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Node assignment problem in Bayesian networks

100%

Polanska J. , Borys D. , Polanski A.

International Journal of Applied Mathematics and Computer Science

2006

tom 16

nr 2

233-240

This paper deals with the problem of searching for the best assignments of random variables to nodes in a Bayesian network (BN) with a given topology. Likelihood functions for the studied BNs are formulated, methods for their maximization are described and, finally, the results of a study concerning the reliability of revealing BNs' roles are reported. The results of BN node assignments can be applied to problems of the analysis of gene expression profiles.

Panel of serum metabolites discriminates cancer patients and healthy participants of lung cancer screening - a pilot study

63%

Roś-Mazurczyk M. , Wojakowska A. , Marczak Ł. , Polański K. , Pietrowska M. , Polanska J. , Dziadziuszko R. , Jassem J. , Rzyman W. , Widlak P.

Acta Biochimica Polonica

2017

tom 64

nr 3

513-518

Introduction. Blood biomarkers may support early diagnosis of lung cancer by enabling pre-selection of candidates for computed tomography screening or discrimination between benign and malignant screening-detected nodules. We aimed to identify features of serum metabolome distinguishing individuals with early-detected lung cancer from healthy participants of the lung cancer screening program. Methods. Blood samples were collected in the course of a low-dose computed tomography screening program performed in the Gdansk district (Northern Poland). The analysis included 31 patients with screening-detected lung cancer and the pair-matched group of 92 healthy controls. The gas chromatography coupled to mass spectrometry (GC/MS) approach was used to identify and quantify small metabolites present in serum. Results. There were several metabolites detected in the sera whose abundances discriminated patients with lung cancer from controls. Majority of the differentiating components were downregulated in cancer samples, including amino acids, carboxylic acids and tocopherols, whereas benzaldehyde was the only compound significantly upregulated. A classifier including nine serum metabolites allowed separation of cancer and control samples with 100% sensitivity and 95% specificity. Conclusions. Signature of serum metabolites discriminating between cancer patients and healthy participants of the early lung cancer screening program was identified using a GC/MS metabolomics approach. This signature, though not validated in an independent dataset, deserves further investigation in a larger cohort study.