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https://www.readbyqxmd.com/read/29155996/hierarchical-attention-networks-for-information-extraction-from-cancer-pathology-reports
#1
Shang Gao, Michael T Young, John X Qiu, Hong-Jun Yoon, James B Christian, Paul A Fearn, Georgia D Tourassi, Arvind Ramanthan
Objective: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods: Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program...
November 16, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/29149419/changes-in-chromatin-state-reveal-arnt2-at-a-node-of-a-tumorigenic-transcription-factor-signature-driving-glioblastoma-cell-aggressiveness
#2
Alexandra Bogeas, Ghislaine Morvan-Dubois, Elias A El-Habr, François-Xavier Lejeune, Matthieu Defrance, Ashwin Narayanan, Klaudia Kuranda, Fanny Burel-Vandenbos, Salwa Sayd, Virgile Delaunay, Luiz G Dubois, Hugues Parrinello, Stéphanie Rialle, Sylvie Fabrega, Ahmed Idbaih, Jacques Haiech, Ivan Bièche, Thierry Virolle, Michele Goodhardt, Hervé Chneiweiss, Marie-Pierre Junier
Although a growing body of evidence indicates that phenotypic plasticity exhibited by glioblastoma cells plays a central role in tumor development and post-therapy recurrence, the master drivers of their aggressiveness remain elusive. Here we mapped the changes in active (H3K4me3) and repressive (H3K27me3) histone modifications accompanying the repression of glioblastoma stem-like cells tumorigenicity. Genes with changing histone marks delineated a network of transcription factors related to cancerous behavior, stem state, and neural development, highlighting a previously unsuspected association between repression of ARNT2 and loss of cell tumorigenicity...
November 17, 2017: Acta Neuropathologica
https://www.readbyqxmd.com/read/29149186/cell-cycle-time-series-gene-expression-data-encoded-as-cyclic-attractors-in-hopfield-systems
#3
Anthony Szedlak, Spencer Sims, Nicholas Smith, Giovanni Paternostro, Carlo Piermarocchi
Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics...
November 17, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29146561/recurrent-neural-networks-with-specialized-word-embeddings-for-health-domain-named-entity-recognition
#4
Iñigo Jauregi Unanue, Ehsan Zare Borzeshi, Massimo Piccardi
BACKGROUND: Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings"...
November 13, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/29144299/the-modelling-of-lead-removal-from-water-by-deep-eutectic-solvents-functionalized-cnts-artificial-neural-network-ann-approach
#5
Seef Saadi Fiyadh, Mohammed Abdulhakim AlSaadi, Mohamed Khalid AlOmar, Sabah Saadi Fayaed, Ako R Hama, Sharifah Bee, Ahmed El-Shafie
The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb(2+)...
November 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/29124504/variable-synaptic-strengths-controls-the-firing-rate-distribution-in-feedforward-neural-networks
#6
Cheng Ly, Gary Marsat
Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling...
November 10, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29118809/nonintrusive-load-monitoring-based-on-advanced-deep-learning-and-novel-signature
#7
Jihyun Kim, Thi-Thu-Huong Le, Howon Kim
Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29113103/deep-recurrent-neural-networks-for-human-activity-recognition
#8
Abdulmajid Murad, Jae-Young Pyun
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data...
November 6, 2017: Sensors
https://www.readbyqxmd.com/read/29107988/basic-emotions-and-adaptation-a-computational-and-evolutionary-model
#9
Daniela Pacella, Michela Ponticorvo, Onofrio Gigliotta, Orazio Miglino
The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment...
2017: PloS One
https://www.readbyqxmd.com/read/29104967/identifying-autism-from-resting-state-fmri-using-long-short-term-memory-networks
#10
Nicha C Dvornek, Pamela Ventola, Kevin A Pelphrey, James S Duncan
Functional magnetic resonance imaging (fMRI) has helped characterize the pathophysiology of autism spectrum disorders (ASD) and carries promise for producing objective biomarkers for ASD. Recent work has focused on deriving ASD biomarkers from resting-state functional connectivity measures. However, current efforts that have identified ASD with high accuracy were limited to homogeneous, small datasets, while classification results for heterogeneous, multi-site data have shown much lower accuracy. In this paper, we propose the use of recurrent neural networks with long short-term memory (LSTMs) for classification of individuals with ASD and typical controls directly from the resting-state fMRI time-series...
September 2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29104927/retrosynthetic-reaction-prediction-using-neural-sequence-to-sequence-models
#11
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, Vijay Pande
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists...
October 25, 2017: ACS Central Science
https://www.readbyqxmd.com/read/29096993/nonlinear-dynamic-systems-identification-using-recurrent-interval-type-2-tsk-fuzzy-neural-network-a-novel-structure
#12
Ahmad M El-Nagar
In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights...
October 31, 2017: ISA Transactions
https://www.readbyqxmd.com/read/29096200/multistability-and-instability-analysis-of-recurrent-neural-networks-with-time-varying-delays
#13
Fanghai Zhang, Zhigang Zeng
This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k0 is a nonnegative integer such that k0≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties...
October 14, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29095571/generative-recurrent-networks-for-de-novo-drug-design
#14
Anvita Gupta, Alex T Müller, Berend J H Huisman, Jens A Fuchs, Petra Schneider, Gisbert Schneider
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy...
November 2, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/29081577/neural-tree-indexers-for-text-understanding
#15
Tsendsuren Munkhdalai, Hong Yu
Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models...
April 2017: Proceedings of the Conference on Computational Linguistics
https://www.readbyqxmd.com/read/29077847/drug-drug-interaction-extraction-via-hierarchical-rnns-on-sequence-and-shortest-dependency-paths
#16
Yijia Zhang, Wei Zheng, Hongfei Lin, Jian Wang, Zhihao Yang, Michel Dumontier
Motivation: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction...
October 25, 2017: Bioinformatics
https://www.readbyqxmd.com/read/29075167/uarizona-at-the-clef-erisk-2017-pilot-task-linear-and-recurrent-models-for-early-depression-detection
#17
Farig Sadeque, Dongfang Xu, Steven Bethard
The 2017 CLEF eRisk pilot task focuses on automatically detecting depression as early as possible from a users' posts to Reddit. In this paper we present the techniques employed for the University of Arizona team's participation in this early risk detection shared task. We leveraged external information beyond the small training set, including a preexisting depression lexicon and concepts from the Unified Medical Language System as features. For prediction, we used both sequential (recurrent neural network) and non-sequential (support vector machine) models...
September 2017: CEUR Workshop Proceedings
https://www.readbyqxmd.com/read/29072597/inferring-interaction-force-from-visual-information-without-using-physical-force-sensors
#18
Wonjun Hwang, Soo-Chul Lim
In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations...
October 26, 2017: Sensors
https://www.readbyqxmd.com/read/29068076/emg-based-estimation-of-limb-movement-using-deep-learning-with-recurrent-convolutional-neural-networks
#19
Peng Xia, Jie Hu, Yinghong Peng
A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model...
October 25, 2017: Artificial Organs
https://www.readbyqxmd.com/read/29064785/predictive-coding-for-dynamic-visual-processing-development-of-functional-hierarchy-in-a-multiple-spatiotemporal-scales-rnn-model
#20
Minkyu Choi, Jun Tani
This letter proposes a novel predictive coding type neural network model, the predictive multiple spatiotemporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatiotemporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network can imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error...
October 24, 2017: Neural Computation
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