scholarly journals Identifying G-protein Coupled Receptors Using Mixed-Feature Extraction Methods and Machine Learning Methods

IEEE Access ◽  
2020 ◽  
pp. 1-1 ◽  
Author(s):  
Chunyan Ao ◽  
Lin Gao ◽  
Liang Yu
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
G Protein ◽  
Extraction Methods ◽  
G Protein Coupled Receptors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
G Protein Coupled

scholarly journals Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

2019 ◽  
Vol 9 (5) ◽  
pp. 940 ◽  
Author(s):  
Huseyin Polat ◽  
Homay Danaei Mehr
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Ct Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Diagnosis Of Lung Cancer ◽  
3D Cnn

Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.

scholarly journals Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors

2019 ◽  
Vol 117 (11) ◽  
pp. 2228-2239 ◽  
Author(s):  
Sanychen Muk ◽  
Soumadwip Ghosh ◽  
Srisairam Achuthan ◽  
Xiaomin Chen ◽  
XiaoJie Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
G Protein ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled

scholarly journals Modulation Classification of Underwater Communication with Deep Learning Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Wang ◽  
Hao Zhang ◽  
Zhanliang Sang ◽  
Lingwei Xu ◽  
Conghui Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Language Processing ◽  
Communication Process ◽  
Underwater Acoustic Communication ◽  
Learning Method ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Machine Learning Methods

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

scholarly journals Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7620
Author(s):  
Zhenyi Ye ◽  
Yuan Liu ◽  
Qiliang Li
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Recent Progress ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sensor Drift ◽  
Drift Compensation

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

scholarly journals Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

2019 ◽  
Author(s):  
S. Muk ◽  
S. Ghosh ◽  
S. Achuthan ◽  
X. Chen ◽  
X. Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Membrane Proteins ◽  
G Protein ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Complement Factor ◽  
Computational Framework ◽  
Alanine Scanning ◽  
Structure And Dynamics ◽  
G Protein Coupled

AbstractAlthough the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.Statement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

scholarly journals Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets

2022 ◽  
Vol 14 (2) ◽  
pp. 399
Author(s):  
Xueyuan Tang ◽  
Sheng Dong ◽  
Kun Luo ◽  
Jingxue Guo ◽  
Lin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Noise Removal ◽  
Airborne Radar ◽  
Learning Methods ◽  
Ice Flow ◽  
Machine Learning Methods ◽  
Internal Structures

The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.

scholarly journals A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods

2020 ◽  
Vol 21 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Zheng-Xing Guan ◽  
Shi-Hao Li ◽  
Zi-Mei Zhang ◽  
Dan Zhang ◽  
Hui Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Genomics ◽  
Experimental Methods ◽  
Extraction Methods ◽  
Clear Understanding ◽  
Learning Methods ◽  
Accurate Identification ◽  
Rna Molecules ◽  
Machine Learning Methods ◽  
Benchmark Datasets

MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as timeconsuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.

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