Breast cancer detection by Michaelis-Menten constants via linear programming

Computer Methods and Programs in Biomedicine

Volumn 85, Issue 3, 2007, Pages 210-213

  Blokh, David ^a   Afrimzon, Elena ^a   Stambler, Ilia ^a   Korech, Eden ^a   Shafran, Yana ^a   Zurgil, Naomi ^a   Deutsch, Mordechai ^a  

^a BAR ILAN UNIVERSITY   (Israel)

Author keywords

Breast cancer (BC); Linear programming; Michaelis Menten constants (Km and Vmax); Peripheral blood mononuclear cells (PBMC)

Indexed keywords

CELL CULTURE; DIAGNOSIS; DISEASES; FLUORESCENCE; HYDROLYSIS; LINEAR PROGRAMMING;

BREAST CANCER (BC); FLUORESCEIN DIACETATE (FDA); MICHAELIS-MENTEN CONSTANTS; PERIPHERAL BLOOD MONONUCLEAR CELLS (PBMC);

TUMORS;

FLUORESCEIN DIACETATE; MITOGENIC AGENT; PHYTOHEMAGGLUTININ;

ADULT; AGED; ARTICLE; BREAST CANCER; CANCER DIAGNOSIS; CANCER PATIENT; CLINICAL ARTICLE; COMPARATIVE STUDY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; FEMALE; FLUORESCENCE; HUMAN; HUMAN CELL; HYDROLYSIS; MICHAELIS CONSTANT; PERIPHERAL BLOOD MONONUCLEAR CELL; SYSTEM ANALYSIS; UNCERTAINTY;

BREAST NEOPLASMS; DIAGNOSTIC TECHNIQUES AND PROCEDURES; FEMALE; HUMANS; ISRAEL; PROGRAMMING, LINEAR;

EID: 33846918138     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2006.11.003     Document Type: Article

References (12)

1. Floyd C.E., Yun A.J., Sullivan D., and Kornguth P. Prediction of breast cancer malignancy using an artificial neural network. Cancer 74 (1994) 2944-2998
2. Furundzic D., Djordjevic M., and Bekic A.J. Neural networks approach to early breast cancer detection. J. Syst. Architect. 44 (1998) 617-633
3. Pendharkar P.C., Roger J.A., Yaverbaum G.J., Herman N., and Benner M. Association, statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Syst. Appl. 17 (1999) 223-232
4. Wolberg W.H., and Mangasarian O.L. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. USA 87 (1990) 9193-9196
5. Mangasarian O.L., Street W.N., and Wolberg W.H. Breast cancer diagnosis and prognosis via linear programming. Oper. Res. 43 (1995) 570-577
6. Tou J.T., and Gonzalez R.C. Pattern Recognition Principles (1974), Addison-Wesley, Reading, MA
7. Verhayen C.J.D.M., Duin R.P.W., Groen F.C.A., Joosen J.C., and Verbeek P.W. Progress report on pattern recognition. Rep. Prog. Phys. 43 (1980) 785-831
8. Gelfand I.M., Rosenfeld B.I., and Shifrin M.A. Essays on Collaboration of Mathematicians and Physicians (1989), Nauka, Moscow (in Russian)
9. Nieddu L., and Patrizi G. Formal methods in pattern recognition: a review. Eur. J. Oper. Res. 120 (2000) 459-495
10. Fersht A. Enzyme Structure and Mechanism (1977), W.H. Freeman, San Francisco
11. Sunray M., Zurgil N., Shafran Y., and Deutsch M. Determination of individual cell Michaelis-Menten constants. Cytometry 47 (2002) 8-16
12. D. Blokh, Penalties for a convex programming problem, in: L.V. Kantorovich, A.G. Kruglikov (Eds.), System Modeling and Optimization Methods in Technological Change Studies. Collected Papers, The Institute for System Studies, Moscow, 1985, pp. 87-89 (in Russian).