Podolsky, Maxim D
(ITMO University)
,
Barchuk, Anton A
(NN Petrov Research Institute of Oncology of the USSR Ministry of Health)
,
Kuznetcov, Vladimir I
(KBST ITMO LLC)
,
Gusarova, Natalia F
(ITMO University)
,
Gaidukov, Vadim S
(ITMO University)
,
Tarakanov, Segrey A
(ITMO University)
Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatme...
Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.
Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.
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제안 방법
Each sample is described by 12600 gene expression levels. Research task for this data set was to classify cancer types.
Each sample is presented by expression levels of 12,533 genes. The task is to make a binary classification of malignant pleural mesothelioma and adenocarcinoma.
Each sample is presented by expression levels of 7,129 genes. The task was to detect adenocarcinoma.
Each sample is presented by expression levels of 2,880 genes. The task was to detect recurrences.
At present it is optimal to use machine learning methods to ascertain a definite diagnosis. Their final aim is to obtain trained algorithms which compute type and developmental character of malignant growth by usage of one or several classification attributes. These algorithms can be used by clinicians as auxiliary tools to process huge amounts of patient data for establishing diagnosis (Sun et al.
대상 데이터
Samples were divided in two sets: training (sixteen samples of each cancer type) and testing (the remaining 149 samples). Each sample is presented by expression levels of 12,533 genes. The task is to make a binary classification of malignant pleural mesothelioma and adenocarcinoma.
, 2002); Consists of 96 samples: 86 -primary adenocarcinoma (where 67 -stage I, nineteen -stage III), ten -non-neoplastic tissue. Each sample is presented by expression levels of 7,129 genes. The task was to detect adenocarcinoma.
Samples of primary tumor and adjacent non-neoplastic tissue were taken during surgical intervention from May 1994 to June 2000 in the University of Michigan Hospital. Peripheral portions of resected lung carcinomas were sectioned, evaluated by a study pathologist and compared with routine H&E sections of the same tumors, and utilized for mRNA isolation.
이론/모형
At that, effectiveness of various algorithms differs depending on analyzed data sets. To evaluate effectiveness of the algorithms and compare them it is accepted to use Receiver Operating Characteristic curve (ROC curve) and Matthews Correlation Coefficient (MCC) as a measure of the quality of binary (two-class) as well as non-binary classifications (Baldi et al., 2000).
To train the algorithms using data sets of Dana-Farber Cancer Institute, University of Michigan and University of Toronto 10-fold cross validation was used. For Brigham and Women’s Hospital data set we have used training and testing samples that have been already prepared.
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