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This paper presents a new lung tumor motion prediction method for tumor following radiation therapy. An essential core of the method is accurate estimation of complex fluctuation of time-variant periodical nature of lung tumor motion. Such estimation can be achieved by using a multiple time-variant seasonal autoregressive integral moving average (TVSARIMA) model in which several windows of different...
Lung cancer has been one of the major causes of cancer-related death worldwide. To predict survival outcomes of lung cancer patients, many prognosis gene sets were identified by using gene expression microarrays. However, these gene sets were often inconsistent across independent cohorts. To identify genes with more consistency, we combined gene expression and copy number variations (CNVs). Affymetrix...
Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify “Breast Cancer Disease” tumor using neural network with Feed-forward Backpropagation Algorithm to classify...
Ultra-wideband (UWB) microwave imaging is a promising technique for detecting early stage breast cancer, which exploits the significant contrast in dielectric properties between normal and malignant breast tissues. In this paper, we have proposed a new modified compensation method and beamforming technique for microwave imaging. We used a three dimensional (3-D) Finite Integration Technique (FIT)...
Content-based image retrieval (CBIR) has been an active research area since mid 90's with major focus on feature extraction, due to its significant impact on image retrieval performance. When applying CBIR in the medical domain, different imaging modalities and anatomical regions require different feature extraction methods that integrate some domain-specific knowledge for effective image retrieval...
This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further...
Pancreatic cancer (PC) is the fourth leading cause of cancer death in the United States, with 4% survival 5 years after diagnosis. Patients with pancreatic cancer are usually diagnosed at late stages, when the disease is incurable. Sensitive and more specific biomarkers are thus critical for supporting new prevention, diagnostic or therapeutic strategies. Here, we report mass spectrometry-based metabolomic...
We developed a procedure for indentifying transcriptional master regulators (MRs) related to special biological phenomena, such as diseases, in conjunction with network screening and inference. Network screening is a system for detecting activated transcriptional regulatory networks under particular conditions, based on the estimation of the graph structure consistency with the measured data. Since...
Collections of tumor genomes created by insertional mutagenesis experiments, e.g., the Retroviral Tagged Cancer Gene Database, can be analyzed to find connections between mutations of specific genes and cancer. Such connections are found by identifying the locations of insertions or groups of insertions that frequently occur in the collection of tumor genomes. Recent work has employed a kernel density...
The study was to compare principle component (PC) versus partial least square (PLS) regression, the former unsupervised and the latter supervised gene component analysis, for highly complicated and correlated microarray gene expression profile. Projection of derived classifiers into independent samples for clinical phenotype prediction was evaluated as well. Previous studies had suggested that PLS...
In this work, we introduce a computational approach that automatically detects stained follicles in IHC-stained follicular lymphoma slides using various biomarkers. This novel approach is to process whole-slide and Giga-byte scaled pathology images at multi-resolution levels. The average segmentation accuracy achieves at 83.09±6.25%. Such a computerized analysis of images is expected to provide a...
A cell culture monitoring platform, called Sensing Cell Culture Flask (SCCF), together with results from measurements in tumor cell cultures is presented. The SCCF can be equipped with oxygen, NO, pH and temperature sensors. In this work measurements from an amperometric oxygen sensor array are shown. Two different cell lines from breast cancer and a brain tumor were tested. Incubation was done under...
In this paper, we designed a Computer-Aided-Diagnosis (CAD) system for lesion detection in breast MR images. The CAD process begins with analysis of MR images to detect the existence of lesion. If lesion exists, it is then coloured based on its type; benign, suspicious or malignant. Our CAD system enables better visualization of the lesions and improves accuracy as well as speed for breast cancer...
Recent studies on the geometry of fractals indicate that tumors with irregular shapes can be utilized for the study of the morphology and diagnosis of cancerous cases. In this paper, we deal with the fractal modeling of the mammographic images and their background morphology. It is shown that the use of fractal modeling as applied to a given image can clearly discern cancerous zones from noncancerous...
The liver is a common site for the occurrence of tumors. Automatic hepatic lesion segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some...
Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related...
Genetic transfer and regulation methodologies in vivo are essential for analysis and manipulation of cells and tissue functions. A variety of such systems have also been applied to basic and preclinical studies on gene therapy and regenerative medicine, etc., while clinical utilization has not been successful enough so far. The gene transfection efficiencies of nonviral vectors are drastically improved...
We develop a three-dimensional (3-D) microwave imaging technique, which is extended from the forward-backward time-stepping (FBTS) algorithm and the Tikhonov's regularization approach, to determine the sizes and positions of tumors within breast. The effectiveness of reconstructing quantitatively of breast composition using the presented technique is demonstrated by a numerical example based on a...
We formulate the control problem in gene regulatory networks as an inverse perturbation problem, which provides the feasible set of perturbations that force the network to transition from an undesirable steady-state distribution to a desirable one. We derive a general characterization of such perturbations in an appropriate basis representation. We subsequently consider the optimal perturbation, which...
The inference of gene predictors in the gene regulatory network (GRN) has become an important research area in the genomics and medical disciplines. Accurate predicators are necessary for constructing the GRN model and to enable targeted biological experiments that attempt to validate or control the regulation process. In this paper, we implement a SAT-based algorithm to determine the gene predictor...
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