1
|
Wang F, Jia K, Li Y. Integrative deep learning with prior assisted feature selection. Stat Med 2024; 43:3792-3814. [PMID: 38923006 DOI: 10.1002/sim.10148] [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: 11/23/2023] [Revised: 04/23/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024]
Abstract
Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the "smalln $$ n $$ and largep $$ p $$ " challenge. Leveraging the powerful capabilities of deep learning in extracting complex relationship between genes and diseases, our objective in this study is to incorporate deep learning into the framework of integrative analysis. Recognizing the redundancy within candidate features, we introduce a dedicated feature selection layer in the proposed integrative deep learning method. To further improve the performance of feature selection, the rich previous researches are utilized by an ensemble learning method to identify "prior information". This leads to the proposed prior assisted integrative deep learning (PANDA) method. We demonstrate the superiority of the PANDA method through a series of simulation studies, showing its clear advantages over competing approaches in both feature selection and outcome prediction. Finally, a skin cutaneous melanoma (SKCM) dataset is extensively analyzed by the PANDA method to show its practical application.
Collapse
Affiliation(s)
- Feifei Wang
- Center for Applied Statistics, Renmin University of China, Beijing, China
- School of Statistics, Renmin University of China, Beijing, China
| | - Ke Jia
- School of Statistics, Renmin University of China, Beijing, China
| | - Yang Li
- Center for Applied Statistics, Renmin University of China, Beijing, China
- School of Statistics, Renmin University of China, Beijing, China
| |
Collapse
|
2
|
Tian W, Zang L, Ijaz M, Dong Z, Zhang S, Gao L, Li M, Nie L, Zang H. Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124396. [PMID: 38733911 DOI: 10.1016/j.saa.2024.124396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/24/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.
Collapse
Affiliation(s)
- Weilu Tian
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmaceutical Products, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China
| | - Lixuan Zang
- National Glycoengineering Research Center, Shandong University, Jinan 250012, China
| | - Muhammad Ijaz
- Department of Pharmacology, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China
| | - Zaixing Dong
- School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Shudi Zhang
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmaceutical Products, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China
| | - Lele Gao
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmaceutical Products, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China
| | - Meiqi Li
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmaceutical Products, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China
| | - Lei Nie
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmaceutical Products, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China.
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmaceutical Products, School of Pharmaceutical Sciences, Shandong University, Jinan 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China.
| |
Collapse
|
3
|
Tang W, van Ooijen PMA, Sival DA, Maurits NM. Automatic two-dimensional & three-dimensional video analysis with deep learning for movement disorders: A systematic review. Artif Intell Med 2024; 156:102952. [PMID: 39180925 DOI: 10.1016/j.artmed.2024.102952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 07/19/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024]
Abstract
The advent of computer vision technology and increased usage of video cameras in clinical settings have facilitated advancements in movement disorder analysis. This review investigated these advancements in terms of providing practical, low-cost solutions for the diagnosis and analysis of movement disorders, such as Parkinson's disease, ataxia, dyskinesia, and Tourette syndrome. Traditional diagnostic methods for movement disorders are typically reliant on the subjective assessment of motor symptoms, which poses inherent challenges. Furthermore, early symptoms are often overlooked, and overlapping symptoms across diseases can complicate early diagnosis. Consequently, deep learning has been used for the objective video-based analysis of movement disorders. This study systematically reviewed the latest advancements in automatic two-dimensional & three-dimensional video analysis using deep learning for movement disorders. We comprehensively analyzed the literature published until September 2023 by searching the Web of Science, PubMed, Scopus, and Embase databases. We identified 68 relevant studies and extracted information on their objectives, datasets, modalities, and methodologies. The study aimed to identify, catalogue, and present the most significant advancements, offering a consolidated knowledge base on the role of video analysis and deep learning in movement disorder analysis. First, the objectives, including specific PD symptom quantification, ataxia assessment, cerebral palsy assessment, gait disorder analysis, tremor assessment, tic detection (in the context of Tourette syndrome), dystonia assessment, and abnormal movement recognition were discussed. Thereafter, the datasets used in the study were examined. Subsequently, video modalities and deep learning methodologies related to the topic were investigated. Finally, the challenges and opportunities in terms of datasets, interpretability, evaluation methods, and home/remote monitoring were discussed.
Collapse
Affiliation(s)
- Wei Tang
- Department of Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; Data Science Center in Health, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands.
| | - Peter M A van Ooijen
- Data Science Center in Health, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands
| | - Deborah A Sival
- Department of Pediatric Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands
| | - Natasha M Maurits
- Department of Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands
| |
Collapse
|
4
|
Jiang C, Yang J, Peng X, Li X. A permutable MLP-like architecture for disease prediction from gut metagenomic data. BMC Bioinformatics 2024; 25:246. [PMID: 39048979 PMCID: PMC11270793 DOI: 10.1186/s12859-024-05856-w] [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: 09/14/2023] [Accepted: 07/05/2024] [Indexed: 07/27/2024] Open
Abstract
Metagenomic data plays a crucial role in analyzing the relationship between microbes and diseases. However, the limited number of samples, high dimensionality, and sparsity of metagenomic data pose significant challenges for the application of deep learning in data classification and prediction. Previous studies have shown that utilizing the phylogenetic tree structure to transform metagenomic abundance data into a 2D matrix input for convolutional neural networks (CNNs) improves classification performance. Inspired by the success of a Permutable MLP-like architecture in visual recognition, we propose Metagenomic Permutator (MetaP), which applied the Permutable MLP-like network structure to capture the phylogenetic information of microbes within the 2D matrix formed by phylogenetic tree. Our experiments demonstrate that our model achieved competitive performance compared to other deep neural networks and traditional machine learning, and has good prospects for multi-classification and large sample sizes. Furthermore, we utilize the SHAP (SHapley Additive exPlanations) method to interpret our model predictions, identifying the microbial features that are associated with diseases.
Collapse
Affiliation(s)
- Cong Jiang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
| | - Jian Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiaogang Peng
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China.
| | - Xiaozheng Li
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.
