1
|
Jia J, Wu G, Li M. iGly-IDN: Identifying Lysine Glycation Sites in Proteins Based on Improved DenseNet. J Comput Biol 2024; 31:161-174. [PMID: 38016151 DOI: 10.1089/cmb.2023.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023] Open
Abstract
Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet). First, one hot encoding was adopted to obtain the original feature maps. Afterward, the improved DenseNet was adopted to capture feature information with the importance degrees during the feature learning. According to the experimental results, Acc reaches 66%, and Mathews correlation coefficient reaches 0.33 on the independent testing data set, which indicates that the iGly-IDN can provide more effective glycation site identification than the current predictors.
Collapse
Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Genqiang Wu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- College of Modern Economics and Management, Jiangxi University of Finance and Economics, Nanchang, China
| | - Meifang Li
- School of Computer Information Engineering, Nanchang Institute of Technology, Nanchang, China
| |
Collapse
|
2
|
Ou YY, Ho QT, Chang HT. Recent advances in features generation for membrane protein sequences: From multiple sequence alignment to pre-trained language models. Proteomics 2023; 23:e2200494. [PMID: 37863817 DOI: 10.1002/pmic.202200494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/22/2023]
Abstract
Membrane proteins play a crucial role in various cellular processes and are essential components of cell membranes. Computational methods have emerged as a powerful tool for studying membrane proteins due to their complex structures and properties that make them difficult to analyze experimentally. Traditional features for protein sequence analysis based on amino acid types, composition, and pair composition have limitations in capturing higher-order sequence patterns. Recently, multiple sequence alignment (MSA) and pre-trained language models (PLMs) have been used to generate features from protein sequences. However, the significant computational resources required for MSA-based features generation can be a major bottleneck for many applications. Several methods and tools have been developed to accelerate the generation of MSAs and reduce their computational cost, including heuristics and approximate algorithms. Additionally, the use of PLMs such as BERT has shown great potential in generating informative embeddings for protein sequence analysis. In this review, we provide an overview of traditional and more recent methods for generating features from protein sequences, with a particular focus on MSAs and PLMs. We highlight the advantages and limitations of these approaches and discuss the methods and tools developed to address the computational challenges associated with features generation. Overall, the advancements in computational methods and tools provide a promising avenue for gaining deeper insights into the function and properties of membrane proteins, which can have significant implications in drug discovery and personalized medicine.
Collapse
Affiliation(s)
- Yu-Yen Ou
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
- Graduate Program in Biomedical Informatics, Yuan Ze University, Chung-Li, Taiwan
| | - Quang-Thai Ho
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
| | - Heng-Ta Chang
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan
| |
Collapse
|
3
|
Le NQK, Li W, Cao Y. Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. Brief Bioinform 2023; 24:bbad319. [PMID: 37649385 DOI: 10.1093/bib/bbad319] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/09/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially determined and screened by modeling. As a result, this study created a new pipeline aimed at using protein sequence to predict protein crystallization propensity in the protein material production stage, purification stage and production of crystal stage. The newly created pipeline proposed a new feature selection method, which involves combining Chi-square (${\chi }^{2}$) and recursive feature elimination together with the 12 selected features, followed by a linear discriminant analysisfor dimensionality reduction and finally, a support vector machine algorithm with hyperparameter tuning and 10-fold cross-validation is used to train the model and test the results. This new pipeline has been tested on three different datasets, and the accuracy rates are higher than the existing pipelines. In conclusion, our model provides a new solution to predict multistage protein crystallization propensity which is a big challenge in computational biology.
Collapse
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wuxing Street, 110, Taipei, Taiwan
| | - Wanru Li
- NUS-ISS, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Yanshuang Cao
- NUS-ISS, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| |
Collapse
|
4
|
Liu J, Zhang T, Kang Y, Wang Y, Zhang Y, Hu D, Chen Y. Deep residual constrained reconstruction via learned convolutional sparse coding for low-dose CT imaging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
|
5
|
Soylu NN, Sefer E. BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2177-2189. [PMID: 37819796 DOI: 10.1109/tcbb.2023.3237769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.
