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PubMed Journals Articles About "LSTM Based Framework Biomedical Event Extraction" RSS

06:00 EDT 25th June 2019 | BioPortfolio

LSTM Based Framework Biomedical Event Extraction PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest LSTM Based Framework Biomedical Event Extraction articles that have been published worldwide.

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Showing "LSTM Based Framework Biomedical Event Extraction" PubMed Articles 1–25 of 38,000+

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. In addition, it is complicated to generate features from syntactic and dependency analysis separately. Therefore, in this paper, we propose an end to-end model based on long short-term memory (LSTM) to optimize biomedical event extraction. Experimental results de...


Contextual Label Sensitive Gated Network for Biomedical Event Trigger Extraction.

Biomedical events play a key role in improving biomedical research. Event trigger identification, extracting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in previous methods: (1) The association among contextual trigger labels which can provide significant clues is ignored. (2) The weight between word embeddings and contextual features needs to be adjusted dynamically according to the trigger ca...

Domain Transformation on Biological Event Extraction By Learning Methods.

Event extraction and annotation has become a significant focus of recent efforts in biological text mining and information extraction (IE). However, event extraction, event annotation methods, and resources have so far focused almost exclusively on a single domain. State-of-the-art studies on biological event extraction and annotation are typically domain-dependent and domain-restricted. In this paper, we adopt an approach aimed at extracting events and relations for two different tasks by generating a comm...


Exploring Semi-supervised V ariational Autoencoders for Biomedical Relation Extraction.

The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). Biomedical relation extraction aims to automatically extract biomedical relations from biomedical text for various biomedical research. State-of-the-art methods for biomedical relation extraction are primarily based on supervised machine learning and therefore depend on (sufficient) labeled data. However, creating large sets of tra...

Evaluating automated entity extraction with respect to drug and non-drug treatment strategies.

Treatment used in a randomized clinical trial is a critical data element both for physicians at the point of care and reviewers who are evaluating different interventions. Much of existing work on treatment extraction from the biomedical literature has focused on the extraction of pharmacological interventions. However, non-pharmacological interventions (e.g., exercise, diet, etc.) that are frequently used to address chronic conditions are less well studied. The goal of this study is to compare knowledge-ba...

Metal organic framework based carbon porous as an efficient dispersive solid phase extraction adsorbent for analysis of methamphetamine from urine matrix.

Carboxylated carbon porous adsorbent was derived from zeolite imidazole framework (ZIF-8) via carbonization of ZIF-8 under a nitrogen atmosphere. The synthesized carboxylated adsorbent was fully characterized by various techniques including Fourier transform spectroscopy (FTIR), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), and zeta potential analysis. The carboxylated adsorbent was applied as dispersive solid phase extraction (DSPE) adsorbent for efficient extraction of methamphetamin...

A new strategy for extraction and depuration of pantoprazole in rat plasma: Vortex assisted dispersive micro-solid-phase extraction employing metal organic framework MIL-101(Cr) as sorbent followed by dispersive liquid-liquid microextraction based on solidification of a floating organic droplet.

Dispersive micro-solid-phase extraction (DMSPE) combined with dispersive liquid-liquid microextraction based on the solidification of a floating organic droplet (DLLME-SFO) was successfully developed for extraction and depuration of pantoprazole in rat plasma. The remarkable metal organic framework (MOF), MIL-101(Cr) was used as DMSPE adsorbent. The detection of pantoprazole was performed by convenient HPLC-UV. In the extraction of pantoprazole from plasma samples, small molecule compounds, including the ta...

Cardiac Phase Detection in Echocardiograms with Densely Gated Recurrent Neural Networks and Global Extrema Loss.

Accurate detection of end-systolic (ES) and enddiastolic (ED) frames in an echocardiographic cine series can be a difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem, and propose several deep learning-based architectures that minimize a novel global extrema structu...

Long short-term memory - Fully connected (LSTM-FC) neural network for PM concentration prediction.

People have been suffering from air pollution for a decade in China, especially from PM (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-driven model, called as long short-term memory - fully connected (LSTM-FC) neural network, to predict PM contamination of a specific air quality monitoring station over 48 h using historical air quality data, meteorological data, weather forecast data, and th...

Facile synthesis of magnetic zinc metal-organic framework for extraction of N-containing heterocyclic fungicides from lettuce vegetable samples.

We present a simple method for the fabrication of a magnetic amino-functionalized zinc metal-organic framework based on a magnetic graphene oxide composite. The resultant framework exhibited a porous 3D structure, high surface area and good adsorption properties for N-containing heterocyclic fungicides adsorption. The adsorption process and capacity indicated that the primary adsorption mechanism might be hydrogen bonding and π-π conjugation. In addition, an optimized protocol for magnetic solid phase ext...

Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks.

Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an early age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. In this work, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life (0-3 years) from subjects' longitudinal gut microbiome profiles. Using the DIABIMMUNE dataset, we show an i...

A Disocclusion Inpainting Framework for Depth-based View Synthesis.

