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https://www.readbyqxmd.com/read/28535188/molecular-heterogeneity-at-the-network-level-high-dimensional-testing-clustering-and-a-tcga-case-study
#1
Nicolas Städler, Frank Dondelinger, Steven M Hill, Rehan Akbani, Yiling Lu, Gordon B Mills, Sach Mukherjee
Motivation: Molecular pathways and networks play a key role in basic and disease biology. An emerging notion is that networks encoding patterns of molecular interplay may themselves differ between contexts, such as cell type, tissue or disease (sub)type. However, while statistical testing of differences in mean expression levels has been extensively studied, testing of network differences remains challenging. Furthermore, since network differences could provide important and biologically interpretable information to identify molecular subgroups, there is a need to consider the unsupervised task of learning subgroups and networks that define them...
May 23, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28534800/a-deep-convolutional-neural-network-based-framework-for-automatic-fetal-facial-standard-plane-recognition
#2
Zhen Yu, Ee-Leng Tan, Dong Ni, Jing Qin, Siping Chen, Shenli Li, Baiying Lei, Tianfu Wang
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intra-class variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28534792/neural-ailc-for-error-tracking-against-arbitrary-initial-shifts
#3
Mingxuan Sun, Tao Wu, Lejian Chen, Guofeng Zhang
This paper concerns with the adaptive iterative learning control using neural networks for systems performing repetitive tasks over a finite time interval. Two standing issues of such iterative learning control processes are addressed: one is the initial condition problem and the other is that related to the approximation error. Instead of the state tracking, an error tracking approach is proposed to tackle the problem arising from arbitrary initial shifts. The desired error trajectory is prespecified at the design stage, suitable to different tracking tasks...
May 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28534781/reviving-the-two-state-markov-chain-approach
#4
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall...
May 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534753/barrier-function-based-neural-adaptive-control-with-locally-weighted-learning-and-finite-neuron-self-growing-strategy
#5
Zi-Jun Jia, Yong-Duan Song
This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation...
June 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28534414/estimating-pm2-5-concentrations-in-the-conterminous-united-states-using-the-random-forest-approach
#6
Xuefei Hu, Jessica Hartmann Belle, Xia Meng, Avani Wildani, Lance Waller, Matthew Strickland, Yang Liu
To estimate PM2.5 concentrations, many parametric regression models have been developed, while non-parametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating Aerosol Optical Depth (AOD) data, meteorological fields, and land use variables to estimate daily 24-hour averaged ground level PM2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability...
May 23, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28534043/familiarity-detection-is-an-intrinsic-property-of-cortical-microcircuits-with-bidirectional-synaptic-plasticity
#7
Xiaoyu Zhang, Han Ju, Trevor B Penney, Antonius M J VanDongen
Humans instantly recognize a previously seen face as "familiar." To deepen our understanding of familiarity-novelty detection, we simulated biologically plausible neural network models of generic cortical microcircuits consisting of spiking neurons with random recurrent synaptic connections. NMDA receptor (NMDAR)-dependent synaptic plasticity was implemented to allow for unsupervised learning and bidirectional modifications. Network spiking activity evoked by sensory inputs consisting of face images altered synaptic efficacy, which resulted in the network responding more strongly to a previously seen face than a novel face...
May 2017: ENeuro
https://www.readbyqxmd.com/read/28532500/extending-access-to-essential-services-against-constraints-the-three-tier-health-service-delivery-system-in-rural-china-1949-1980
#8
Xing Lin Feng, Melisa Martinez-Alvarez, Jun Zhong, Jin Xu, Beibei Yuan, Qingyue Meng, Dina Balabanova
BACKGROUND: China has made remarkable progress in scaling up essential services during the last six decades, making health care increasingly available in rural areas. This was partly achieved through the building of a three-tier health system in the 1950s, established as a linked network with health service facilities at county, township and village level, to extend services to the whole population. METHODS: We developed a Theory of Change to chart the policy context, contents and mechanisms that may have facilitated the establishment of the three-tier health service delivery system in rural China...
May 23, 2017: International Journal for Equity in Health
https://www.readbyqxmd.com/read/28530717/sparse-coding-with-memristor-networks
#9
Patrick M Sheridan, Fuxi Cai, Chao Du, Wen Ma, Zhengya Zhang, Wei D Lu
Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors...
May 22, 2017: Nature Nanotechnology
https://www.readbyqxmd.com/read/28530547/application-of-machine-learning-approaches-for-protein-protein-interactions-prediction
#10
Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao, Bing Niu
BACKGROUND: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed...