Collapse
|
6
|
Sharma A, Lysenko A, Boroevich KA, Tsunoda T. DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics. Sci Rep 2023; 13:2483. [PMID: 36774402 PMCID: PMC9922304 DOI: 10.1038/s41598-023-29644-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/08/2023] [Indexed: 02/13/2023] Open
Abstract
Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.
Collapse
Affiliation(s)
- Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
| | - Artem Lysenko
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
| | - Keith A Boroevich
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
| |
Collapse
|
7
|
OAU-net: Outlined Attention U-net for biomedical image segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
8
|
FEDA: Fine-grained emotion difference analysis for facial expression recognition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
9
|
Zhao Z, Gui J, Yao A, Le NQK, Chua MCH. Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units. ACS OMEGA 2022; 7:40569-40577. [PMID: 36385847 PMCID: PMC9647964 DOI: 10.1021/acsomega.2c05881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
In recent times, the importance of peptides in the biomedical domain has received increasing concern in terms of their effect on multiple disease treatments. However, before successful large-scale implementation in the industry, accurate identification of peptide toxicity is a vital prerequisite. The existing computational methods have reached good results from toxicity prediction, and we present an improved model based on different deep learning architectures. The modification mainly focuses on two aspects: sequence encoding and variational information bottlenecks. Consequently, one of our modified plans shows an obvious increase in sensitivity, while the rest show good performance meanwhile adding novelty in the peptide toxicity prediction domain. In detail, our best model could achieve an accuracy of 97.38 and 95.03% in protein and peptide toxicity predictions, respectively. The performance was superior to previous predictors on the same datasets.
Collapse
Affiliation(s)
- Zhengyun Zhao
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| | - Jingyu Gui
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| | - Anqi Yao
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence
in Medicine, College of Medicine, Taipei
Medical University, Taipei 106, Taiwan
- Research
Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Matthew Chin Heng Chua
- Institute of Systems
Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore
| |
Collapse
|
10
|
Adaptive Correlation Integration for Deep Image Clustering. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
11
|
Zheng Q, Zhang X, Zhang J, Bai F, Huang S, Pu J, Chen W, Wang L. A texture-aware U-Net for identifying incomplete blinking from eye videography. Biomed Signal Process Control 2022; 75. [DOI: 10.1016/j.bspc.2022.103630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
12
|
svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7196040. [PMID: 35345526 PMCID: PMC8957449 DOI: 10.1155/2022/7196040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/04/2022] [Accepted: 01/27/2022] [Indexed: 12/01/2022]
Abstract
Structural variation (SV) is an important type of genome variation and confers susceptibility to human cancer diseases. Systematic analysis of SVs has become a crucial step for the exploration of mechanisms and precision diagnosis of cancers. The central point is how to accurately detect SV breakpoints by using next-generation sequencing (NGS) data. Due to the cooccurrence of multiple types of SVs in the human genome and the intrinsic complexity of SVs, the discrimination of SV breakpoint types is a challenging task. In this paper, we propose a convolutional neural network- (CNN-) based approach, called svBreak, for the detection and discrimination of common types of SV breakpoints. The principle of svBreak is that it extracts a set of SV-related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints. The performance of the proposed approach is tested via simulation studies and application to a real sequencing sample. The experimental results demonstrate the merits of the proposed approach when compared with existing methods. Thus, svBreak can be expected to be a supplementary approach in the field of SV analysis in human tumor genomes.
Collapse
|
13
|
Uma K, Sathya Bama B, Sabarinathan D, Md. Mansoor Roomi S. Identification and retrieval of medicinal plants from Southern India using EfficientB4Net. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-211426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Plant species identification is essential for healthy survival as well as the preservation and protection of biodiversity. Manual identification is time-consuming, hence to address this issue deep learning algorithms for automated plant species identification have been developed. A Novel Architecture comprising of EfficientB4Net, Convolutional Block Attention Module (CBAM) and Residual Block Decoder is proposed to act as Autoencoder for identification and retrieval of twenty distinct groups of medicinal plants, widely available in southern India. The EfficientB4 encoder compresses and encodes the input features along with channel and spatial features to the Residual Block Decoder for efficient learning. Residual Block Decoders work to reconstruct the data from the encoded form to be as close to the original input as possible, by eliminating noise. The information-rich encoded features and the global features from the CBAM are transferred to the fully connected layer and stored in the database for retrieval of the plants. When a query image is received, the encoded feature of the query image and the database images are compared using similarity measurement, and the related images are retrieved. From the retrieved images, the query image is identified and the experimental results clearly show that the proposed method has achieved 95% accuracy when compared with other methods.