- JCY Biotech Ltd., Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, China.
| |
Collapse
|
5
|
Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [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] [Received: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
Collapse
Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
| |
Collapse
|
6
|
Yi T, Luo J, Liao R, Wang L, Wu A, Li Y, Zhou L, Ni C, Wang K, Tang X, Zou W, Wu J. An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks. Biomolecules 2024; 14:267. [PMID: 38540688 PMCID: PMC10968240 DOI: 10.3390/biom14030267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 06/28/2024] Open
Abstract
(1) Background: Radiation-induced thrombocytopenia (RIT) often occurs in cancer patients undergoing radiation therapy, which can result in morbidity and even death. However, a notable deficiency exists in the availability of specific drugs designed for the treatment of RIT. (2) Methods: In our pursuit of new drugs for RIT treatment, we employed three deep learning (DL) algorithms: convolutional neural network (CNN), deep neural network (DNN), and a hybrid neural network that combines the computational characteristics of the two. These algorithms construct computational models that can screen compounds for drug activity by utilizing the distinct physicochemical properties of the molecules. The best model underwent testing using a set of 10 drugs endorsed by the US Food and Drug Administration (FDA) specifically for the treatment of thrombocytopenia. (3) Results: The Hybrid CNN+DNN (HCD) model demonstrated the most effective predictive performance on the test dataset, achieving an accuracy of 98.3% and a precision of 97.0%. Both metrics surpassed the performance of the other models, and the model predicted that seven FDA drugs would exhibit activity. Isochlorogenic acid A, identified through screening the Chinese Pharmacopoeia Natural Product Library, was subsequently subjected to experimental verification. The results indicated a substantial enhancement in the differentiation and maturation of megakaryocytes (MKs), along with a notable increase in platelet production. (4) Conclusions: This underscores the potential therapeutic efficacy of isochlorogenic acid A in addressing RIT.
Collapse
Affiliation(s)
- Taian Yi
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; (T.Y.); (Y.L.)
| | - Jiesi Luo
- Department of Chemistry, School of Basic Medical Sciences, Southwest Medical University, Luzhou 646000, China;
| | - Ruixue Liao
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Long Wang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Anguo Wu
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Yueyue Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; (T.Y.); (Y.L.)
| | - Ling Zhou
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Chengyang Ni
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Kai Wang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Xiaoqin Tang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
| | - Wenjun Zou
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; (T.Y.); (Y.L.)
| | - Jianming Wu
- Department of Chemistry, School of Basic Medical Sciences, Southwest Medical University, Luzhou 646000, China;
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China (L.W.); (A.W.); (L.Z.); (C.N.); (K.W.); (X.T.)
- The Institute of Cardiovascular Research, Key Laboratory of Medical Electrophysiology of Ministry of Education, Luzhou 646000, China
| |
Collapse
|
7
|
Rahul J, Sharma D, Sharma LD, Nanda U, Sarkar AK. A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front Hum Neurosci 2024; 18:1347082. [PMID: 38419961 PMCID: PMC10899326 DOI: 10.3389/fnhum.2024.1347082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.
Collapse
Affiliation(s)
- Jagdeep Rahul
- Department of Electronics and Communication Engineering, Rajiv Gandhi University, Arunachal Pradesh, India
| | - Diksha Sharma
- Department of Electronics and Communication, Indian Institute of Information Technology, Sri City, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amrawati, India
| | - Umakanta Nanda
- School of Electronics Engineering, VIT-AP University, Amrawati, India
| | - Achintya Kumar Sarkar
- Department of Electronics and Communication, Indian Institute of Information Technology, Sri City, India
| |
Collapse
|
8
|
Alsenan S, Al-Turaiki I, Aldayel M, Tounsi M. Role of Optimization in RNA-Protein-Binding Prediction. Curr Issues Mol Biol 2024; 46:1360-1373. [PMID: 38392205 PMCID: PMC11154364 DOI: 10.3390/cimb46020087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 02/24/2024] Open
Abstract
RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms including deep convolutional neural network models. One of the integral parts of modeling deep learning is achieving optimal hyperparameter tuning and minimizing a loss function using optimization algorithms. In this paper, we investigate the role of optimization in the RBP classification problem using the CLIP-Seq 21 dataset. Three optimization methods are employed on the RNA-protein binding CNN prediction model; namely, grid search, random search, and Bayesian optimizer. The empirical results show an AUC of 94.42%, 93.78%, 93.23% and 92.68% on the ELAVL1C, ELAVL1B, ELAVL1A, and HNRNPC datasets, respectively, and a mean AUC of 85.30 on 24 datasets. This paper's findings provide evidence on the role of optimizers in improving the performance of RNA-protein binding prediction.
Collapse
Affiliation(s)
- Shrooq Alsenan
- Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Isra Al-Turaiki
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11653, Saudi Arabia;
| | - Mashael Aldayel
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Mohamed Tounsi
- Department of Computer Science, College of Computer and information Sciences, Prince Sultan University, P.O. Box 66833, Riyadh 12435, Saudi Arabia;
| |
Collapse
|
9
|
Jan M, Spangaro A, Lenartowicz M, Mattiazzi Usaj M. From pixels to insights: Machine learning and deep learning for bioimage analysis. Bioessays 2024; 46:e2300114. [PMID: 38058114 DOI: 10.1002/bies.202300114] [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: 06/24/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023]
Abstract
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.
Collapse
Affiliation(s)
- Mahta Jan
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Allie Spangaro
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Michelle Lenartowicz
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| | - Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, Canada
| |
Collapse
|
10
|
Meng J, Liu J, Song W, Li H, Wang J, Zhang L, Peng Y, Wu A, Jiang T. PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network. Brief Bioinform 2024; 25:bbae033. [PMID: 38343322 PMCID: PMC10859661 DOI: 10.1093/bib/bbae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 01/13/2024] [Accepted: 01/18/2024] [Indexed: 02/15/2024] Open
Abstract
Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.
Collapse
Affiliation(s)
- Jing Meng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Jingze Liu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Honglei Li
- Beijing Cloudna Technology Company, Limited, Beijing 100029, China
| | | | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yousong Peng
- College of Biology, Hunan University, Changsha 410082, China
| | - Aiping Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Taijiao Jiang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
- Guangzhou National Laboratory, Guangzhou 510005, China
- State Key Laboratory of Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510120, China
| |
Collapse
|
11
|
Danishuddin, Khan S, Kim JJ. From cancer big data to treatment: Artificial intelligence in cancer research. J Gene Med 2024; 26:e3629. [PMID: 37940369 DOI: 10.1002/jgm.3629] [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] [Received: 08/25/2023] [Revised: 09/12/2023] [Accepted: 10/18/2023] [Indexed: 11/10/2023] Open
Abstract
In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.