May 22, 2017: Medicinal Chemistry
https://www.readbyqxmd.com/read/28530225/a-canonical-neural-mechanism-for-behavioral-variability
#11
Ran Darshan, William E Wood, Susan Peters, Arthur Leblois, David Hansel
The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours...
May 22, 2017: Nature Communications
https://www.readbyqxmd.com/read/28529068/adverse-outcome-pathways-application-to-enhance-mechanistic-understanding-of-neurotoxicity
#12
REVIEW
Anna Bal-Price, M E Meek
Recent developments have prompted the transition of empirically based testing of late stage toxicity in animals for a range of different endpoints including neurotoxicity to more efficient and predictive mechanistically based approaches with greater emphasis on measurable key events early in the progression of disease. The adverse outcome pathway (AOP) has been proposed as a simplified organizational construct to contribute to this transition by linking molecular initiating events and earlier (more predictive) key events at lower levels of biological organization to disease outcomes...
May 18, 2017: Pharmacology & Therapeutics
https://www.readbyqxmd.com/read/28527481/rethinking-anatomy-how-to-overcome-challenges-of-medical-education-s-evolution
#13
REVIEW
Bruno Guimarães, Luís Dourado, Stanislav Tsisar, José Miguel Diniz, Maria Dulce Madeira, Maria Amélia Ferreira
INTRODUCTION: Due to scientific and technological development, Medical Education has been readjusting its focus and strategies. Medical curriculum has been adopting a vertical integration model, in which basic and clinical sciences coexist during medical instruction. This context favours the introduction of new complementary technology-based pedagogical approaches. Thus, even traditional core sciences of medical curriculum, like Anatomy, are refocusing their teaching/learning paradigm...
February 27, 2017: Acta Médica Portuguesa
https://www.readbyqxmd.com/read/28526212/3d-deeply-supervised-network-for-automated-segmentation-of-volumetric-medical-images
#14
Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, Pheng-Ann Heng
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN...
May 8, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28525568/scenery-a-web-application-for-causal-network-reconstruction-from-cytometry-data
#15
Georgios Papoutsoglou, Giorgos Athineou, Vincenzo Lagani, Iordanis Xanthopoulos, Angelika Schmidt, Szabolcs Éliás, Jesper Tegnér, Ioannis Tsamardinos
Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data...
May 19, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28523139/an-overview-of-the-use-of-artificial-neural-networks-in-lung-cancer-research
#16
EDITORIAL
Luca Bertolaccini, Piergiorgio Solli, Alessandro Pardolesi, Antonello Pasini
The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification...
April 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/28522969/equilibrium-propagation-bridging-the-gap-between-energy-based-models-and-backpropagation
#17
Benjamin Scellier, Yoshua Bengio
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28522735/d3-receptors-regulate-excitability-in-a-unique-class-of-prefrontal-pyramidal-cell
#18
Rebecca L Clarkson, Alayna T Liptak, Steven M Gee, Vikaas S Sohal, Kevin J Bender
The D3 dopamine receptor, a member of the Gi-coupled D2-family of dopamine receptors, is expressed throughout limbic circuits affected in neuropsychiatric disorders, including prefrontal cortex. These receptors are important for prefrontal executive function, as pharmacological and genetic manipulations that affect prefrontal D3 receptors alter anxiety, social interaction, and reversal learning. And yet, the mechanisms by which D3 receptors regulate prefrontal circuits, and whether D3 receptors regulate specific prefrontal subnetworks, remain unknown...
May 17, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28521127/synaptic-plasticity-engrams-and-network-oscillations-in-amygdala-circuits-for-storage-and-retrieval-of-emotional-memories
#19
REVIEW
Marco Bocchio, Sadegh Nabavi, Marco Capogna
The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition, consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity in BLA neurons is essential for associative emotional learning and is a candidate mechanism through which subsets of BLA neurons (commonly termed "engram") are recruited during learning and reactivated during memory retrieval. In parallel, synchronous oscillations in the theta and gamma bands between the BLA and interconnected structures have been shown to occur during consolidation and retrieval of emotional memories...
May 17, 2017: Neuron
https://www.readbyqxmd.com/read/28521122/ready-steady-go-imaging-cortical-activity-during-movement-planning-and-execution
#20
Arkarup Banerjee, Michael A Long
In this issue of Neuron, Chen et al. (2017) examine premotor activity representing motor planning, Allen et al. (2017) observe the global representation of goal-directed movement on the cortical network, and Makino et al. (2017) track changes in such dynamics throughout learning.
May 17, 2017: Neuron
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