Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0023418 (leukemia)
 93,477 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The effect of a singular amino acid, asparagine (Asn), glutamine (Gln), or proline deletion in a cultured medium (RPMI 1640 supplemented with 10% fetal calf serum and other ingredients) on adriamycin (ADR) cytotoxicity was evaluated in the growth of P388 murine leukemia cells and CEM human acute lymphoblastic leukemia cells over a 3 day period. No enhancement of ADR cytotoxicity was observed in the assay of IC50 values under the amino acid deleted condition. Singular deletion of Gln or Asn from ADR-free medium apparently inhibited the proliferation of both cells, i.e. both cell lines strongly require them. The cytotoxicity of 5 nM ADR was then examined in medium which included one or the other of them in stepwise levels varied at 80, 60, 40, 20 and 0% of the ordinary level. Change of Asn level caused a difference in ADR toxicity; also, the change of Gln level, especially the 60% level caused ADR toxicity of 5 nM, which is less than the IC50 value, in the proliferation of both cells. This suggested the usefulness of glutamine level modification on the enhancement of ADR cytotoxicity.
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PMID:Inhibition of cultured leukemia cell growth by enhanced adriamycin cytotoxicity with reduction of glutamine or asparagine level in medium. 788 97

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data.
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PMID:Ensemble Feature Learning of Genomic Data Using Support Vector Machine. 2730 23