0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
BioBERT-based SNP-traits Associations Extraction from Biomedical Literature
Authors :
Mohammad Dehghani
1
Behrouz Bokharaeian
2
Zahra Yazdanparast
3
1- University of Tehran
2-
3- Tarbiat Modarres University
Keywords :
Machine learning،Deep learning،SNP،Phenotype،trait
Abstract :
A vast amount of information available in scientific literature presents a valuable opportunity to develop text-mining techniques for extracting biomedical relationships. One crucial area of interest involves analyzing the connection between singular nucleotide polymorphism (SNP) and traits. In this study, we introduce BioBERT-GRU to identify the associations between SNP and traits. Through evaluating our approach using the SNPPhenA dataset, we have determined that this novel method outperforms previous machine learning and deep learning techniques that have been used for a similar aim. BioBERT-GRU achieved a significant result, demonstrating a precision, recall, and F1-score equal to 88.3%, 88.2%, 88.1%, respectively.
Papers List
List of archived papers
Hybrid Vision Transformer for Detection of Dentigerous Cysts in Dental Radiography Images
Reza Tavasoli - Arya VarastehNezhad - Hamed Farbeh
Adaptive Sliding Window Optimization for Multi-Dimensional Data Streams Using Reinforcement Learning
Abolfazl Zarghani
Virtual Network Embedding based on Univariate Distribution Estimation
Arezoo Jahani
Analysis of Address Lifespans in Bitcoin and Ethereum
Amir Mohammad Karimi Mamaghan - Amin Setayesh - Behnam Bahrak
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution
Alireza Aghelan - Modjtaba Rouhani
A New Time Series Approach in Churn Prediction with Discriminatory Intervals
Hedieh Ahmadi - Seyed Mohammad Hossein Hasheminejad
Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction
Rojan Roshankar - Mohammad Reza Keyvanpour
Adaptive Ensemble Learning for Software Defect Prediction: A Dynamic Weighted Hybrid Model Using SVM, DT, and ANFIS-PSO
Mohsen EsfandyariDoulabi - Amin Esfandiyari Doulabi - Javad Khaligh
Introducing Meta-Contrastive Adaptive Autoencoder to Tackle Cold-Start Challenges in Sparse Domains
Hossein Rashid - Erfan Arzhmand - Fatemeh Hosseini
Evaluation of Efficient Electrocardiomatrix-based Identification Using Deep Learning Methods
Amirhossein Safari - Narges Mokhtari - Mohsen Hooshmand - Sadegh Sadeghi - Peyman Pahlevani
more
Samin Hamayesh - Version 43.7.0