Collapse
Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
| |
Collapse
|
12
|
Cano J, Bertomeu-González V, Fácila L, Hornero F, Alcaraz R, Rieta JJ. Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration. Bioengineering (Basel) 2023; 10:1439. [PMID: 38136030 PMCID: PMC10741001 DOI: 10.3390/bioengineering10121439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.
Collapse
Affiliation(s)
- Jesús Cano
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| | - Vicente Bertomeu-González
- Cardiovascular Research Group, Clinical Medicine Department, Miguel Hernández University, 03202 Alicante, Spain;
| | - Lorenzo Fácila
- Cardiology Department, General University Hospital Consortium of Valencia, 46014 Valencia, Spain;
| | - Fernando Hornero
- Cardiovascular Surgery Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain;
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain;
| | - José J. Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| |
Collapse
|
13
|
Wu J, Ren P, Song B, Zhang R, Zhao C, Zhang X. Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism. PLoS One 2023; 18:e0294174. [PMID: 37976294 PMCID: PMC10655964 DOI: 10.1371/journal.pone.0294174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.
Collapse
Affiliation(s)
- Jiawei Wu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Peng Ren
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
- Engineering Research Center of Medical and Health Sensing Technology, Xuzhou Medical University, Xuzhou, China
| | - Boming Song
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Ran Zhang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
- Engineering Research Center of Medical and Health Sensing Technology, Xuzhou Medical University, Xuzhou, China
| | - Chen Zhao
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Xiao Zhang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| |
Collapse
|
14
|
Mao J, Cao Y, Zhang Y, Huang B, Zhao Y. A novel method for identifying key genes in macroevolution based on deep learning with attention mechanism. Sci Rep 2023; 13:19727. [PMID: 37957311 PMCID: PMC10643560 DOI: 10.1038/s41598-023-47113-9] [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: 06/27/2023] [Accepted: 11/09/2023] [Indexed: 11/15/2023] Open
Abstract
Macroevolution can be regarded as the result of evolutionary changes of synergistically acting genes. Unfortunately, the importance of these genes in macroevolution is difficult to assess and hence the identification of macroevolutionary key genes is a major challenge in evolutionary biology. In this study, we designed various word embedding libraries of natural language processing (NLP) considering the multiple mechanisms of evolutionary genomics. A novel method (IKGM) based on three types of attention mechanisms (domain attention, kmer attention and fused attention) were proposed to calculate the weights of different genes in macroevolution. Taking 34 species of diurnal butterflies and nocturnal moths in Lepidoptera as an example, we identified a few of key genes with high weights, which annotated to the functions of circadian rhythms, sensory organs, as well as behavioral habits etc. This study not only provides a novel method to identify the key genes of macroevolution at the genomic level, but also helps us to understand the microevolution mechanisms of diurnal butterflies and nocturnal moths in Lepidoptera.
Collapse
Affiliation(s)
- Jiawei Mao
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224, China
| | - Yong Cao
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224, China
| | - Yan Zhang
- College of Mathematics and Physics, Southwest Forestry University, Kunming, 650224, China
| | - Biaosheng Huang
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224, China
| | - Youjie Zhao
- College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224, China.
| |
Collapse
|
15
|
Younas MI, Iqbal MJ, Aziz A, Sodhro AH. Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:8885. [PMID: 37960584 PMCID: PMC10650388 DOI: 10.3390/s23218885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023]
Abstract
Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the "quality of service" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.
Collapse
Affiliation(s)
- Muhammad Irfan Younas
- Department of Computer System Engineering, Sukkur IBA University, Sukkur 65200, Pakistan;
- Institute of Space Science and Technology, University of Karachi, Karachi 75270, Pakistan;
| | - Muhammad Jawed Iqbal
- Institute of Space Science and Technology, University of Karachi, Karachi 75270, Pakistan;
| | - Abdul Aziz
- Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan;
| | - Ali Hassan Sodhro
- Department of Computer Science, Kristianstad University, 29188 Kristianstad, Sweden
| |
Collapse
|
16
|
Nikam R, Yugandhar K, Gromiha MM. Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2023; 1871:140948. [PMID: 37567456 DOI: 10.1016/j.bbapap.2023.140948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/05/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.
Collapse
Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India
| | - Kumar Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computational Biology, Cornell University, New York, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Department of Computer Science, National University of Singapore, Singapore.
| |
Collapse
|
17
|
Shan W, Chen L, Xu H, Zhong Q, Xu Y, Yao H, Lin K, Li X. GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47. Front Chem 2023; 11:1292869. [PMID: 37927570 PMCID: PMC10623438 DOI: 10.3389/fchem.2023.1292869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 μM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.
Collapse
Affiliation(s)
- Wenying Shan
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Lvqi Chen
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hao Xu
- Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry, Nanjing, China
- National Engineering Laboratory for Biomass Chemical Utilization, Nanjing, China
| | - Qinghao Zhong
- School of Humanities and Social Sciences, The Chinese University of Hong Kong, Shenzhen, China
| | - Yinqiu Xu
- Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xuanyi Li
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| |
Collapse
|
18
|
Li J, Wu N, Zhang J, Wu HH, Pan K, Wang Y, Liu G, Liu X, Yao Z, Zhang Q. Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction. NANO-MICRO LETTERS 2023; 15:227. [PMID: 37831203 PMCID: PMC10575847 DOI: 10.1007/s40820-023-01192-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/10/2023] [Indexed: 10/14/2023]
Abstract
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.
Collapse
Affiliation(s)
- Jin Li
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China
| | - Naiteng Wu
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China
| | - Jian Zhang
- New Energy Technology Engineering Lab of Jiangsu Province, College of Science, Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, People's Republic of China
| | - Hong-Hui Wu
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China.
