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Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data...
Breast Cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Woman within the age of 40-69 have more risk of breast cancer. Though breast cancer leads to death, early detection of breast cancer can increase the survival rate. Clustered Microcalcification (MC) in mammograms is the major indication for early detection of breast cancer. MC is quiet...
This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological...
For last few years, researchers are increasingly employing machine learning methods in the domain of cancer prognosis. The main reason behind these efforts is to help oncologist to make accurate and less invasive decisions for the patient's treatment. Moreover, it would relieve many cancer patients from agonizingly complex surgical treatments and their colossal costs. In this paper, we have proposed...
Identifying historical records of patients who are similar to the new patient could help to retrieve similar reference cases for predicting the clinical outcome of the new patient. Amongst different potential applications, this study illustrates use of patient similarity in predicting survival of patients suffering from hepatocellular carcinoma (HCC) treated with locoregional chemotherapy. This study...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
Feature selection continues to grow in importance in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such datasets, tools are required that can reliably distinguish the most relevant features. The latter is a useful goal in itself (e.g. such...
The characterization and quantitative description of histological images is not a simple problem. To reach a final diagnosis, usually the specialist relies on the analysis of characteristics easily observed, such as cells size, shape, staining and texture, but also depends on the hidden information of tissue localization, physiological and pathological mechanisms, clinical aspects, or other etiological...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has to rule out cancer for each...
There are several successful approaches dealing with imbalanced datasets. In this paper, the Fuzzy Labeled Neural Gas (FLNG) is extended to work with this type of data. The proposed approach is based on assigning two different values in the learning rate depending on the data vector membership of the class. The technique is tested with several datasets and compared with other approaches. The results...
Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing...
Exploring micro RNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method for identifying functional miRNA-mRNA modules from heterogeneous expression...
Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods to deal with this problem, including resampling techniques and cost-sensitive learning (CSL), most of them focus on...
Adaptive Local Hyperplane (ALH) is a recently proposed classifier for the multi-class classification problems and it has shown encouraging performance in many pattern recognition problems. However, ALH's performance over many general classification datasets has only been tested by using a single loop of cross-validation procedure, where the whole datasets are used for both hyper-parameter determination...
Computer aided diagnosis systems using machine learning techniques have been developed in order to assist radiologists' diagnosis and overcome inherent limitations of conventional mammography. Such systems base their diagnosis on image features extracted from mammograms, which are mainly related to the shape, the morphology, the texture and the position of the suspicious abnormality. Since the discrimination...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis....
Microarray technology has been broadly used for monitoring the expression levels of thousands of genes simultaneously, providing the opportunities of identifying disease-related genes by finding differentially expressed genes in different conditions. However, a great challenge of analyzing microarray data is the significant noise brought by different experimental settings, laboratory procedures, genetic...
Analyzing gene expression data from microarray devices has many important applications in medicine and biology: the diagnosis of disease, accurate prognosis for particular patients, and understanding the response of a disease to drugs, to name a few. Two classifiers, random forests and support vector machines are studied in application to micro array cancer data sets. Performance of classifiers with...
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