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https://www.readbyqxmd.com/read/29221521/china-s-efforts-on-management-surveillance-and-research-of-noncommunicable-diseases-ncd-scorecard-project
#1
Xiao-Lei Zhu, Jie-Si Luo, Xiao-Chang Zhang, Yi Zhai, Jing Wu
BACKGROUND: The incidence of noncommunicable diseases (NCDs) is rising dramatically throughout the world. Aspects of researches concerned with the improvement and development of prevention and control of NCDs have been conducted. Furthermore, the influence of most determinants of the major NCDs has showed that a broad and deep response involving stakeholders in different sectors is required in the prevention and control of NCDs. OBJECTIVE: China has experienced an increase in NCDs in a short period compared with many countries...
May 2017: Annals of Global Health
https://www.readbyqxmd.com/read/29220314/random-access-memories-a-new-paradigm-for-target-detection-in-high-resolution-aerial-remote-sensing-images
#2
Zhengxia Zou, Zhenwei Shi
We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)...
March 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29219084/an-improved-bayesian-network-method-for-reconstructing-gene-regulatory-network-based-on-candidate-auto-selection
#3
Linlin Xing, Maozu Guo, Xiaoyan Liu, Chunyu Wang, Lei Wang, Yin Zhang
BACKGROUND: The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics...
November 17, 2017: BMC Genomics
https://www.readbyqxmd.com/read/29219072/a-deep-auto-encoder-model-for-gene-expression-prediction
#4
Rui Xie, Jia Wen, Andrew Quitadamo, Jianlin Cheng, Xinghua Shi
BACKGROUND: Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE)...
November 17, 2017: BMC Genomics
https://www.readbyqxmd.com/read/29219068/predicting-enhancers-with-deep-convolutional-neural-networks
#5
Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang
BACKGROUND: With the rapid development of deep sequencing techniques in the recent years, enhancers have been systematically identified in such projects as FANTOM and ENCODE, forming genome-wide landscapes in a series of human cell lines. Nevertheless, experimental approaches are still costly and time consuming for large scale identification of enhancers across a variety of tissues under different disease status, making computational identification of enhancers indispensable. RESULTS: To facilitate the identification of enhancers, we propose a computational framework, named DeepEnhancer, to distinguish enhancers from background genomic sequences...
December 1, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29218921/machine-learning-and-deep-analytics-for-biocomputing-call-for-better-explainability
#6
Dragutin Petkovic, Lester Kobzik, Christopher Re
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use information explaining why and how the MLDA approach made its decisions. We believe that much greater effort is needed to address the issue of MLDA explainability because of: 1) the ever increasing use and dependence on MLDA in biocomputing including the need for increased adoption by non-MLD experts; 2) the diversity, complexity and scale of biocomputing data and MLDA algorithms; 3) the emerging importance of MLDA-based decisions in patient care, in daily research, as well as in the development of new costly medical procedures and drugs...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218895/deep-integrative-analysis-for-survival-prediction
#7
Chenglong Huang, Albert Zhang, Guanghua Xiao
Survival prediction is very important in medical treatment. However, recent leading research is challenged by two factors: 1) the datasets usually come with multi-modality; and 2) sample sizes are relatively small. To solve the above challenges, we developed a deep survival learning model to predict patients' survival outcomes by integrating multi-view data. The proposed network contains two sub-networks, one view-specific and one common sub-network. We designated one CNN-based and one FCN-based sub-network to efficiently handle pathological images and molecular profiles, respectively...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218894/mri-to-mgmt-predicting-methylation-status-in-glioblastoma-patients-using-convolutional-recurrent-neural-networks
#8
Lichy Han, Maulik R Kamdar
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the O6-methylguanine methyltransferase (MGMT) gene may impact the efficacy and sensitivity of temozolomide, and hence may affect overall patient survival. Microscopic genetic changes may manifest as macroscopic morphological changes in the brain tumors that can be detected using magnetic resonance imaging (MRI), which can serve as noninvasive biomarkers for determining methylation of MGMT regulatory regions...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218875/mapping-patient-trajectories-using-longitudinal-extraction-and-deep-learning-in-the-mimic-iii-critical-care-database
#9
Brett K Beaulieu-Jones, Patryk Orzechowski, Jason H Moore
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient's interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29218871/extracting-a-biologically-relevant-latent-space-from-cancer-transcriptomes-with-variational-autoencoders
#10
Gregory P Way, Casey S Greene
The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29217875/deep-auto-context-convolutional-neural-networks-for-standard-dose-pet-image-estimation-from-low-dose-pet-mri
#11
Lei Xiang, Yu Qiao, Dong Nie, Le An, Qian Wang, Dinggang Shen
Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient's exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI)...