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 8588, USA.
| | - Kunming Pan
- Henan Key Laboratory of High-Temperature Structural and Functional Materials, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan University of Science and Technology, Luoyang, 471003, People's Republic of China
| | - Yingxue Wang
- National Engineering Laboratory for Risk Perception and Prevention, Beijing, 100041, People's Republic of China.
| | - Guilong Liu
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China
| | - Xianming Liu
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China.
| | - Zhenpeng Yao
- Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, 200000, People's Republic of China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200000, People's Republic of China
| | - Qiaobao Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Materials, Xiamen University, Xiamen, 361005, People's Republic of China.
| |
Collapse
|
19
|
Zhang W, Xu Y, Wang A, Chen G, Zhao J. Fuse feeds as one: cross-modal framework for general identification of AMPs. Brief Bioinform 2023; 24:bbad336. [PMID: 37779248 DOI: 10.1093/bib/bbad336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.
Collapse
Affiliation(s)
- Wentao Zhang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Yanchao Xu
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Aowen Wang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Gang Chen
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Junbo Zhao
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| |
Collapse
|
20
|
Yassi M, Chatterjee A, Parry M. Application of deep learning in cancer epigenetics through DNA methylation analysis. Brief Bioinform 2023; 24:bbad411. [PMID: 37985455 PMCID: PMC10661960 DOI: 10.1093/bib/bbad411] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/08/2023] [Accepted: 10/25/2023] [Indexed: 11/22/2023] Open
Abstract
DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, such as cancer. Recent technological advances have led to the development of high-throughput approaches, such as genome-scale profiling, that allow for computational analysis of epigenetics. Deep learning (DL) methods are essential in facilitating computational studies in epigenetics for DNA methylation analysis. In this systematic review, we assessed the various applications of DL applied to DNA methylation data or multi-omics data to discover cancer biomarkers, perform classification, imputation and survival analysis. The review first introduces state-of-the-art DL architectures and highlights their usefulness in addressing challenges related to cancer epigenetics. Finally, the review discusses potential limitations and future research directions in this field.
Collapse
Affiliation(s)
- Maryam Yassi
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
- Honorary Professor, UPES University, Dehradun, India
| | - Matthew Parry
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand
- Te Pūnaha Matatini Centre of Research Excellence, University of Auckland, Auckland, New Zealand
| |
Collapse
|
21
|
Maraver P, Tecuatl C, Ascoli GA. Automatic identification of scientific publications describing digital reconstructions of neural morphology. Brain Inform 2023; 10:23. [PMID: 37684527 PMCID: PMC10491540 DOI: 10.1186/s40708-023-00202-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/06/2023] [Indexed: 09/10/2023] Open
Abstract
The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects.
Collapse
Affiliation(s)
- Patricia Maraver
- Bioengineering Department; College of Engineering and Computing, George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Bioengineering Department; College of Engineering and Computing, George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Giorgio A Ascoli
- Bioengineering Department; College of Engineering and Computing, George Mason University, Fairfax, VA, USA.
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| |
Collapse
|
22
|
Halawani R, Buchert M, Chen YPP. Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity. Comput Biol Med 2023; 164:107274. [PMID: 37506451 DOI: 10.1016/j.compbiomed.2023.107274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/03/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023]
Abstract
Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.
Collapse
Affiliation(s)
- Raid Halawani
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Michael Buchert
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia; Olivia Newton-John Cancer Research Institute, Melbourne, Victoria, Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
| |
Collapse
|
23
|
Zhu J, Oh JH, Simhal AK, Elkin R, Norton L, Deasy JO, Tannenbaum A. Geometric graph neural networks on multi-omics data to predict cancer survival outcomes. Comput Biol Med 2023; 163:107117. [PMID: 37329617 PMCID: PMC10638676 DOI: 10.1016/j.compbiomed.2023.107117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/19/2023]
Abstract
The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational methods to analyze large-scale, high-dimensional genomic data. However, traditional machine learning methods face a challenge in handling the high-dimensional, low-sample size problem that is shown in most genomic data sets. To address this, our group has developed geometric network analysis techniques on multi-omics data in connection with prior biological knowledge derived from protein-protein interactions (PPIs) or pathways. Geometric features obtained from the genomic network, such as Ollivier-Ricci curvature and the invariant measure of the associated Markov chain, have been shown to be predictive of survival outcomes in various cancers. In this study, we propose a novel supervised deep learning method called geometric graph neural network (GGNN) that incorporates such geometric features into deep learning for enhanced predictive power and interpretability. More specifically, we utilize a state-of-the-art graph neural network with sparse connections between the hidden layers based on known biology of the PPI network and pathway information. Geometric features along with multi-omics data are then incorporated into the corresponding layers. The proposed approach utilizes a local-global principle in such a manner that highly predictive features are selected at the front layers and fed directly to the last layer for multivariable Cox proportional-hazards regression modeling. The method was applied to multi-omics data from the CoMMpass study of multiple myeloma and ten major cancers in The Cancer Genome Atlas (TCGA). In most experiments, our method showed superior predictive performance compared to other alternative methods.
Collapse
Affiliation(s)
- Jiening Zhu
- Department of Applied Mathematics & Statistics, Stony Brook University, NY, USA.
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Anish K Simhal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Rena Elkin
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Larry Norton
- Department of Medicine, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, USA.
| | - Allen Tannenbaum
- Department of Applied Mathematics & Statistics, Stony Brook University, NY, USA; Department of Computer Science, Stony Brook University, NY, USA.
| |
Collapse
|
24
|
Kea K, Han Y, Kim TK. Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning. PLoS One 2023; 18:e0290337. [PMID: 37594957 PMCID: PMC10437833 DOI: 10.1371/journal.pone.0290337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/04/2023] [Indexed: 08/20/2023] Open
Abstract
The growing use of Internet-of-Things devices in electric power systems has resulted in increased complexity and flexibility, making monitoring power usage critical for effective system maintenance and detecting abnormal behavior. However, traditional anomalous power consumption detection methods struggle to handle the vast amounts of data generated by these devices. While deep learning and machine learning are effective in anomaly detection, they require significant amounts of training data collected on centralized servers. This centralized approach results in high response time delays and data leakage problems. To address these challenges, we propose an Autoencoder-based Federated Learning method that combines the AutoEncoder and Federated Learning networks to develop a high-accuracy algorithm for identifying anomalies of power consumption data in distributed power systems. The proposed method allows for decentralized training of anomaly detection models among IoT devices, reducing response time and eventually solving data leakage issues. Our experimental results demonstrate the effectiveness of the FLAE method in detecting anomalies without needing data transferring.
