagnostic/prognostic) potential for the field of cancer biology.
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DNA demethylation and histone deacetylation inhibition co‐operate to re‐express estrogen receptor beta and induce apoptosis in prostate cancer cell‐lines
Thomas J. Walton,Thomas J. Walton,Geng Li,Rashmi Seth,Stephanie E. B. McArdle,M.C. Bishop,Robert C. Rees +6 more
TL;DR: Epigenetic silencing mechanisms are increasingly thought to play a major role in the development of human cancers, including prostate cancer, yet few studies have examined a potential interaction in prostate cancer.
Serological identification and expression analysis of gastric cancer-associated genes.
TL;DR: This study has revealed several new gastric cancer candidate genes; additional studies are required to gain a deeper insight into their role in the tumorigenesis and their potential as therapeutic targets.
Identification of tumour antigens by serological analysis of cDNA expression cloning.
TL;DR: The immunoscreening of cDNA expression libraries constructed from human tumour tissues with antibodies in sera from cancer patents (SEREX) provides a powerful approach to identify immunogenic tumour antigens, which facilitate the identification of epitopes recognised by antigen-specific cytotoxic T lymphocytes in a wide variety of human cancers.
A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.
Shahid Mian,Graham Ball,Jo Hornbuckle,Finn Holding,J. Carmichael,Ian O. Ellis,Selman A. Ali,Geng Li,Stephanie E. B. McArdle,Colin S. Creaser,Robert C. Rees +10 more
TL;DR: Results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign ‘class’ for treatment regimen with a high degree of accuracy using boot‐strap statistical validation techniques and that biomarker ion patterns indicative of response/non‐response phenotypes are associated with MCF‐7 and MCF-7/ADR cells exposed to Doxorubicin.
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