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MeSH Review

Artificial Intelligence

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Disease relevance of Artificial Intelligence

  • Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival [1].
  • MOTIVATION: The motivation is to identify, through machine learning techniques, specific patterns in HIV and HCV viral polyprotein amino acid residues where viral protease cleaves the polyprotein as it leaves the ribosome [2].
  • We developed a decision support system using artificial intelligence techniques for the classification and ultimately the diagnosis of epilepsies and epilepsy syndromes in children [3].

High impact information on Artificial Intelligence


Biological context of Artificial Intelligence

  • The support vector machine, as a novel type of learning machine, for the first time, was used to develop a QSAR model of 57 analogues of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate (EPC), an inhibitor of AP-1 and NF-kappa B mediated gene expression, based on calculated quantum chemical parameters [9].
  • Comparison efficiency of the artificial intelligence methods for the diagnosis of Acid - base and anion gap disorders [10].

Associations of Artificial Intelligence with chemical compounds


Gene context of Artificial Intelligence

  • An artificial-intelligence technique for qualitatively deriving enzyme kinetic mechanisms from initial-velocity measurements and its application to hexokinase [16].
  • Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors [17].
  • The Surrogate Model combines molecular modeling, machine learning and an Artificial Neural Network. This novel approach includes an accounting for experimental error using a Monte Carlo analysis [18].
  • For such a purpose, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates [19].
  • The precise control of stimulus and response required to define the mainly minor differences between the epilepsy and control groups can only be fulfilled by computerized testing, which should undergo further refinement including voice and language recognition, followed by artificial intelligence [20].


  1. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Ye, Q.H., Qin, L.X., Forgues, M., He, P., Kim, J.W., Peng, A.C., Simon, R., Li, Y., Robles, A.I., Chen, Y., Ma, Z.C., Wu, Z.Q., Ye, S.L., Liu, Y.K., Tang, Z.Y., Wang, X.W. Nat. Med. (2003) [Pubmed]
  2. Mining viral protease data to extract cleavage knowledge. Narayanan, A., Wu, X., Yang, Z.R. Bioinformatics (2002) [Pubmed]
  3. Decision support system for classification of epilepsies in childhood. Vassilakis, K.M., Vorgia, L., Micheloyannis, S. J. Child Neurol. (2002) [Pubmed]
  4. SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures. Hao, G., Derakhshan, B., Shi, L., Campagne, F., Gross, S.S. Proc. Natl. Acad. Sci. U.S.A. (2006) [Pubmed]
  5. Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. King, R.D., Muggleton, S., Lewis, R.A., Sternberg, M.J. Proc. Natl. Acad. Sci. U.S.A. (1992) [Pubmed]
  6. An artificial intelligence approach to the study of the structural moieties relevant to drug-receptor interactions in aldose reductase inhibitors. Klopman, G., Buyukbingol, E. Mol. Pharmacol. (1988) [Pubmed]
  7. Extraction and visualization of potential pharmacophore points using support vector machines: application to ligand-based virtual screening for COX-2 inhibitors. Franke, L., Byvatov, E., Werz, O., Steinhilber, D., Schneider, P., Schneider, G. J. Med. Chem. (2005) [Pubmed]
  8. Modeling of human cytochrome p450-mediated drug metabolism using unsupervised machine learning approach. Korolev, D., Balakin, K.V., Nikolsky, Y., Kirillov, E., Ivanenkov, Y.A., Savchuk, N.P., Ivashchenko, A.A., Nikolskaya, T. J. Med. Chem. (2003) [Pubmed]
  9. QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-kappa B mediated gene expression based on support vector machines. Liu, H.X., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D., Fan, B.T. Journal of chemical information and computer sciences. (2003) [Pubmed]
  10. Comparison efficiency of the artificial intelligence methods for the diagnosis of Acid - base and anion gap disorders. Kacki, E., Małolepszy, A. Studies in health technology and informatics. (2005) [Pubmed]
  11. Use of artificial intelligence in structure-affinity correlations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) receptor ligands. Rannug, U., Sjögren, M., Rannug, A., Gillner, M., Toftgård, R., Gustafsson, J.A., Rosenkranz, H., Klopman, G. Carcinogenesis (1991) [Pubmed]
  12. The discrimination of similarly colored objects in computer images of the ocular fundus. Goldbaum, M.H., Katz, N.P., Nelson, M.R., Haff, L.R. Invest. Ophthalmol. Vis. Sci. (1990) [Pubmed]
  13. Molecular hashkeys: a novel method for molecular characterization and its application for predicting important pharmaceutical properties of molecules. Ghuloum, A.M., Sage, C.R., Jain, A.N. J. Med. Chem. (1999) [Pubmed]
  14. Tenofovir resistance and resensitization. Wolf, K., Walter, H., Beerenwinkel, N., Keulen, W., Kaiser, R., Hoffmann, D., Lengauer, T., Selbig, J., Vandamme, A.M., Korn, K., Schmidt, B. Antimicrob. Agents Chemother. (2003) [Pubmed]
  15. WIZARD: AI in conformational analysis. Dolata, D.P., Leach, A.R., Prout, K. J. Comput. Aided Mol. Des. (1987) [Pubmed]
  16. An artificial-intelligence technique for qualitatively deriving enzyme kinetic mechanisms from initial-velocity measurements and its application to hexokinase. Garfinkel, L., Cohen, D.M., Soo, V.W., Garfinkel, D., Kulikowski, C.A. Biochem. J. (1989) [Pubmed]
  17. Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors. Arimoto, R., Prasad, M.A., Gifford, E.M. Journal of biomolecular screening : the official journal of the Society for Biomolecular Screening. (2005) [Pubmed]
  18. Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces. Smith, J.R., Knight, D., Kohn, J., Rasheed, K., Weber, N., Kholodovych, V., Welsh, W.J. Journal of chemical information and computer sciences. (2004) [Pubmed]
  19. Prediction of P-glycoprotein substrates by a support vector machine approach. Xue, Y., Yap, C.W., Sun, L.Z., Cao, Z.W., Wang, J.F., Chen, Y.Z. Journal of chemical information and computer sciences. (2004) [Pubmed]
  20. Computerized neuropsychological assessment of cognitive functioning in children with epilepsy. Alpherts, W.C., Aldenkamp, A.P. Epilepsia (1990) [Pubmed]
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