scholarly journals Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach

2021 ◽  
Author(s):  
Saranya M ◽  
Arockia Xavier Annie R ◽  
Geetha T V
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Drug Repositioning ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Learning Approach ◽  
Computational Approaches ◽  
The World ◽  
The Cost ◽  
Very High

Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug discovery as it needs more man power. Due to the limited computational approaches, the relation extraction between drug-gene and genedisease association from the unstructured biomedical documents is very hard. In this work, we proposed a semi-supervised approach named pattern based bootstrapping method to extract the direct relations between drug, gene and disease from the biomedical literature. These direct relationships are used to infer indirect relationships between entities such as drug and disease. Now these indirect relationships are used to determine the new candidates for drug repositioning which in turn will reduce the time and the patient’s risk.

scholarly journals A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Tao Chen ◽  
Mingfen Wu ◽  
Hexi Li
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Relation Extraction ◽  
Training Model ◽  
Biomedical Literature ◽  
Training Data ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Additional Time ◽  
Clinical Records

Abstract The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.

scholarly journals SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cheng Yan ◽  
Jianxin Wang ◽  
Wei Lan ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Computational Methods ◽  
Drug Target ◽  
Large Scale ◽  
High Efficiency ◽  
Drug Repositioning ◽  
External Validation ◽  
Complex Diseases ◽  
Biological Databases ◽  
Biomolecular Networks ◽  
New Chemical Entities

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

scholarly journals A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

2017 ◽  
Author(s):  
Yunan Luo ◽  
Xinbin Zhao ◽  
Jingtian Zhou ◽  
Jinglin Yang ◽  
Yanqing Zhang ◽  
...  
Keyword(s):  
Heterogeneous Network ◽  
Drug Target ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Inflammatory Diseases ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
Heterogeneous Information ◽  
Cox Inhibitors ◽  
New Drug

AbstractThe emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. Systematic integration of these heterogeneous data not only serves as a promising tool for identifying new drug-target interactions (DTIs), which is an important step in drug development, but also provides a more complete understanding of the molecular mechanisms of drug action. In this work, we integrate diverse drug-related information, including drugs, proteins, diseases and side-effects, together with their interactions, associations or similarities, to construct a heterogeneous network with 12,015 nodes and 1,895,445 edges. We then develop a new computational pipeline, called DTINet, to predict novel drug-target interactions from the constructed heterogeneous network. Specifically, DTINet focuses on learning a low-dimensional vector representation of features for each node, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for DTI prediction. Moreover, we have experimentally validated the novel interactions between three drugs and the cyclooxygenase (COX) protein family predicted by DTINet, and demonstrated the new potential applications of these identified COX inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs. The source code of DTINet and the input heterogeneous network data can be downloaded from http://github.com/luoyunan/DTINet.

scholarly journals Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 377 ◽  
Author(s):  
Hanbi Lee ◽  
Wankyu Kim
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Large Scale ◽  
Deep Neural Network ◽  
Structural Information ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Ppi Network ◽  
Drug Induced ◽  
Integrated Network

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

Failure Mode Based Design and Optimization of the Electrode Packaging Process for Large Scale Battery Cells

2014 ◽  
Vol 907 ◽  
pp. 309-319
Author(s):  
Jan Schmitt ◽  
Annika Raatz
Keyword(s):  
Failure Mode ◽  
Large Scale ◽  
Lithium Ion ◽  
Packaging Process ◽  
Design And Optimization ◽  
Safety Requirements ◽  
Production Processes ◽  
Increasing Demand ◽  
The Cost ◽  
Very High

The increasing demand of electric vehicles and thus Lithium-Ion batteries results in a multitude of challenges in production technology. The cost-effectiveness, reproducibility, performance and safety requirements of large scale batteries for automotive applications are very high. At the same time the production processes are complex and have many uncertainties, namely how single parameters influence the specific values of the battery performance. Therefore, this article focuses on the design and optimization of production processes of large scale batteries using an established FMEA approach. This method is applied to the electrode packaging process, which constitutes a crucial production step, as the anode and cathode material is assembled to create a multi-layer cell. Based on a failure mode ranking, two categories of essential failure are considered in detail. First the positioning error of the electrode foils and following this the multi-layer handling during the process. Here, an algorithm to simulate the stacking error is presented and a sensor concept to detect multi gripped layers during the handling by a gripper integrated eddy current sensor is introduced.

HR Issues Towards Managing MSMEs Operating in India During Globalization Trends (with Special Reference to HR Outsourcing)

2014 ◽  
Vol 5 (3) ◽  
pp. 1-16
Author(s):  
Tweena Pandey ◽  
Abhishek Singh Chauhan
Keyword(s):  
Large Scale ◽  
Small And Medium Enterprises ◽  
Small Enterprises ◽  
The Past ◽  
The World ◽  
Medium Enterprises ◽  
The Cost ◽  
Medium And Small Enterprises ◽  
Processing Period ◽  
Day By Day

If you have never entered the world of subcontracting it seems a puzzling blend of products, rigid selling and in the past has carried a bad reputation. Today, when grooming with different parameters of economies is becoming harder for every industrialist day by day, it is for sure that this disaster of cost- knitting is affecting one of the upcoming sectors of present commercial age and that is SMEs. If one talks about Medium and Small Enterprises, one can simply say that they also act as feeder to the large scale enterprises, which are into support production activities for the big enterprises. As it is known that they have a small gestation period (processing period), i.e. stalk in- product out, which further acts fast profit generation to the domestic scale of production as compared to large enterprises. But still if one focuses upon its competency mainly in comparison to the globalized standards, nevertheless one stands far behind, irrespective of availability of sufficient resources. Here the big question arises that in what terms and why? This review is an attempt to navigate some upcoming disasters in concern with managing the workforce employed in these small and medium enterprises, especially with respect to manage the cost incurred over the human capital- “A key capital for strengthening the enterprise's credentials”.

scholarly journals Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 1173
Author(s):  
Hafiz Farooq Ahmad ◽  
Hamid Mukhtar ◽  
Hesham Alaqail ◽  
Mohamed Seliaman ◽  
Abdulaziz Alhumam
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Large Scale ◽  
Cost Effective ◽  
World Health ◽  
Evaluation Metrics ◽  
Diabetic Patients ◽  
The World ◽  
The Government ◽  
The Cost

Diabetes Mellitus (DM) is one of the most common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of its medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient with only a handful of features can allow cost-effective, rapid, and widely-available screening of diabetes, thereby lessening the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic patients and compare the role of HbA1c and FPG as input features. By using five different machine learning classifiers, and using feature elimination through feature permutation and hierarchical clustering, we established good performance for accuracy, precision, recall, and F1-score of the models on the dataset implying that our data or features are not bound to specific models. In addition, the consistent performance across all the evaluation metrics indicate that there was no trade-off or penalty among the evaluation metrics. Further analysis was performed on the data to identify the risk factors and their indirect impact on diabetes classification. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing analysis of the disease using selected features, important factors specific to the Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learned from this research.

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