1. The prognostic value of adaptive nuclear texture features from patient gray level entropy matrices in early stage ovarian cancer / B. Nielsen [et al.] // Anal Cell Pathol (Amst). 2012. № 4. P. 305-314.
2. Cord A., Bach F., Jeulin D. Texture classification by statistical learning from morphological image processing: application to metallic surfaces // J. Microsc. 2010. № 2. P. 159-166.
3. The value of nuclear DNA and texture analysis by digital image processing in the diagnosis of lipomatous and leiomyomatous tumours / M. Remmelink [et al.] // Anal Cell Pathol. 1996. № 1. P. 45-58.
4. Classification of melanocytic lesions with color and texture analysis using digital image processing / T. Schindewolf [et al.] // Anal Quant Cytol Histol. 1993. № 1. P. 1-11.
5. An interactive processing system for ultrasonic compound imaging, real-time image processing and texture analysis / E. Schuster [et al.] // Ultrason Imaging. 1986. № 2. P. 131-150.
6. Coelho L.P. Mahotas: Open source software for scriptable computer vision // J. of Open Research Software. 2013. № 1. P. 1-6.
7. Ferraty F., Romain Y. The Oxford handbook of functional data analysis. Oxford, New York, 2011. 494 p.
8. Gray V. Principal component analysis: methods, applications, and technology. Nova Science Publishers, 2017. 130 p.
9. Härdle W., Mori Y., Vieu P. Statistical methods for biostatistics and related fields. Berlin, New York, 2007. 370 p.
10. Huang H.C., Qin L.X. Empirical evaluation of data normalization methods for molecular classification // Peer J. 2018. № 1. P. 4584.
11. Nahid A.A., Mehrabi M.A., Kong Y. Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering // Biomed Res Int. 2018. № 2. Article ID 2362108.
12. Impact of Tumor Purity on Immune Gene Expression and Clustering Analyses across Multiple Cancer Types / J.K. Rhee [et al.] // Cancer Immunol Res. 2018. № 1. P. 87-97.
13. Zhang E., Ma X. Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages // Molecules. 2018. № 5. P 22-29.
14. Support Vector Machines (SVM) classification of prostate cancer Gleason score in central gland using multiparametric magnetic resonance images: A cross-validated study / J. Li [et al.] // Eur J. Radiol. 2018. № 3. P. 61-67.
15. Moteghaed N.Y., Maghooli K., Garshasbi M. Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine // J. Med Signals Sens. 2018. № 1. P. 1-11.
16. Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study / I. Vidic [et al.] // J. Magn Reson Imaging. 2018. № 5. P. 1205-1216.
17. Genetic Variants in Metabolic Signaling Pathways and Their Interaction with Lifestyle Factors on Breast Cancer Risk: A Random Survival Forest Analysis / S.Y. Jung [et al.] // Cancer Prev Res (Phila). 2018. № 1. P. 44-51.
18. Characteristic miRNA expression signature and random forest survival analysis identify potential cancer-driving miRNAs in a broad range of head and neck squamous cell carcinoma subtypes / Y.O. Nunez Lopez [et al.] // Rep Pract Oncol Radiother. 2018. № 1. P. 6-20.
19. Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model / H. Wang [et al.] // Sci Rep. 2018. № 1. P. 5670.
20. Hofmann W.-K. Gene expression profiling by microarrays: clinical implications. Cambridge, New York, 2006. 246 p.
Послать статью по эл. почте 