This paper proposes a disocclusion inpainting framework for depth-based view synthesis. It consists of four modules: foreground extraction, motion compensation, improved background reconstruction, and inpainting. The foreground extraction module detects the foreground objects and removes them from both depth map and rendered video; the motion compensation module guarantees the background reconstruction model to suit for moving camera scenarios; the improved background reconstruction module constructs a stab...

An ensemble long short-term memory neural network for hourly PM concentration forecasting.

To protect public health by providing an early warning, PM concentration forecasting is an essential and effective work. In this paper, an ensemble long short-term memory neural network (E-LSTM) is proposed for hourly PM concentration forecasting. The presented model is implemented using three steps: (1) ensemble empirical mode decomposition (EEMD) is firstly utilized for multi-modal feature extraction, (2) long short-term memory approach (LSTM) is then employed for multi-modal feature learning, and (3) inv...

A Novel Equivalent Model of Active Distribution Networks Based on LSTM.

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of...

Application of integrative cloud point extraction and concentration for the analysis of polyphenols and alkaloids in mulberry leaves.

A simple and efficient method based on cloud point extraction and concentration combined with high performance liquid chromatography was developed for the simultaneous separation and determination of five target compounds (deoxynojirimycin, chlorogenic acid, rutin, isoquercitrin and astragalin) in mulberry leaves samples. Firstly, to obtain a high extraction rate, the ultrasound assisted extraction was developed on acid modified Triton X-114 system. Under the optimal conditions, the total maximum extraction...

Quantification of co-, n-, and ad-lupulone in hop-based dietary supplements and phytopharmaceuticals and modulation of their contents by the extraction method.

Hop β-bitter acids (lupulones) are health-beneficial components of Humulus lupulus L. showing, for example, antidepressant-like effects in vitro. Despite of the widespread use of hops for medicinal purposes, the concentrations of lupulones in hop-based drugs have not been reported yet. The present study developed, validated, and applied a method with external calibration, which allows for the first time separate quantification of co-, n-, and ad-lupulone in hop-based drugs by UHPLC‒DAD. Concentrations be...

Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry.

This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Metho...

A Structure-Based Human Facial Age Estimation Framework under a Constrained Condition.

Developing an automatic age estimation method towards human faces continues to possess an important role in computer vision and pattern recognition. Many studies regarding facial age estimation mainly focus on two aspects: facial aging feature extraction and classification/regression model learning. To set our work apart from existing age estimation approaches, we consider a different aspect -system structuring, which is, under a constrained condition: given a fixed feature type and a fixed learning method,...

Towards real-time respiratory motion prediction based on long short-term memory neural networks.

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient's respiratory motions and predict the respiratory signals, a generalized model for predictions of different types of patients' respiratory motions is desired. The aim of this study is to explore the feasibility of developing a Long Short-Term Memory (LSTM)-based generalized model for the respiratory signal prediction. To achieve that, 1703 sets of Real-Time P...

Drug Analogs from Fragment Based Long Short-Term Memory Generative Neural Networks.

Several recent reports have shown that long short-term memory generative neural networks (LSTM) of the type used for grammar learning efficiently learn to write SMILES of drug-like compounds when trained with SMILES from a database of bioactive compounds such as ChEMBL and can later produce focused sets upon transfer learning with compounds of specific bioactivity profiles. Here we trained an LSTM using molecules taken either from ChEMBL, DrugBank, commercially available fragments, or from FDB-17 (a databas...

RIscoper: a tool for RNA-RNA interaction extraction from the literature.

Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA-RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available.

An Intelligent Recurrent Neural Network with Long Short-Term Memory (LSTM) BASED Batch Normalization for Medical Image Denoising.

The process of denoising of medical images that are corrupted by noise is considered as a long established setback in the signal or image processing domain. An effective system for denoising in order to remove white, salt and also pepper noises by means of merging the Long Short-Term Memory, otherwise known as LSTM, based Batch Normalization and Recurrent Neural Network or RNN techniques have been proposed in this research paper. The images of the lung CT are considered as an input in this particular work. ...

Aqueous two phase based selective extraction of mannose/glucose specific lectin from Indian cultivar of Pisum sativum seed.

Pisum sativum lectin (Psl) being a high-value protein has marked its application in the biomedical and therapeutic field. Aqueous two phase extraction (ATPE) was implemented as a selective partitioning technique for the partial purification of Psl from its seeds. PEG/citrate based biodegradable aqueous two phase system (ATPS) was screened and the factors such as the type and concentration of citrate salts, molar mass and concentration of polyethylene glycol (PEG), tie line length (TLL) and additive (NaCl) c...

Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. We propose a model, called the feature fusion long short-term memory-convolutional neural network (LSTM-CNN) model, that combines features learned from different representations of the same data, namely, stock time series and stock chart images, to predict stock prices. The proposed model is composed of LSTM and a CNN, which are utilized for extracting temporal ...

Natural Language Statistical Features of LSTM-Generated Texts.

Long short-term memory (LSTM) networks have recently shown remarkable performance in several tasks that are dealing with natural language generation, such as image captioning or poetry composition. Yet, only few works have analyzed text generated by LSTMs in order to quantitatively evaluate to which extent such artificial texts resemble those generated by humans. We compared the statistical structure of LSTM-generated language to that of written natural language, and to those produced by Markov models of va...


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