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https://www.readbyqxmd.com/read/28107365/parenclitic-network-analysis-of-methylation-data-for-cancer-identification
#1
Alexander Karsakov, Thomas Bartlett, Artem Ryblov, Iosif Meyerov, Mikhail Ivanchenko, Alexey Zaikin
We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures...
2017: PloS One
https://www.readbyqxmd.com/read/28096347/human-transposon-insertion-profiling-analysis-visualization-and-identification-of-somatic-line-1-insertions-in-ovarian-cancer
#2
Zuojian Tang, Jared P Steranka, Sisi Ma, Mark Grivainis, Nemanja Rodić, Cheng Ran Lisa Huang, Ie-Ming Shih, Tian-Li Wang, Jef D Boeke, David Fenyö, Kathleen H Burns
Mammalian genomes are replete with interspersed repeats reflecting the activity of transposable elements. These mobile DNAs are self-propagating, and their continued transposition is a source of both heritable structural variation as well as somatic mutation in human genomes. Tailored approaches to map these sequences are useful to identify insertion alleles. Here, we describe in detail a strategy to amplify and sequence long interspersed element-1 (LINE-1, L1) retrotransposon insertions selectively in the human genome, transposon insertion profiling by next-generation sequencing (TIPseq)...
January 17, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28095769/pcm-sabre-a-platform-for-benchmarking-and-comparing-outcome-prediction-methods-in-precision-cancer-medicine
#3
Noah Eyal-Altman, Mark Last, Eitan Rubin
BACKGROUND: Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models...
January 17, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28086747/microrna-based-pan-cancer-diagnosis-and-treatment-recommendation
#4
Nikhil Cheerla, Olivier Gevaert
BACKGROUND: The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome...
January 13, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28067293/drug-response-prediction-as-a-link-prediction-problem
#5
Zachary Stanfield, Mustafa Coşkun, Mehmet Koyutürk
Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models...
January 9, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28065767/derivation-and-internal-validation-of-a-clinical-prediction-tool-for-30-day-mortality-in-lower-gastrointestinal-bleeding
#6
Neil Sengupta, Elliot B Tapper
BACKGROUND AND AIMS: There are limited data to predict which patients with lower gastrointestinal bleeding are at risk for adverse outcomes. We aimed to develop a clinical tool based on admission variables to predict 30-day mortality in lower gastrointestinal bleeding. METHODS: We used a validated machine learning algorithm to identify adult patients hospitalized with lower gastrointestinal bleeding at an academic medical center between 2008 and 2015. The cohort was split randomly into a derivation and validation cohort...
January 5, 2017: American Journal of Medicine
https://www.readbyqxmd.com/read/28061434/systematic-evaluation-of-supervised-classifiers-for-fecal-microbiota-based-prediction-of-colorectal-cancer
#7
Luoyan Ai, Haiying Tian, Zhaofei Chen, Huimin Chen, Jie Xu, Jing-Yuan Fang
Predicting colorectal cancer (CRC) based on fecal microbiota presents a promising method for non-invasive screening of CRC, but the optimization of classification models remains an unaddressed question. The purpose of this study was to systematically evaluate the effectiveness of different supervised machine-learning models in predicting CRC in two independent eastern and western populations. The structures of intestinal microflora in feces in Chinese population (N = 141) were determined by 454 FLX pyrosequencing, and different supervised classifiers were employed to predict CRC based on fecal microbiota operational taxonomic unit (OTUs)...
January 4, 2017: Oncotarget
https://www.readbyqxmd.com/read/28060807/svm-and-svm-ensembles-in-breast-cancer-prediction
#8
Min-Wei Huang, Chih-Wen Chen, Wei-Chao Lin, Shih-Wen Ke, Chih-Fong Tsai
Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions...
2017: PloS One
https://www.readbyqxmd.com/read/28059233/cytopathological-image-analysis-using-deep-learning-networks-in-microfluidic-microscopy
#9
G Gopakumar, K Hari Babu, Deepak Mishra, Sai Siva Gorthi, Gorthi R K Sai Subrahmanyam
Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60)...
January 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
https://www.readbyqxmd.com/read/28052254/pan-cancer-immunogenomic-analyses-reveal-genotype-immunophenotype-relationships-and-predictors-of-response-to-checkpoint-blockade
#10
Pornpimol Charoentong, Francesca Finotello, Mihaela Angelova, Clemens Mayer, Mirjana Efremova, Dietmar Rieder, Hubert Hackl, Zlatko Trajanoski
The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia...