Collapse
Affiliation(s)
- Kimleang Kea
- Department of AI Convergence, Pukyong National University, Nam-gu, Busan, South Korea
| | - Youngsun Han
- Department of AI Convergence, Pukyong National University, Nam-gu, Busan, South Korea
| | - Tae-Kyung Kim
- Department of Computer & Information Technology, Incheon Jaeneung University, Dong-gu, Incheon, South Korea
| |
Collapse
|
25
|
Wang D, Gao L, Gao X, Wang C, Tian S. Identification of monotonically expressed long non-coding RNA signatures for breast cancer using variational autoencoders. PLoS One 2023; 18:e0289971. [PMID: 37561760 PMCID: PMC10414641 DOI: 10.1371/journal.pone.0289971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 07/29/2023] [Indexed: 08/12/2023] Open
Abstract
As breast cancer is a multistage progression disease resulting from a genetic sequence of mutations, understanding the genes whose expression values increase or decrease monotonically across pathologic stages can provide insightful clues about how breast cancer initiates and advances. Utilizing variational autoencoder (VAE) networks in conjunction with traditional statistical testing, we successfully ascertain long non-coding RNAs (lncRNAs) that exhibit monotonically differential expression values in breast cancer. Subsequently, we validate that the identified lncRNAs really present monotonically changed patterns. The proposed procedure identified 248 monotonically decreasing expressed and 115 increasing expressed lncRNAs. They correspond to a total of 65 and 33 genes respectively, which possess unique known gene symbols. Some of them are associated with breast cancer, as suggested by previous studies. Furthermore, enriched pathways by the target mRNAs of these identified lncRNAs include the Wnt signaling pathway, human papillomavirus (HPV) infection, and Rap 1 signaling pathway, which have been shown to play crucial roles in the initiation and development of breast cancer. Additionally, we trained a VAE model using the entire dataset. To assess the effectiveness of the identified lncRNAs, a microarray dataset was employed as the test set. The results obtained from this evaluation were deemed satisfactory. In conclusion, further experimental validation of these lncRNAs with a large-sized study is warranted, and the proposed procedure is highly recommended.
Collapse
Affiliation(s)
- Dongjiao Wang
- Department of Gynecological Oncology, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
| | - Ling Gao
- Department of Radiation Oncology, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
| | - Xinliang Gao
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
| | - Chi Wang
- Department of Internal Medicine, College of Medicine, University of Kentucky, Lexington, Kentucky, United States of America
- Markey Cancer Center, University of Kentucky, Lexington, KY, United States of America
| | - Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin, People’s Republic of China
| |
Collapse
|
26
|
Gomes RFT, Schuch LF, Martins MD, Honório EF, de Figueiredo RM, Schmith J, Machado GN, Carrard VC. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review. J Digit Imaging 2023; 36:1060-1070. [PMID: 36650299 PMCID: PMC10287602 DOI: 10.1007/s10278-023-00775-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.
Collapse
Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil.
| | - Lauren Frenzel Schuch
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Manoela Domingues Martins
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | | | - Rodrigo Marques de Figueiredo
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Jean Schmith
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Giovanna Nunes Machado
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Vinicius Coelho Carrard
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Epidemiology, School of Medicine, TelessaúdeRS-UFRGS, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- Department of Oral Medicine, Otorhinolaryngology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
| |
Collapse
|
27
|
Alzoubi H, Alzubi R, Ramzan N. Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094439. [PMID: 37177642 PMCID: PMC10181706 DOI: 10.3390/s23094439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals' disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.
Collapse
Affiliation(s)
- Hadeel Alzoubi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Raid Alzubi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK
| |
Collapse
|
28
|
Rather MA, Agarwal D, Bhat TA, Khan IA, Zafar I, Kumar S, Amin A, Sundaray JK, Qadri T. Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture. Int J Biol Macromol 2023; 233:123549. [PMID: 36740117 DOI: 10.1016/j.ijbiomac.2023.123549] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.
Collapse
Affiliation(s)
- Mohd Ashraf Rather
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir, India.
| | - Deepak Agarwal
- Institute of Fisheries Post Graduation Studies OMR Campus, Vaniyanchavadi, Chennai, India
| | | | - Irfan Ahamd Khan
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir, India
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Punjab, Pakistan
| | - Sujit Kumar
- Department of Bioinformatics and Computational Biology, Virtual University Punjab, Pakistan
| | - Adnan Amin
- Postgraduate Institute of Fisheries Education and Research Kamdhenu University, Gandhinagar-India University of Kurasthra, India; Department of Aquatic Environmental Management, Faculty of Fisheries Rangil- Ganderbel -SKUAST-K, India
| | - Jitendra Kumar Sundaray
- ICAR-Central Institute of Freshwater Aquaculture, Kausalyaganga, Bhubaneswar, Odisha 751002, India
| | - Tahiya Qadri
- Division of Food Science and Technology, SKUAST-K, Shalimar, India
| |
Collapse
|
29
|
Bachmann L, Beermann J, Brey T, de Boer HJ, Dannheim J, Edvardsen B, Ericson PGP, Holston KC, Johansson VA, Kloss P, Konijnenberg R, Osborn KJ, Pappalardo P, Pehlke H, Piepenburg D, Struck TH, Sundberg P, Markussen SS, Teschke K, Vanhove MPM. The role of systematics for understanding ecosystem functions: Proceedings of the Zoologica Scripta Symposium, Oslo, Norway, 25 August 2022. ZOOL SCR 2023. [DOI: 10.1111/zsc.12593] [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]
|
30
|
Sharma M, Patel RK, Garg A, SanTan R, Acharya UR. Automated detection of schizophrenia using deep learning: a review for the last decade. Physiol Meas 2023; 44. [PMID: 36630717 DOI: 10.1088/1361-6579/acb24d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/11/2023] [Indexed: 01/12/2023]
Abstract
Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.