Collapse
Affiliation(s)
- K. Uma
- Thiagarajar College of Engineering, Thiruparankundram, Madurai, India
| | - B. Sathya Bama
- Thiagarajar College of Engineering, Thiruparankundram, Madurai, India
| | - D. Sabarinathan
- Thiagarajar College of Engineering, Thiruparankundram, Madurai, India
| | | |
Collapse
|
14
|
Ho QT, Le NQK, Ou YY. mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences. Brief Bioinform 2022; 23:6361041. [PMID: 34472594 DOI: 10.1093/bib/bbab352] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
In the past decade, convolutional neural networks (CNNs) have been used as powerful tools by scientists to solve visual data tasks. However, many efforts of convolutional neural networks in solving protein function prediction and extracting useful information from protein sequences have certain limitations. In this research, we propose a new method to improve the weaknesses of the previous method. mCNN-ETC is a deep learning model which can transform the protein evolutionary information into image-like data composed of 20 channels, which correspond to the 20 amino acids in the protein sequence. We constructed CNN layers with different scanning windows in parallel to enhance the useful pattern detection ability of the proposed model. Then we filtered specific patterns through the 1-max pooling layer before inputting them into the prediction layer. This research attempts to solve a basic problem in biology in terms of application: predicting electron transporters and classifying their corresponding complexes. The performance result reached an accuracy of 97.41%, which was nearly 6% higher than its predecessor. We have also published a web server on http://bio219.bioinfo.yzu.edu.tw, which can be used for research purposes free of charge.
Collapse
Affiliation(s)
- Quang-Thai Ho
- Computer Science and Engineering Departments at the Yuan Ze University, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Taiwan
| | - Yu-Yen Ou
- Department of Computer Science and Engineering, Yuan-Ze University, Taiwan
| |
Collapse
|
15
|
Sharifrazi D, Alizadehsani R, Joloudari JH, Band SS, Hussain S, Sani ZA, Hasanzadeh F, Shoeibi A, Dehzangi A, Sookhak M, Alinejad-Rokny H. CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2381-2402. [PMID: 35240789 DOI: 10.3934/mbe.2022110] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.
Collapse
Affiliation(s)
- Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, IR
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, AU
| | | | - Shahab S Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, TW
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam 786004, IN
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, IR
| | | | - Afshin Shoeibi
- FPGA Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, IR
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Mehdi Sookhak
- Department of Computer Science, Texas A & M University at Corpus Christi, Corpus Christi, TX 78412, USA
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, AU
- Health Data Analytics Program, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, AU
| |
Collapse
|
16
|
Amilpur S, Bhukya R. Predicting novel drug candidates against Covid-19 using generative deep neural networks. J Mol Graph Model 2022; 110:108045. [PMID: 34688160 PMCID: PMC8510991 DOI: 10.1016/j.jmgm.2021.108045] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/17/2021] [Accepted: 10/04/2021] [Indexed: 12/21/2022]
Abstract
The novel Coronavirus outbreak has created a massive economic crisis, and many succumb to death, disturbing the lives of mankind all over the world. Currently, there are no viable treatment for this condition, drug development approaches are being pursued with vigor. The major treatment options are to repurpose existing drugs or to find new ones. Traditional methods for drug discovery take a longer time, so there is an urgent need to develop some alternative techniques that reduces search space for drug candidates. Towards this endeavor, we propose a novel drug discovery method that leverages on long short term memory (LSTM) model to generate novel molecules that are adept at binding with novel Coronavirus protease. Our study demonstrates that the proposed method is able to recreate novel molecules that correlate very much with the properties of trained molecules. Further, we fine-tune the model to generate novel drug-like molecules that are active towards a specific target. We consider 3CLPro, the main protease of novel Coronavirus, as a therapeutic target and demonstrated in silico screening to assess target structural binding affinities with docking simulations. We observed that 80% of generated molecules show docking free energy of less than -5.8 kcal/mol. The top generated drug candidate has the highest binding affinity with a docking score of -8.5 kcal/mol, which is very much lower when compared to approved existing commercial drugs including, Remdesivir. The low binding energy indicates that the generated molecules could be explored as potential drug candidates for Covid-19.
