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Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This...
Aberrant changes to interactions among cellular components have been conjectured to be potential causes of abnormalities in cellular functions. By systematic analysis of high-throughput-omics data, researchers hope to detect potential associations among measured variables for better biomarker identification and phenotype prediction. In this paper, we focus on the methods to measure pairwise interactive...
The aim of this paper is (i)to study breast cancer growth by mean of a mathematical model describing cell population dynamics during cancer growth, and (ii)to use this model to reproduce and explain experimental data. We started from a linear model describing cancer subpopulations evolution based on the Cancer Stem Cell (CSC) theory, and we added feedback mechanisms from the cell populations to mimic...
Glioblastoma multiforme (GBM) is the most fatal malignant type of brain tumor with a very poor prognosis with a median survival of around one year. Numerous studies have reported tumor subtypes that consider different characteristics on individual patients, which may play important roles in determining the survival rates in GBM. In this study, we present a pathway-based clustering method using Restricted...
Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHRs in breast cancer risk prediction. We conducted a retrospective case-control study, gathering patients' ICD-9 diagnosis codes from an existing EHR data repository. Based on the hierarchical structure of ICD-9 codes, which are composed of 3-5...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
This article proposed ‘TLiSVM’ or ‘3LiSVM’ (Triple Linear SVM Weight) as an alternative technique for dimensionality reduction with a Support Vector Machine (SVM) classifier on a two-class dataset. The efficiency of TLiSVM was compared with two chosen techniques, including Linear SVM Weight (LiSVM) and Double Linear SVM Weight (DLiSVM). Three datasets, including DLBCL, Duke Breast-Cancer and Leukemia,...
Effective data management of clinical information is well-known to be a very complex task. The breast cancer context provides a good example of how challenging the problem is in Information Systems terms. In fact, it is one of the basic problems faced by clinicians who work in this area. However, this problem is even bigger when clinicians and biologists try to relate these data to biological data...
Data mining (DM) is a collection of algorithms that are used to find some novel, useful and interesting knowledge in databases. DM algorithms are based on applied fields of mathematics and informatics, such as mathematical statistics, probability theory, information theory, neural networks. Some methods of these fields can be used to find hidden relation between data, what can be used to create models...
Soft tissue force modeling with the approach of creating a force-feedback simulator for training medical skills has been the focus of many attempts up to now. The most important parameter considered in soft tissue modeling, is its being real-time along with its precision and high sensitivity. In this article, using ANFIS (Adaptive Neuro Fuzzy Inference System), a neuro-fuzzy model is presented for...
Medical informatics mainly deals with finding solutions for the issues related to the diagnosis and prognosis of various deadly diseases using machine learning and data mining approaches. One such disease is breast cancer, killing millions of people, especially women. In this paper we propose a bio inspired model called BATELM which is a combination of Bat algorithm (BAT) and Extreme Learning Machines...
Choosing an appropriate cancer treatment is potentially the most important task in the treatment of a cancer patient. If it were possible to identify the best option for a patient (or at minimum to remove options that will not help the patient), then the general prognosis of the patient improves. However, this task becomes much more subtle due to characteristics such as high dimensionality found in...
This paper proposes a novel approach to select features that are jointly predictive of survival times and classification within subgroups. Both tasks are common but generally tackled independently in clinical data analysis. Here we propose an embedded feature selection to select common markers, i.e. genes, for both tasks seen as a multi-objective optimization. The Coxlogit model relies on a Cox proportional...
Our Genomic Relevance Parameterization (GReP) model aims to explore a possible relationship between gene expression values from breast cancer patients and mathematical tumor growth modeling parameters calculated using data from clinical and preclinical measurements. We introduce two methods to relate genomic information and the tumor growth measurements. One method explores the impact of exponentiation...
Breast cancer is the most common malignant tumor for women. In the past twenty years, the incidence of breast cancer continues to rise. Then, the diagnosis and treatment of the breast cancer have become an extremely urgent work to do. In this study, we intend to build a diagnostic model of breast cancer by using data mining techniques. A feature selection method, INTERACT is applied to select relevant...
Breast cancer is the world's second most frequent type of cancer and in Japan it is the third most frequent one. The prognosis of its recurrence, after a first treatment, is very important to increase the survival rate of a patient. This work shows the application of the k-Nearest Neighbors (kNN) method to prognosis breast cancer and also proposes a method to select a good setting with the parameters...
In this paper, we propose an efficient algorithm Support Vector Machines with multiple kernels based on Isometric feature mapping(Isomap) in the process of breast cancer classification. We use Wisconsin Diagnostic Breast Cancer (WDBC) as our original data set. The first step, we use Isomap to project high dimensional breast cancer data into a much lower dimensional space. Second, we use SVM with multiple...
The ability of environmental epidemiology to determine the relationships between health and environmental insults has become exceedingly difficult. The multifactorial nature of disease and the diversity of the insults, which include biologic, physical, social and cultural factors, combined with genetic susceptibility, suggest the need to incorporate comprehensive perspectives of multidisciplinary...
Identification of condition-specific protein interaction subnetworks has emerged as an attractive research field to reveal molecular mechanisms of diseases and provide reliable network biomarkers for disease diagnosis. Several methods have been proposed, which integrate gene expression and protein-protein interaction (PPI) data to identify subnetworks. However, existing methods treat differential...
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