Collapse
Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ruchit Kumar Patel
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Akshat Garg
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ru SanTan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore
| |
Collapse
|
31
|
Maraver P, Tecuatl C, Ascoli GA. Automatic identification of scientific publications describing digital reconstructions of neural morphology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.527522. [PMID: 36824882 PMCID: PMC9949101 DOI: 10.1101/2023.02.14.527522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Motivation The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. Results The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects. Availability https://github.com/Joindbre/TextRelevancy. Contact ascoli@gmu.edu. Supplementary information Supplementary information, tool installation, and API usage are available at https://docs.joindbre.com.
Collapse
Affiliation(s)
- Patricia Maraver
- Bioengineering Department; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Bioengineering Department; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Bioengineering Department; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
| |
Collapse
|
32
|
Said A, Shabbir M, Hassan SU, Hassan ZR, Ahmed A, Koutsoukos X. On augmenting topological graph representations for attributed graphs. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
33
|
Li Z, Gao E, Zhou J, Han W, Xu X, Gao X. Applications of deep learning in understanding gene regulation. CELL REPORTS METHODS 2023; 3:100384. [PMID: 36814848 PMCID: PMC9939384 DOI: 10.1016/j.crmeth.2022.100384] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.
Collapse
Affiliation(s)
- Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Elva Gao
- The KAUST School, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| |
Collapse
|
34
|
DeepCF-PPI: improved prediction of protein-protein interactions by combining learned and handcrafted features based on attention mechanisms. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04387-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
35
|
Wang F, Feng X, Kong R, Chang S. Generating new protein sequences by using dense network and attention mechanism. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4178-4197. [PMID: 36899622 DOI: 10.3934/mbe.2023195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Protein engineering uses de novo protein design technology to change the protein gene sequence, and then improve the physical and chemical properties of proteins. These newly generated proteins will meet the needs of research better in properties and functions. The Dense-AutoGAN model is based on GAN, which is combined with an Attention mechanism to generate protein sequences. In this GAN architecture, the Attention mechanism and Encoder-decoder can improve the similarity of generated sequences and obtain variations in a smaller range on the original basis. Meanwhile, a new convolutional neural network is constructed by using the Dense. The dense network transmits in multiple layers over the generator network of the GAN architecture, which expands the training space and improves the effectiveness of sequence generation. Finally, the complex protein sequences are generated on the mapping of protein functions. Through comparisons of other models, the generated sequences of Dense-AutoGAN verify the model performance. The new generated proteins are highly accurate and effective in chemical and physical properties.
Collapse
Affiliation(s)
- Feng Wang
- School of Computer Engineering, Suzhou Vocational University, Suzhou, China
- Information Engineering Department, Changzhou University Huaide College, Taizhou, China
| | - Xiaochen Feng
- Information Engineering Department, Changzhou University Huaide College, Taizhou, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
| |
Collapse
|
36
|
Kim YW, Lee S. Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition. SENSORS (BASEL, SWITZERLAND) 2022; 23:184. [PMID: 36616781 PMCID: PMC9823777 DOI: 10.3390/s23010184] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a data valuation algorithm for inertial measurement unit-based human activity recognition (IMU-based HAR) data based on meta reinforcement learning. Unlike previous studies that received feature-level input, the algorithm in this study added a feature extraction structure to the data valuation algorithm, and it can receive raw-level inputs and achieve excellent performance. As IMU-based HAR data are multivariate time-series data, the proposed algorithm incorporates an architecture capable of extracting both local and global features by inserting a transformer encoder after the one-dimensional convolutional neural network (1D-CNN) backbone in the data value estimator. In addition, the 1D-CNN-based stacking ensemble structure, which exhibits excellent efficiency and performance on IMU-based HAR data, is used as a predictor to supervise model training. The Berg balance scale (BBS) IMU-based HAR dataset and the public datasets, UCI-HAR, WISDM, and PAMAP2, are used for performance evaluation in this study. The valuation performance of the proposed algorithm is observed to be excellent on IMU-based HAR data. The rate of discovering corrupted data is higher than 96% on all datasets. In addition, classification performance is confirmed to be improved by the suppression of discovery of low-value data.
Collapse
Affiliation(s)
- Yeon-Wook Kim
- Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Sangmin Lee
- Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea
- Department of Smart Engineering Program in Biomedical Science & Engineering, Inha University, Incheon 22212, Republic of Korea
| |
Collapse
|
37
|
Zhapa-Camacho F, Kulmanov M, Hoehndorf R. mOWL: Python library for machine learning with biomedical ontologies. Bioinformatics 2022; 39:6935780. [PMID: 36534832 PMCID: PMC9848046 DOI: 10.1093/bioinformatics/btac811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/25/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Ontologies contain formal and structured information about a domain and are widely used in bioinformatics for annotation and integration of data. Several methods use ontologies to provide background knowledge in machine learning tasks, which is of particular importance in bioinformatics. These methods rely on a set of common primitives that are not readily available in a software library; a library providing these primitives would facilitate the use of current machine learning methods with ontologies and the development of novel methods for other ontology-based biomedical applications. RESULTS We developed mOWL, a Python library for machine learning with ontologies formalized in the Web Ontology Language (OWL). mOWL implements ontology embedding methods that map information contained in formal knowledge bases and ontologies into vector spaces while preserving some of the properties and relations in ontologies, as well as methods to use these embeddings for similarity computation, deductive inference and zero-shot learning. We demonstrate mOWL on the knowledge-based prediction of protein-protein interactions using the gene ontology and gene-disease associations using phenotype ontologies. AVAILABILITY AND IMPLEMENTATION mOWL is freely available on https://github.com/bio-ontology-research-group/mowl and as a Python package in PyPi. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Fernando Zhapa-Camacho
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Maxat Kulmanov
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | | |
Collapse
|
38
|
Albahri AS, Zaidan AA, AlSattar HA, A. Hamid R, Albahri OS, Qahtan S, Alamoodi AH. Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex
T
‐spherical fuzzy‐weighted zero‐inconsistency method. Comput Intell 2022. [DOI: 10.1111/coin.12562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Ahmed S. Albahri
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Aws A. Zaidan
- Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates
| | - Hassan A. AlSattar
- Department of Business Administration, College of Administrative Sciences The University of Mashreq Baghdad Iraq
| | - Rula A. Hamid
- Informatics Institute for Postgraduate Studies (IIPS) Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq
| | - Osamah S. Albahri
- Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq
| | - Sarah Qahtan
- Department of Computer Center, College of Health and Medical Techniques Middle Technical University Baghdad Iraq
| | - Abdulla H. Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia
| |
Collapse
|
39
|
Wen LY, Zhang XM, Li QF, Min F. KGA: integrating KPCA and GAN for microbial data augmentation. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-022-01707-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
40
|
Yan J, Cai J, Zhang B, Wang Y, Wong DF, Siu SWI. Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning. Antibiotics (Basel) 2022; 11:1451. [PMID: 36290108 PMCID: PMC9598685 DOI: 10.3390/antibiotics11101451] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.