Collapse
Affiliation(s)
| | - Raju Bhukya
- National Institute of Technology, Waranagl, 506004, India
| |
Collapse
|
17
|
Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer. Diagnostics (Basel) 2021; 12:diagnostics12010072. [PMID: 35054239 PMCID: PMC8774306 DOI: 10.3390/diagnostics12010072] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/28/2021] [Indexed: 12/26/2022] Open
Abstract
The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection.
Collapse
|
18
|
Le NQK, Ho QT. Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes. Methods 2021; 204:199-206. [PMID: 34915158 DOI: 10.1016/j.ymeth.2021.12.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 11/30/2021] [Accepted: 12/09/2021] [Indexed: 12/19/2022] Open
Abstract
As one of the most common post-transcriptional epigenetic modifications, N6-methyladenine (6 mA), plays an essential role in various cellular processes and disease pathogenesis. Therefore, accurately identifying 6 mA modifications is necessary for a deep understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models were developed with small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we present a novel model based on transformer architecture and deep learning to identify DNA 6 mA sites from the cross-species genome. The model is constructed on a benchmark dataset and explored a feature derived from pre-trained transformer word embedding approaches. Subsequently, a convolutional neural network was employed to learn the generated features and generate the prediction outcomes. As a result, our predictor achieved excellent performance during independent test with the accuracy and Matthews correlation coefficient (MCC) of 79.3% and 0.58, respectively. Overall, its performance achieved better accuracy than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, our model is expected to assist biologists in accurately identifying 6mAs and formulate the novel testable biological hypothesis. We also release source codes and datasets freely at https://github.com/khanhlee/bert-dna for front-end users.
Collapse
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan.
| | - Quang-Thai Ho
- College of Information & Communication Technology, Can Tho University, Viet Nam; Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan
| |
Collapse
|
19
|
Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.12.035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
20
|
Campbell TW, Wilson MP, Roder H, MaWhinney S, Georgantas RW, Maguire LK, Roder J, Erlandson KM. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. Int J Med Inform 2021; 155:104594. [PMID: 34601240 PMCID: PMC8459591 DOI: 10.1016/j.ijmedinf.2021.104594] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 08/30/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Abstract
Rationale Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. The study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. Methods Hierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of acute respiratory distress syndrome, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk. Main results Test performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, C-reactive protein, lactate dehydrogenase, and D-dimer were often found to be important in the assignment of risk. Conclusions Risk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.
Collapse
Affiliation(s)
| | - Melissa P Wilson
- Department of Medicine, Division of Personalized Medicine and Bioinformatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States
| | | | - Samantha MaWhinney
- Department of Biostatistics and Informatics, University of Colorado, Colorado School of Public Health, United States
| | | | | | | | - Kristine M Erlandson
- Department of Medicine, Division of Infectious Diseases, University of Colorado-Anschutz Medical Campus, Aurora, CO, United States
| |
Collapse
|
21
|
Zhang Y, Li M, Ji Z, Fan W, Yuan S, Liu Q, Chen Q. Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
22
|
Ahmed H, Alarabi L, El-Sappagh S, Soliman H, Elmogy M. Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles. PeerJ Comput Sci 2021; 7:e697. [PMID: 34616886 PMCID: PMC8459785 DOI: 10.7717/peerj-cs.697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. METHODS We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. RESULTS It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. CONCLUSIONS Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.
Collapse
Affiliation(s)
- Hala Ahmed
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Louai Alarabi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Hassan Soliman
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| |
Collapse
|
23
|
Cao R, Wang M, Bin Y, Zheng C. DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion. PeerJ 2021; 9:e11906. [PMID: 34414035 PMCID: PMC8344685 DOI: 10.7717/peerj.11906] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 07/14/2021] [Indexed: 01/10/2023] Open
Abstract
An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model's predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model's area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.