December 6, 2017: Neurocomputing
https://www.readbyqxmd.com/read/29215876/deep-learning-of-atomically-resolved-scanning-transmission-electron-microscopy-images-chemical-identification-and-tracking-local-transformations
#12
Maxim Ziatdinov, Ondrej Dyck, Artem Maksov, Xufan Li, Xiahan Sang, Kai Xiao, Raymond R Unocic, Rama Vasudevan, Stephen Jesse, Sergei V Kalinin
Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent...
December 7, 2017: ACS Nano
https://www.readbyqxmd.com/read/29215763/breast-cancer-the-translation-of-big-genomic-data-to-cancer-precision-medicine
#13
REVIEW
Siew-Kee Low, Hitoshi Zembutsu, Yusuke Nakamura
Cancer is a complex genetic disease that consequence from the accumulation of genomic alterations, in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past two decades, genomic research has advanced remarkably, evolving from single-gene to whole-genome screening by using genome-wide association study (GWAS) and next generation sequencing (NGS) that contributing to big genomic data. International collaborative efforts have contributed in curating these data to identify clinically significant alterations that could be used in the clinical settings...
December 7, 2017: Cancer Science
https://www.readbyqxmd.com/read/29215311/classification-of-breast-cancer-from-ultrasound-imaging-using-a-generic-deep-learning-analysis-software-a-pilot-study
#14
Anton S Becker, Michael Mueller, Elina Stoffel, Magda Marcon, Soleen Ghafoor, Andreas Boss
PURPOSE: To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience. METHODS: In this IRB-approved, HIPAA compliant, retrospective study, all breast ultrasound examinations from January 1, 2014, to December 31, 2014 were reviewed. Patients with postsurgical scars, initially indeterminate, or malignant lesions with histological diagnoses or 2-year follow-up were included...
December 7, 2017: British Journal of Radiology
https://www.readbyqxmd.com/read/29210882/fluency-in-nursing-education-and-simulation-a-concept-analysis
#15
Audra Lewis
AIM: The aim of the study was to realign how nurses view simulation in nursing education as a means of facilitating fluency in knowledge and action to promote expertise in practice. BACKGROUND: Nursing expertise is attained by translating complex phenomena across multiple representations and by constructing meaning through experience. Simulation provides learners the experiences necessary to develop fluency in thought and action. METHOD: Procedures outlined by Hupcey and Penrod (2005) and Walker and Avant (2011) were used to identify uses, defining attributes, philosophical assumptions, contextual factors, and values of the concept...
November 27, 2017: Nursing Education Perspectives
https://www.readbyqxmd.com/read/29210358/computer-aided-assessment-of-breast-density-comparison-of-supervised-deep-learning-and-feature-based-statistical-learning
#16
Songfeng Li, Jun Wei, Heang-Ping Chan, Mark A Helvie, Marilyn A Roubidoux, Yao Lu, Chuan Zhou, Lubomir M Hadjiiski, Ravi K Samala
Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DM). The input "for processing" DM was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800µm×800µm from 100µm×100µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method...
December 6, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/29209408/panicle-seg-a-robust-image-segmentation-method-for-rice-panicles-in-the-field-based-on-deep-learning-and-superpixel-optimization
#17
Xiong Xiong, Lingfeng Duan, Lingbo Liu, Haifu Tu, Peng Yang, Dan Wu, Guoxing Chen, Lizhong Xiong, Wanneng Yang, Qian Liu
Background: Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field's complex background, rice panicle segmentation in the field is a very large challenge. Results: In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization...
2017: Plant Methods
https://www.readbyqxmd.com/read/29209363/a-new-approach-for-mobile-advertising-click-through-rate-estimation-based-on-deep-belief-nets
#18
Jie-Hao Chen, Zi-Qian Zhao, Ji-Yun Shi, Chong Zhao
In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29209036/a-universal-3d-voxel-descriptor-for-solid-state-material-informatics-with-deep-convolutional-neural-networks
#19
Seiji Kajita, Nobuko Ohba, Ryosuke Jinnouchi, Ryoji Asahi
Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI...
December 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29208513/clinical-evaluation-of-atlas-and-deep-learning-based-automatic-contouring-for-lung-cancer
#20
Tim Lustberg, Johan van Soest, Mark Gooding, Devis Peressutti, Paul Aljabar, Judith van der Stoep, Wouter van Elmpt, Andre Dekker
BACKGROUND AND PURPOSE: Contouring of organs at risk (OARs) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. MATERIAL AND METHODS: Twenty CT scans of stage I-III NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation...
December 2, 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
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