Collapse
Affiliation(s)
- Jielu Yan
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Jianxiu Cai
- Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
- Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China
| | - Bob Zhang
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Yapeng Wang
- Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
| | - Derek F. Wong
- NLP2CT Lab, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Shirley W. I. Siu
- Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
| |
Collapse
|
41
|
Zha Y, Chong H, Yang P, Ning K. Microbial Dark Matter: from Discovery to Applications. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:867-881. [PMID: 35477055 PMCID: PMC10025686 DOI: 10.1016/j.gpb.2022.02.007] [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] [Received: 07/06/2021] [Revised: 09/28/2021] [Accepted: 03/22/2022] [Indexed: 01/12/2023]
Abstract
With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.
Collapse
Affiliation(s)
- Yuguo Zha
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Chong
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Pengshuo Yang
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Kang Ning
- MOE Key Laboratory of Molecular Biophysics, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
| |
Collapse
|
42
|
Text Score Analysis under the IPE Environment Based on Improved Transformer. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:8354429. [PMID: 36213025 PMCID: PMC9536923 DOI: 10.1155/2022/8354429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/05/2022] [Accepted: 09/17/2022] [Indexed: 11/26/2022]
Abstract
In order to improve the accuracy of ideological and political education (IPE) text scoring, an improved short-text similarity calculation model based on transformer is proposed. This model takes the DSSM model as the basic framework and uses the Bert model to realize text representation and solve polysemy problem. The transformer encoding component is used to extract the characteristics of the text and obtain the internal information of the text. With the help of the encoding component, the two texts can interact with information on multiple levels. Finally, the semantic similarity between two texts is calculated by concatenation vector inference.
Collapse
|
43
|
Shastry KA, Vijayakumar V, V MKM, B A M, B N C. Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review. Healthcare (Basel) 2022; 10:1842. [PMID: 36292289 PMCID: PMC9601959 DOI: 10.3390/healthcare10101842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.
Collapse
Affiliation(s)
- K Aditya Shastry
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
| | - V Vijayakumar
- School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
- School of NUOVOS, Ajeenkya D Y Patil University, Pune 412105, India
- Swiss School of Business and Management, 1213 Geneva, Switzerland
| | - Manoj Kumar M V
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
| | - Manjunatha B A
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
| | - Chandrashekhar B N
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
| |
Collapse
|
44
|
Gu X. Evaluation of Teaching Quality on IP Environment Driven by Multiple Values Theory Based on Big Data. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:4857155. [PMID: 36159768 PMCID: PMC9507675 DOI: 10.1155/2022/4857155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/19/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022]
Abstract
Despite the fact that big data technology has been applied in education, there are no studies and cases that combine big data with ideological and political (IP) teaching quality. At the same time, the existing methods of IP teaching quality evaluation lack the consideration of multiple values, and the system is not complete and systematic. The use of big data analysis technology can improve the rigor of teaching quality assessment and make the data analysis more scientific, so as to improve the management system of universities and enhance the education quality. Therefore, this paper fully considers the background conditions of large data at this stage, on the basis of studying the methods of evaluating the quality of IP teaching in colleges. The big data about teaching quality is obtained by distributed algorithm, and multiple value indicators are drawn into the quality evaluation system as a main driver to emphasize the multiple value theory. Hierarchical analysis (AHP) method and fuzzy comprehensive evaluation (FCE) method are selected as the data analysis methods to provide evaluation basis for the proposed model. This model can further test the evaluation index system of education and further verify the rationality of the distribution of the weight of indicators at all levels. The evaluation results based on the large educational data and research data of a university show that the IP teaching quality of the university is excellent. The comprehensive evaluation model overcomes the limitations of traditional evaluation methods and provides a more comprehensive analysis about the teaching quality of IP teaching in colleges. Meanwhile, the conclusions obtained by the proposed evaluation model can be used for both the overall comprehensive evaluation of teachers' teaching quality and a single comprehensive evaluation of the single factor affecting teaching quality. Using the evaluation results obtained by the model, we can set up advanced models and encourage backward students to have evidence. With the single-index evaluation, we can know what advantages the IP teaching or a certain teacher has and what aspects need to be strengthened. Therefore, we can put forward reasonable suggestions to progress instructing strategies and educating quality.
Collapse
Affiliation(s)
- Xiujuan Gu
- College of Marxism, Xiangtan University, Xiangtan 411105, China
| |
Collapse
|
45
|
Li Z, Chen X, Shi S, Zhang H, Wang X, Chen H, Li W, Li L. DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits. MOLECULAR PLANT 2022; 15:1418-1427. [PMID: 35996754 DOI: 10.1016/j.molp.2022.08.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/03/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Bulked segregant analysis (BSA) is a rapid, cost-effective method for mapping mutations and quantitative trait loci (QTLs) in animals and plants based on high-throughput sequencing. However, the algorithms currently used for BSA have not been systematically evaluated and are complex and fallible to operate. We developed a BSA method driven by deep learning, DeepBSA, for QTL mapping and functional gene cloning. DeepBSA is compatible with a variable number of bulked pools and performed well with various simulated and real datasets in both animals and plants. DeepBSA outperformed all other algorithms when comparing absolute bias and signal-to-noise ratio. Moreover, we applied DeepBSA to an F2 segregating maize population of 7160 individuals and uncovered five candidate QTLs, including three well-known plant-height genes. Finally, we developed a user-friendly graphical user interface for DeepBSA, by integrating five widely used BSA algorithms and our two newly developed algorithms, that is easy to operate and can quickly map QTLs and functional genes. The DeepBSA software is freely available to non-commercial users at http://zeasystemsbio.hzau.edu.cn/tools.html and https://github.com/lizhao007/DeepBSA.