Collapse
Affiliation(s)
- Ruifen Cao
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
| | - Meng Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
| |
Collapse
|
24
|
Reshi AA, Ashraf I, Rustam F, Shahzad HF, Mehmood A, Choi GS. Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms. PeerJ Comput Sci 2021; 7:e547. [PMID: 34395856 PMCID: PMC8323723 DOI: 10.7717/peerj-cs.547] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/25/2021] [Indexed: 06/13/2023]
Abstract
Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F 1 score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique.
Collapse
Affiliation(s)
- Aijaz Ahmad Reshi
- College of Computer Science and Engineering, Department of Computer Science, Taibah University, Al Madinah Al Munawarah, Saudi Arabia
| | - Imran Ashraf
- Information and Communication Engineering, Yeungnam University, Gyeongbuk, Gyeongsan-si, South Korea
| | - Furqan Rustam
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Hina Fatima Shahzad
- Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Arif Mehmood
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Gyu Sang Choi
- Information and Communication Engineering, Yeungnam University, Gyeongbuk, Gyeongsan-si, South Korea
| |
Collapse
|
25
|
Shang C, Liu Q, Tong Q, Sun J, Song M, Bi J. Multi-view spectral graph convolution with consistent edge attention for molecular modeling. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.02.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
26
|
Su R, Zhang D, Liu J, Cheng C. MSU-Net: Multi-Scale U-Net for 2D Medical Image Segmentation. Front Genet 2021; 12:639930. [PMID: 33679900 PMCID: PMC7928319 DOI: 10.3389/fgene.2021.639930] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 01/20/2021] [Indexed: 11/15/2022] Open
Abstract
Aiming at the limitation of the convolution kernel with a fixed receptive field and unknown prior to optimal network width in U-Net, multi-scale U-Net (MSU-Net) is proposed by us for medical image segmentation. First, multiple convolution sequence is used to extract more semantic features from the images. Second, the convolution kernel with different receptive fields is used to make features more diverse. The problem of unknown network width is alleviated by efficient integration of convolution kernel with different receptive fields. In addition, the multi-scale block is extended to other variants of the original U-Net to verify its universality. Five different medical image segmentation datasets are used to evaluate MSU-Net. A variety of imaging modalities are included in these datasets, such as electron microscopy, dermoscope, ultrasound, etc. Intersection over Union (IoU) of MSU-Net on each dataset are 0.771, 0.867, 0.708, 0.900, and 0.702, respectively. Experimental results show that MSU-Net achieves the best performance on different datasets. Our implementation is available at https://github.com/CN-zdy/MSU_Net.
Collapse
Affiliation(s)
- Run Su
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
| | - Deyun Zhang
- School of Engineering, Anhui Agricultural University, Hefei, China
| | - Jinhuai Liu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
| | - Chuandong Cheng
- Department of Neurosurgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), Hefei, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Anhui Province Key Laboratory of Brain Function and Brain Disease, Hefei, China
| |
Collapse
|
27
|
Ho QT, Nguyen TTD, Khanh Le NQ, Ou YY. FAD-BERT: Improved prediction of FAD binding sites using pre-training of deep bidirectional transformers. Comput Biol Med 2021; 131:104258. [PMID: 33601085 DOI: 10.1016/j.compbiomed.2021.104258] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/16/2021] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
The electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. Identifying Flavin Adenine Dinucleotide (FAD) binding sites in the electron transport chain is vital since it helps biological researchers precisely understand how electrons are produced and are transported in cells. This study distills and analyzes the contextualized word embedding from pre-trained BERT models to explore similarities in natural language and protein sequences. Thereby, we propose a new approach based on Pre-training of Bidirectional Encoder Representations from Transformers (BERT), Position-specific Scoring Matrix profiles (PSSM), Amino Acid Index database (AAIndex) to predict FAD-binding sites from the transport proteins which are found in nature recently. Our proposed approach archives 85.14% accuracy and improves accuracy by 11%, with Matthew's correlation coefficient of 0.39 compared to the previous method on the same independent set. We also deploy a web server that identifies FAD-binding sites in electron transporters available for academics at http://140.138.155.216/fadbert/.
