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cancer machine learning

Hasseeb Azzawi, Jingyu Hou, Yong Xiang, Russul Alanni
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. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models...
October 2016: IET Systems Biology
Simon Kocbek, Lawrence Cavedon, David Martinez, Christopher Bain, Chris Mac Manus, Gholamreza Haffari, Ingrid Zukerman, Karin Verspoor
OBJECTIVE: Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance...
October 11, 2016: Journal of Biomedical Informatics
E Tan, F Lin, L Sheck, P Salmon, S Ng
PURPOSE: To derive a decision rule for predicting surgical complexity in periorbital basal cell carcinoma (pBCC). DESIGN: Prospective, cohort study. PARTICIPANTS: Patients referred to an oculoplastic service for excision of pBCC from September 2010 to November 2015 METHODS: This study was conducted at two centres in New Zealand from September 2010 to November 2015. Baseline demographic information, and an initial assessment of operative complexity (a four-point grading scale) were collected...
October 14, 2016: Journal of the European Academy of Dermatology and Venereology: JEADV
Robert J Gillies, Thomas Beyer
Over the past decades, imaging in oncology has been undergoing a "quiet" revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ Important advances along this path have been the increasing power of MRI, which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, PET imaging allows quantitative assessment of the spatial localization of positron-emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules...
October 11, 2016: Cancer Research
Wen-Wai Yim, Sharon W Kwan, Meliha Yetisgen
BACKGROUND: Anaphoric references occur ubiquitously in clinical narrative text. However, the problem, still very much an open challenge, is typically less aggressively focused on in clinical text domain applications. Furthermore, existing research on reference resolution is often conducted disjointly from real-world motivating tasks. OBJECTIVE: In this paper, we present our machine-learning system that automatically performs reference resolution and a rule-based system to extract tumor characteristics, with component-based and end-to-end evaluations...
October 8, 2016: Journal of Biomedical Informatics
Hansaim Lim, Aleksandar Poleksic, Yuan Yao, Hanghang Tong, Di He, Luke Zhuang, Patrick Meng, Lei Xie
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification...
October 2016: PLoS Computational Biology
Ahmed Allam, Peter J Schulz, Michael Krauthammer
BACKGROUND: As the Internet becomes the number one destination for obtaining health-related information, there is an increasing need to identify health Web pages that convey an accurate and current view of medical knowledge. In response, the research community has created multicriteria instruments for reliably assessing online medical information quality. One such instrument is DISCERN, which measures health Web page quality by assessing an array of features. In order to scale up use of the instrument, there is interest in automating the quality evaluation process by building machine learning (ML)-based DISCERN Web page classifiers...
October 5, 2016: Journal of the American Medical Informatics Association: JAMIA
Tahereh Marvdashti, Lian Duan, Sumaira Z Aasi, Jean Y Tang, Audrey K Ellerbee Bowden
We report the first fully automated detection of basal cell carcinoma (BCC), the most commonly occurring type of skin cancer, in human skin using polarization-sensitive optical coherence tomography (PS-OCT). Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy. Our classifier achieved 95...
September 1, 2016: Biomedical Optics Express
Andrew B Rosenkrantz, Ankur M Doshi, Luke A Ginocchio, Yindalon Aphinyanaphongs
RATIONALE AND OBJECTIVES: This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. MATERIALS AND METHODS: We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations)...
September 27, 2016: Academic Radiology
Riku Turkki, Nina Linder, Panu E Kovanen, Teijo Pellinen, Johan Lundin
BACKGROUND: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. METHODS: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody...
2016: Journal of Pathology Informatics
Zena M Hira, Duncan F Gillies
In order to provide the most effective therapy for cancer, it is important to be able to diagnose whether a patient's cancer will respond to a proposed treatment. Methylation profiling could contain information from which such predictions could be made. Currently, hypothesis testing is used to determine whether possible biomarkers for cancer progression produce statistically significant results. However, this approach requires the identification of individual genes, or sets of genes, as candidate hypotheses, and with the increasing size of modern microarrays, this task is becoming progressively harder...
2016: Cancer Informatics
Iván Gómez, Nuria Ribelles, Leonardo Franco, Emilio Alba, José M Jerez
Discretization of continuous variables is a common practice in medical research to identify risk patient groups. This work compares the performance of gold-standard categorization procedures (TNM+A protocol) with that of three supervised discretization methods from Machine Learning (CAIM, ChiM and DTree) in the stratification of patients with breast cancer. The performance for the discretization algorithms was evaluated based on the results obtained after applying standard survival analysis procedures such as Kaplan-Meier curves, Cox regression and predictive modelling...
November 2016: Computer Methods and Programs in Biomedicine
Zhan Ye, Ahmad P Tafti, Karen Y He, Kai Wang, Max M He
BACKGROUND: Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. RESULTS: In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database...
2016: PloS One
Ashis Kumar Dhara, Sudipta Mukhopadhyay, Anirvan Dutta, Mandeep Garg, Niranjan Khandelwal
Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user...
September 27, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
G Valdes, A T Berman, C Hann-Hsiang, T D Solberg, C B Simone
No abstract text is available yet for this article.
October 1, 2016: International Journal of Radiation Oncology, Biology, Physics
M W Macomber, A Samareh, W A Chaovalitwongse, S R Bowen, S A Patel, J Zeng, M Nyflot
No abstract text is available yet for this article.
October 1, 2016: International Journal of Radiation Oncology, Biology, Physics
J Zhang, O Ates, A Li
No abstract text is available yet for this article.
October 1, 2016: International Journal of Radiation Oncology, Biology, Physics
T-D Ngo, T-D Tran, M-T Le, K-M Thai
The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%)...
September 2016: SAR and QSAR in Environmental Research
Ying Liu, Yoganand Balagurunathan, Thomas Atwater, Sanja Antic, Qian Li, Ronald C Walker, Gary Smith, Pierre P Massion, Matthew B Schabath, Robert J Gillies
PURPOSE: We propose a systematic methodology to quantify incidentally identified pulmonary nodules based on observed radiological traits (semantics) quantified on a point scale and a machine learning method using these data to predict cancer status. MATERIALS AND METHODS: We investigated 172 patients who had low-dose computed tomography (LDCT) images, with 102 and 70 patients grouped into training and validation cohorts, respectively. On the images, 24 radiological traits were systematically scored and a linear classifier was built to relate the traits to malignant status...
September 23, 2016: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
Behrooz Torabi Moghadam, Michal Dabrowski, Bozena Kaminska, Manfred G Grabherr, Jan Komorowski
BACKGROUND: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. RESULTS: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites...
2016: BMC Bioinformatics
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