Collapse
Affiliation(s)
- Zhao Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hainan Yazhou Bay Seed Lab, Hainan, China
| | - Xiaoxuan Chen
- College of Science, Huazhong Agricultural University, Wuhan 430070, China
| | - Shaoqiang Shi
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hong Chen
- College of Science, Huazhong Agricultural University, Wuhan 430070, China
| | - Weifu Li
- College of Science, Huazhong Agricultural University, Wuhan 430070, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hainan Yazhou Bay Seed Lab, Hainan, China.
| |
Collapse
|
46
|
Pan Y, Chen J, Zhang Y, Zhang Y. An efficient CNN-LSTM Network with spectral normalization and label smoothing technologies for SSVEP frequency recognition. J Neural Eng 2022; 19. [PMID: 36041426 DOI: 10.1088/1741-2552/ac8dc5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/30/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Steady-state visual evoked potentials(SSVEPs) based braincomputer interface(BCI) has received great interests owing to the high information transfer rate(ITR) and available large number of targets. However, the performance of frequency recognition methods heavily depends on the amount of the calibration data for intra-subject classification. Some research adopted the deep learning(DL) algorithm to conduct the inter-subject classification, which could reduce the calculation procedure, but the performance still has large room to improve compared with the intra-subject classification. APPROACH To address these issues, we proposed an efficient SSVEP DL NETwork (termed SSVEPNET) based on 1D convolution and long short-term memory (LSTM) module. To enhance the performance of SSVEPNT, we adopted the spectral normalization and label smoothing technologies during implementing the network architecture. We evaluated the SSVEPNET and compared it with other methods for the intra- and inter-subject classification under different conditions, i.e., two datasets, two time-window lengths (1 s and 0.5 s), three sizes of training data. MAIN RESULTS Under all the experimental settings, the proposed SSVEPNET achieved the highest average accuracy for the intra- and inter-subject classification on the two SSVEP datasets, when compared with other traditional and DL baseline methods. Signif icance. The extensive experimental results demonstrate that the proposed DL model holds promise to enhance frequency recognition performance in SSVEP-based BCIs. Besides, the mixed network structures with CNN and LSTM, and the spectral normalization and label smoothing could be useful optimization strategies to design efficient models for EEG data.
Collapse
Affiliation(s)
- YuDong Pan
- Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang,CN,621010, Mianyang, 621010, CHINA
| | - Jianbo Chen
- Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang 621010, China, Mianyang, 621010, CHINA
| | - Yangsong Zhang
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang,CN,621010, Mianyang, 621010, CHINA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA, Bethlehem, 18015-3027, UNITED STATES
| |
Collapse
|
47
|
Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review. Comput Biol Med 2022; 149:106020. [DOI: 10.1016/j.compbiomed.2022.106020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/16/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022]
|
48
|
Ghouali S, Onyema EM, Guellil MS, Wajid MA, Clare O, Cherifi W, Feham M. Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:124-133. [PMID: 36712318 PMCID: PMC9870271 DOI: 10.1109/ojemb.2022.3192780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/22/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023] Open
Abstract
Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.
Collapse
Affiliation(s)
- S Ghouali
- Faculty of Sciences and TechnologyMustapha Stambouli University Mascara 29000 Algeria
| | - E M Onyema
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
- Adjunct Faculty, Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai 602105 India
| | - M S Guellil
- Faculty of Economics, Business and Management Sciences, MCLDL LaboratoryUniversity of Mascara Mascara 29000 Algeria
| | - M A Wajid
- Department of Computer ScienceAligarh Muslim University Aligarh 202002 India
| | - O Clare
- Department of Mathematics and Computer ScienceCoal City University Enugu 400104 Nigeria
| | - W Cherifi
- InnoDev (Dev Software) Tlemcen 13000 Algeria
| | - M Feham
- STIC Lab, Faculty of TechnologyUniversity of Tlemcen Tlemcen 13000 Algeria
| |
Collapse
|
49
|
Vasa J, Thakkar A. Deep Learning: Differential Privacy Preservation in the Era of Big Data. JOURNAL OF COMPUTER INFORMATION SYSTEMS 2022. [DOI: 10.1080/08874417.2022.2089775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Jalpesh Vasa
- Chandubhai S Patel Institute of Technology (CSPIT), Charotar University of Science and Technology (CHARUSAT), Changa, India
| | - Amit Thakkar
- Chandubhai S Patel Institute of Technology (CSPIT), Charotar University of Science and Technology (CHARUSAT), Changa, India
| |
Collapse
|
50
|
Wang Z, Lei X. A web server for identifying circRNA-RBP variable-length binding sites based on stacked generalization ensemble deep learning network. Methods 2022; 205:179-190. [PMID: 35810958 DOI: 10.1016/j.ymeth.2022.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/23/2022] [Accepted: 06/28/2022] [Indexed: 11/28/2022] Open
Abstract
Circular RNA (circRNA) can exert biological functions by interacting with RNA-binding protein (RBP), and some deep learning-based methods have been developed to predict RBP binding sites on circRNA. However, most of these methods identify circRNA-RBP binding sites are only based on single data resource and cannot provide exact binding sites, only providing the probability value of a sequence fragment. To solve these problems, we propose a binding sites localization algorithm that fuses binding sites from multiple databases, and further design a stacked generalization ensemble deep learning model named CirRBP to identify RBP binding sites on circRNA. The CirRBP is trained by combining the binding sites from multiple databases and makes predictions by weighted aggregating the predictions of each sub-model. The results show that the CirRBP outperforms any sub-model and existing online prediction model. For better access to our research results, we develop an open-source web application called CRWS (CircRNA-RBP Web Server). Its back-end learning model of the CRWS is a stacked generalization ensemble learning model CirRBP based on different deep learning frameworks. Given a full-length circRNA or fragment sequence and a target RBP, the CRWS can analyze and provide the exact potential binding sites of the target RBP on the given sequence through the binding sites localization algorithm, and visualize it. In addition, the CRWS can discover the most widely distributed motif in each RBP dataset. Up to now, CRWS is the first significant online tool that uses multi-source data to train models and predict exact binding sites. CRWS is now publicly and freely available without login requirement at: http://www.bioinformatics.team.
Collapse
Affiliation(s)
- Zhengfeng Wang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China; College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China; Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.
| |
Collapse
|