Collapse
Affiliation(s)
- Quang-Thai Ho
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan; College of Information & Communication Technology, Can Tho University, Viet Nam
| | | | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei City, 106, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City, 106, Taiwan
| | - Yu-Yen Ou
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, 32003, Taiwan.
| |
Collapse
|
28
|
Le NQK, Ho QT, Nguyen TTD, Ou YY. A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information. Brief Bioinform 2021; 22:6128847. [PMID: 33539511 DOI: 10.1093/bib/bbab005] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/01/2021] [Accepted: 01/03/2021] [Indexed: 01/11/2023] Open
Abstract
Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among them, bidirectional encoder representations from transformers (BERT) has proven to be a simple, yet powerful language model that achieved novel state-of-the-art performance. BERT adopted the concept of contextualized word embedding to capture the semantics and context of the words in which they appeared. In this study, we present a novel technique by incorporating BERT-based multilingual model in bioinformatics to represent the information of DNA sequences. We treated DNA sequences as natural sentences and then used BERT models to transform them into fixed-length numerical matrices. As a case study, we applied our method to DNA enhancer prediction, which is a well-known and challenging problem in this field. We then observed that our BERT-based features improved more than 5-10% in terms of sensitivity, specificity, accuracy and Matthews correlation coefficient compared to the current state-of-the-art features in bioinformatics. Moreover, advanced experiments show that deep learning (as represented by 2D convolutional neural networks; CNN) holds potential in learning BERT features better than other traditional machine learning techniques. In conclusion, we suggest that BERT and 2D CNNs could open a new avenue in biological modeling using sequence information.
Collapse
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Quang-Thai Ho
- College of Information and Communication Technology, Can Tho University, Vietnam
| | | | - Yu-Yen Ou
- Department of Computer Science and Engineering, Yuan Ze University, Taiwan
| |
Collapse
|
29
|
Nguyen T, Le N, Ho Q, Phan D, Ou Y. Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain. Mol Inform 2020; 39:e2000033. [DOI: 10.1002/minf.202000033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/26/2020] [Indexed: 11/10/2022]
Affiliation(s)
| | - Nguyen‐Quoc‐Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine College of Medicine, Taipei Medical University Taipei City 106 Taiwan
- Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan
| | - Quang‐Thai Ho
- Department of Computer Science and Engineering Yuan Ze University Chung-Li Taiwan 32003
| | - Dinh‐Van Phan
- University of Economics University of Danang 41 Leduan St Danang City 550000 Vietnam
| | - Yu‐Yen Ou
- Department of Computer Science and Engineering Yuan Ze University Chung-Li Taiwan 32003
| |
Collapse
|
30
|
Wang X, Liu Y, Lu F, Li H, Gao P, Wei D. Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction. Front Bioeng Biotechnol 2020; 8:267. [PMID: 32318557 PMCID: PMC7147459 DOI: 10.3389/fbioe.2020.00267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 03/13/2020] [Indexed: 11/13/2022] Open
Abstract
Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.
Collapse
Affiliation(s)
- Xianfang Wang
- School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, China.,School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Yifeng Liu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Fan Lu
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Hongfei Li
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Dongqing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
31
|
Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072253] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Facial landmarks represent prominent feature points on the face that can be used as anchor points in many face-related tasks. So far, a lot of research has been done with the aim of achieving efficient extraction of landmarks from facial images. Employing a large number of feature points for landmark detection and tracking usually requires excessive processing time. On the contrary, relying on too few feature points cannot accurately represent diverse landmark properties, such as shape. To extract the 68 most popular facial landmark points efficiently, in our previous study, we proposed a model called EMTCNN that extended the multi-task cascaded convolutional neural network for real-time face landmark detection. To improve the detection accuracy, in this study, we augment the EMTCNN model by using two convolution techniques—dilated convolution and CoordConv. The former makes it possible to increase the filter size without a significant increase in computation time. The latter enables the spatial coordinate information of landmarks to be reflected in the model. We demonstrate that our model can improve the detection accuracy while maintaining the processing speed.
Collapse
|