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Straipsniai Klinikiniai tyrimai Patentai

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Tree-based model for breast cancer prognostication.

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OBJECTIVE To define prognostic groups for recurrence-free survival in breast cancer, assess relative effects of prognostic factors, and examine the influence of treatment variations on recurrence-free survival in patients with similar prognostic-factor profiles. METHODS We analyzed 1,055 patients

Breast cancer in a tree shrew (Tupaia glis).

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Data-Driven Tree Transforms and Metrics.

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We consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey

Tree-based analysis of microarray data for classifying breast cancer.

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DNA microarray data have provided us with the opportunity to assess the expression levels for thousands of genes simultaneously. One of the uses of this information is to classify cancer tumors. A noted challenge in using microarray information is analytical. Following the work of Zhang et al. (1),

Characterization of spontaneous breast tumor in tree shrews (Tupaia belangeri chinenesis).

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Breast cancer is a common malignant tumor. It is essential to develop suitable animal models for discovering novel preventive and therapeutic approaches. Tree shrews (Tupaia belangeri chinensis) have a closer evolutionary relationship with humans than do rodents, which have been widely used in

Breast cancer: have we missed the forest because of the tree?

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Breast cancer: have we missed the forest because of the tree?

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Sequencing: one big tree in the forest on breast cancer management.

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Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

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BACKGROUND The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. METHODS The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and

Predicting metastasis in breast cancer: comparing a decision tree with domain experts.

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Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support

Spatially dependent polya tree modeling for survival data.

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With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival context has received increased recent attention. A traditional way to capture a spatial pattern is to introduce frailty terms in the linear predictor of a semiparametric model, such as proportional

Breast cancer and the family tree. An issue for all practice settings.

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Comparative genomic hybridization-based oncogenetic tree model for genetic classification of breast cancer.

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OBJECTIVE To describe a genetic progression pathway in breast cancer by a maximum likelihood-based tree model representing the dependencies between chromosomal imbalances. METHODS One hundred six cases were studied by comparative genomic hybridization, followed by maximum likelihood estimation of an

Decision tree and paradigms of primary breast cancer: changes elicited by preoperative therapy.

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A small difference in decision-making causes a big impact on the long-term outcome in cancer treatment. Novel methods such as preoperative systemic treatment or sentinel node biopsy (SNB) may alter the decision-trees in primary breast cancer management. Recent data showed that preoperative

Brain Tumor Detection Using Depth-First Search Tree Segmentation.

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With the advent of image processing technologies, the in-depth portion of human body can be epitomized visually to perceive abnormalities in human anatomy. Image processing is a tool for identifying the substances and obtaining information from them. Medical image processing is a stimulating area to

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