1
|
Feng R, Li S, Zhang Y. AI-powered microscopy image analysis for parasitology: integrating human expertise. Trends Parasitol 2024; 40:633-646. [PMID: 38824067 DOI: 10.1016/j.pt.2024.05.005] [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: 03/07/2024] [Revised: 05/06/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.
Collapse
Affiliation(s)
- Ruijun Feng
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China; School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Sen Li
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
| |
Collapse
|
2
|
Xu J, Ruan X, Yang J, Hu B, Li S, Hu J. SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction. Comput Biol Chem 2024; 109:108033. [PMID: 38412804 DOI: 10.1016/j.compbiolchem.2024.108033] [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: 11/11/2023] [Revised: 01/09/2024] [Accepted: 02/17/2024] [Indexed: 02/29/2024]
Abstract
As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine learning methods have been developed for functional peptide prediction. However, the main research heavily relies on statistical features and few consider multifunctional peptide identification. So, we propose SME-MFP, a novel predictor in the imbalanced multi-label functional peptide datasets. First, we employ physicochemical and evolutionary information to represent the peptide sequence's initialization features from multiple perspectives. Second, the features are fused and then put into spatial feature extractors, where the residual connection and multiscale convolutional neural network extract more discriminative features of different lengths' peptide sequences. Besides, we also design AFT-based temporal feature extractors to fully capture the global interactions of the sequences. Finally, devising a new loss to replace the traditional cross entropy loss to settle the class imbalance problems. The results show that our framework not only enhances the model's ability to capture sequence features effectively, but also accuracy improves by 3.89% over existing methods on public peptide datasets.
Collapse
Affiliation(s)
- Jing Xu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Xiaoli Ruan
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China.
| | - Jing Yang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Bingqi Hu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Shaobo Li
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Jianjun Hu
- Department of Computer Science and Engineering, University of South Carolina, Columbia 29208, USA
| |
Collapse
|
3
|
Lee B, Shin D. Contrastive learning for enhancing feature extraction in anticancer peptides. Brief Bioinform 2024; 25:bbae220. [PMID: 38725157 PMCID: PMC11082072 DOI: 10.1093/bib/bbae220] [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: 01/08/2024] [Revised: 03/28/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive learning. Our model achieved superior performance on five of six benchmark datasets against previous state-of-the-art models. As prediction tools advance, the potential in peptide-based cancer therapeutics increases, promising a brighter future for oncology research and patient care.
Collapse
Affiliation(s)
- Byungjo Lee
- Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
| | - Dongkwan Shin
- Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
- Department of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
| |
Collapse
|
4
|
Wang R, Wang T, Zhuo L, Wei J, Fu X, Zou Q, Yao X. Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. Brief Bioinform 2024; 25:bbae078. [PMID: 38446739 PMCID: PMC10939340 DOI: 10.1093/bib/bbae078] [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: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
Collapse
Affiliation(s)
- Rui Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Jinhang Wei
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012 Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 611730 Chengdu, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
| |
Collapse
|
5
|
Chung CR, Liou JT, Wu LC, Horng JT, Lee TY. Multi-label classification and features investigation of antimicrobial peptides with various functional classes. iScience 2023; 26:108250. [PMID: 38025779 PMCID: PMC10679894 DOI: 10.1016/j.isci.2023.108250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/15/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens.
Collapse
Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Jhen-Ting Liou
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taoyuan City, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
- Center for Intelligent Drug Systems and Smart Biodevices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
| |
Collapse
|
6
|
Le NQK. Leveraging transformers-based language models in proteome bioinformatics. Proteomics 2023; 23:e2300011. [PMID: 37381841 DOI: 10.1002/pmic.202300011] [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: 05/14/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 06/30/2023]
Abstract
In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.
Collapse
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| |
Collapse
|
7
|
Xing W, Zhang J, Li C, Huo Y, Dong G. iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. Brief Bioinform 2023; 25:bbad443. [PMID: 38055840 PMCID: PMC10699745 DOI: 10.1093/bib/bbad443] [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: 09/08/2023] [Revised: 10/31/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.
Collapse
Affiliation(s)
- Wenxuan Xing
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Jie Zhang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Chen Li
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Yujia Huo
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Gaifang Dong
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| |
Collapse
|
8
|
Yao L, Zhang Y, Li W, Chung C, Guan J, Zhang W, Chiang Y, Lee T. DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning. Protein Sci 2023; 32:e4758. [PMID: 37595093 PMCID: PMC10503419 DOI: 10.1002/pro.4758] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/02/2023] [Accepted: 08/10/2023] [Indexed: 08/20/2023]
Abstract
Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.
Collapse
Affiliation(s)
- Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
- School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina
| | - Yuntian Zhang
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Wenshuo Li
- School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina
| | - Chia‐Ru Chung
- Department of Computer Science and Information EngineeringNational Central UniversityTaoyuanTaiwan
| | - Jiahui Guan
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Wenyang Zhang
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Ying‐Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Tzong‐Yi Lee
- Institute of Bioinformatics and Systems BiologyNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
- Center for Intelligent Drug Systems and Smart Bio‐devices (IDS2B)National Yang Ming Chiao Tung UniversityHsinchuTaiwan
| |
Collapse
|
9
|
Qiu S, Liu R, Liang Y. GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network. Comput Biol Med 2023; 163:107202. [PMID: 37450964 DOI: 10.1016/j.compbiomed.2023.107202] [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: 05/08/2023] [Revised: 06/14/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023]
Abstract
RNA N6-methyladenine (m6A), which is produced by the methylation of the N6 position of eukaryotic adenine, is a relatively common post-transcriptional modification on the surface of the molecule, which frequently plays a crucial role in biological processes. Biological experimental methods to identify m6A have been studied and implemented in recent years, but they cannot be promoted widely due to drawbacks such as the time and cost of reagents and equipment. Therefore, researchers have proposed computational strategies for identifying m6A sites, but these strategies do not account for the mechanism of methylation occurrence or the structure of RNA molecules. This study, therefore, proposed a novel deep learning model for predicting m6A sites, GR-m6A, which predicts m6A sites by extracting features from the physicochemical properties and spatial structure of molecules via residual networks. In GR-m6A, each RNA base string is represented by SMILES as two matrices comprising topology structural information and node attributes with molecular physicochemical characteristics. The feature encoding matrix was then obtained by fusing the topology matrix and the node matrix in accordance with the graphical convolutional network principle. Correspondingly, the more discriminative features were extracted from the encoding matrix using the residual neural network and predicted using a multilayer perceptron. As evident from the 5-fold cross-validation and independent validation, the GR-m6A model outperformed other existing methods. Thus, we hope that GR-m6A can aid researchers in predicting mammalian m6A loci. The source code and database are available at https://github.com/YingLiangjxau/GR-m6A.
Collapse
Affiliation(s)
- Shi Qiu
- College of Engineering, Jiangxi Agricultural University, Nanchang 310045, Jiangxi, China.
| | - Renxin Liu
- College of Engineering, Jiangxi Agricultural University, Nanchang 310045, Jiangxi, China.
| | - Ying Liang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 310045, Jiangxi, China.
| |
Collapse
|