The multiple Myeloma prediction model based on the gene expression profile

被引：0
|
作者：
Jiang, Guotai [1 ]
Chen, Shuohao [1 ]
Xu, Xiaolei [1 ]
机构：
[1] Tongji Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
来源：
PROGRESS ON POST-GENOME TECHNOLOGIES | 2007年
关键词：
gene microarray; multiple myeloma; artificial neural networks;
D O I：
暂无
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
DNA Microarrys technology for Gene Expression Profile has become more and more important in genomics cancer research. This paper, based on the principle of Artifical Neural Network(ANN), analyzes the data of Multiple Myeloma gene expression profile and establishes a Back Propagation-Artifical Neural Network model. By using of 24 feature genes, closely related to the cancer, it sets up a three-layer BP-ANN. This model is able to self-learn and self-adapt. From the experiment we can concludes that it can get more accurate predict results, nearly 100%.
引用
收藏
页码：100 / 101
页数：2
相关论文
共 50 条
  • [21] The prediction model of multiple myeloma based on the BP artificial neural network
    Chen, Shuohao
    Jiang, Guotai
    2008 INTERNATIONAL SPECIAL TOPIC CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS IN BIOMEDICINE, VOLS 1 AND 2, 2008, : 198 - 200
  • [22] Prognostic model for multiple myeloma progression integrating gene expression and clinical features
    Sun, Chen
    Li, Hongyang
    Mills, Ryan E.
    Guan, Yuanfang
    GIGASCIENCE, 2019, 8 (12):
  • [23] Gene expression profiles in multiple myeloma.
    Magrangeas, F
    Avet-Loiseau, H
    Nasser, V
    Doriot, B
    Nguyen, C
    Houlgatte, R
    Bataille, R
    Minvielle, S
    BLOOD, 2001, 98 (11) : 733A - 733A
  • [24] Osteoclast Gene Expression Profiling in Multiple Myeloma
    Moreaux, J.
    Hose, D.
    Reme, T.
    Moine, P.
    Mahtouk, K.
    Moos, M.
    Requirand, G.
    De Vos, J.
    Rossi, J. F.
    Goldschmidt, H.
    Klein, B.
    CLINICAL LYMPHOMA & MYELOMA, 2009, 9 : S153 - S153
  • [25] Metascoring and gene expression profiling in multiple myeloma
    Meissner, T.
    Seckinger, A.
    Reme, T.
    Hielscher, T.
    Moehler, T.
    Neben, K.
    Goldschmidt, H.
    Klein, B.
    Hose, D.
    ONKOLOGIE, 2011, 34 : 242 - 242
  • [26] Prediction Model for Cereblon Expression in Bone Marrow Plasma Cells Based on Blood Markers in Multiple Myeloma Patients
    Lee, Byung-Hyun
    Kang, Ka-Won
    Jeon, Min Ji
    Yu, Eun Sang
    Kim, Dae Sik
    Lee, Se Ryeon
    Sung, Hwa Jung
    Park, Yong
    Choi, Chul Won
    Kim, Byung Soo
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [27] A new method for gene functional prediction based on homologous expression profile
    Lv, S
    Wang, QH
    Zhang, GM
    Wen, FX
    Wang, ZZ
    Li, X
    FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 2, PROCEEDINGS, 2005, 3614 : 836 - 844
  • [28] Gene expression profiling based on microarray among monoclonal gammopathy of undetermined significance, smoldering multiple myeloma and multiple myeloma
    Feng, Baotong
    Ding, Butong
    Sun, Yao
    Guo, Nongjian
    INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL MEDICINE, 2017, 10 (01): : 767 - 773
  • [29] Combining Gene Expression Profiling and Integrated Multidisciplinary Approaches to Improve Prognosis Prediction in Multiple Myeloma
    Cerchione, Claudio
    Alassi, Nour
    Nappi, Davide
    Servili, Chiara
    Scarpi, Emanuela
    Cangini, Delia
    Ronconi, Sonia
    Ceccolini, Michela
    Paganelli, Matteo
    Coppola, Luigi
    Feliciani, Giacomo
    Rossi, Alice
    Mammoli, Fabiana
    Normanno, Nicola
    Musuraca, Gerardo
    Simonetti, Giorgia
    Martinelli, Giovanni
    Marchesini, Matteo
    BLOOD, 2024, 144 : 6838 - 6839
  • [30] Gene expression profiling in the prediction of response of multiple myeloma to the proteasome inhibitor PS-341.
    Shaughnessy, J
    Zhan, FH
    McCastlain, K
    Tian, EM
    Tricot, G
    BLOOD, 2002, 100 (11) : 390A - 390A
12345