January 3, 2017: Cell Reports
https://www.readbyqxmd.com/read/28048472/tu-h-campus-jep3-04-factors-predicting-a-need-for-treatment-replanning-with-proton-radiotherapy-for-lung-cancer
#11
C Teng, G Janssens, C Ainsley, B Teo, G Valdes, B Burgdorf, A Berman, W Levin, Y Xiao, L Lin, P Gabriel, C Simone, T Solberg
PURPOSE: Proton dose distribution is sensitive to tumor regression and tissue and normal anatomy changes. Replanning is sometimes necessary during treatment to ensure continue tumor coverage or avoid overtreatment of organs at risk (OARs). We investigated action thresholds for replanning and identified both dosimetric and non-dosimetric metrics that would predict a need for replan. METHODS: All consecutive lung cancer patients (n = 188) who received definitive proton radiotherapy and had more than two evaluation CT scans at the Roberts Proton Therapy Center (Philadelphia, USA) from 2011 to 2015 were included in this study...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048384/su-d-207b-06-predicting-breast-cancer-malignancy-on-dce-mri-data-using-pre-trained-convolutional-neural-networks
#12
N Antropova, B Huynh, M Giger
PURPOSE: We investigate deep learning in the task of distinguishing between malignant and benign breast lesions on dynamic contrast-enhanced MR images (DCE-MRIs), eliminating the need for lesion segmentation and extraction of tumor features. We evaluate convolutional neural network (CNN) after transfer learning with ImageNet, a database of thousands of non-medical images. METHODS: Under a HIPAA-compliant IRB protocol, a database of 551 (357 malignant and 194 benign) breast MRI cases was collected...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048372/su-f-p-01-changing-your-oncology-information-system-a-detailed-process-and-lessons-learned
#13
C Abing
PURPOSE: Radiation Oncology departments are faced with many options for pairing their treatment machines with record and verify systems. Recently, there is a push to have a single-vendor-solution. In order to achieve this, the department must go through an intense and rigorous transition process. Our department has recently completed this process and now offer a detailed description of the process along with lessons learned. METHODS: Our cancer center transitioned from a multi-vendor department to a single-vendor department over the 2015 calendar year...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048357/su-f-r-05-multidimensional-imaging-radiomics-geodesics-a-novel-manifold-learning-based-automatic-feature-extraction-method-for-diagnostic-prediction-in-multiparametric-imaging
#14
V Parekh, M A Jacobs
PURPOSE: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient's pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). METHODS: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048166/mo-de-207b-06-computer-aided-diagnosis-of-breast-ultrasound-images-using-transfer-learning-from-deep-convolutional-neural-networks
#15
B Huynh, K Drukker, M Giger
PURPOSE: To assess the performance of using transferred features from pre-trained deep convolutional networks (CNNs) in the task of classifying cancer in breast ultrasound images, and to compare this method of transfer learning with previous methods involving human-designed features. METHODS: A breast ultrasound dataset consisting of 1125 cases and 2393 regions of interest (ROIs) was used. Each ROI was labeled as cystic, benign, or malignant. Features were extracted from each ROI using pre-trained CNNs and used to train support vector machine (SVM) classifiers in the tasks of distinguishing non-malignant (benign+cystic) vs malignant lesions and benign vs malignant lesions...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047509/su-f-j-199-predictive-models-for-cone-beam-ct-based-online-verification-of-pencil-beam-scanning-proton-therapy
#16
L Yin, G Janssens, A Lin, P Ahn, T Solberg, J McDonough, B Teo
PURPOSE: To utilize online CBCT scans to develop models for predicting DVH metrics in proton therapy of head and neck tumors. METHODS: Nine patients with locally advanced oropharyngeal cancer were retrospectively selected in this study. Deformable image registration was applied to the simulation CT, target volumes, and organs at risk (OARs) contours onto each weekly CBCT scan. Intensity modulated proton therapy (IMPT) treatment plans were created on the simulation CT and forward calculated onto each corrected CBCT scan...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047168/su-f-t-450-the-investigation-of-radiotherapy-quality-assurance-and-automatic-treatment-planning-based-on-the-kernel-density-estimation-method
#17
J Fan, J Fan, W Hu, J Wang
PURPOSE: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. METHODS: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046577/tu-h-campus-jep2-03-machine-learning-based-delineation-framework-of-gtv-regions-of-solid-and-ground-glass-opacity-lung-tumors-at-datasets-of-planning-ct-and-pet-ct-images
#18
K Ikushima, H Arimura, Z Jin, H Yabuuchi, J Kuwazuru, Y Shioyama, T Sasaki, H Honda, M Sasaki
PURPOSE: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. METHODS: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree of GTV for each voxel in the testing step...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046236/su-d-204-06-integration-of-machine-learning-and-bioinformatics-methods-to-analyze-genome-wide-association-study-data-for-rectal-bleeding-and-erectile-dysfunction-following-radiotherapy-in-prostate-cancer
#19
J Oh, S Kerns, H Ostrer, B Rosenstein, J Deasy
PURPOSE: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. METHODS: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046226/su-f-r-22-malignancy-classification-for-small-pulmonary-nodules-with-radiomics-and-logistic-regression
#20
W Huang, S Tu
PURPOSE: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. METHODS: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy...
June 2016: Medical Physics
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