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Li X, Sun Y, Zhou Z, Li J, Liu S, Chen L, Shi Y, Wang M, Zhu Z, Wang G, Lu Q. Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308970. [PMID: 38454653 PMCID: PMC11095145 DOI: 10.1002/advs.202308970] [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: 11/21/2023] [Revised: 02/15/2024] [Indexed: 03/09/2024]
Abstract
Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.
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Affiliation(s)
- Xinuo Li
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Yuan Sun
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Zheng Zhou
- Department of Computer ScienceRWTH Aachen University52074AachenGermany
| | - Jinran Li
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Sai Liu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Long Chen
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Yiting Shi
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Min Wang
- Affiliated Brain Hospital of Nanjing Medical UniversityNanjing210029China
| | - Zheying Zhu
- School of PharmacyThe University of NottinghamNottinghamNG7 2RDUK
| | - Guangji Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Qiulun Lu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
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Zhang R, Wu C, Yang Q, Liu C, Wang Y, Li K, Huang L, Zhou F. MolFeSCue: enhancing molecular property prediction in data-limited and imbalanced contexts using few-shot and contrastive learning. Bioinformatics 2024; 40:btae118. [PMID: 38426310 PMCID: PMC10984949 DOI: 10.1093/bioinformatics/btae118] [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: 12/09/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
MOTIVATION Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models. RESULTS This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm's effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery. AVAILABILITY AND IMPLEMENTATION We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper.
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Affiliation(s)
- Ruochi Zhang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Chao Wu
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Qian Yang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Chang Liu
- Beijing Life Science Academy, Beijing 102209, China
| | - Yan Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou 550025, China
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Sharma A, Selvam S, Balaji PD, Madhavan T. ANN multi-layer perceptron for prediction of blood-brain barrier permeable compounds for central nervous system therapeutics. J Biomol Struct Dyn 2024:1-6. [PMID: 38497749 DOI: 10.1080/07391102.2024.2326671] [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: 04/18/2023] [Accepted: 02/28/2024] [Indexed: 03/19/2024]
Abstract
Endothelial cells produce a semipermeable barrier known as the blood-brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study's outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aditi Sharma
- Computational Biology Lab, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India
| | - Subathra Selvam
- Computational Biology Lab, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India
| | - Priya Dharshini Balaji
- Computational Biology Lab, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India
| | - Thirumurthy Madhavan
- Computational Biology Lab, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India
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Qi X, Zhao Y, Qi Z, Hou S, Chen J. Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges. Molecules 2024; 29:903. [PMID: 38398653 PMCID: PMC10892089 DOI: 10.3390/molecules29040903] [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: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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Affiliation(s)
- Xin Qi
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Yuanchun Zhao
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Zhuang Qi
- School of Software, Shandong University, Jinan 250101, China;
| | - Siyu Hou
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Jiajia Chen
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
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5
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Zhang G, Li M, Tang Q, Meng F, Feng P, Chen W. MulCNN-HSP: A multi-scale convolutional neural networks-based deep learning method for classification of heat shock proteins. Int J Biol Macromol 2024; 257:128802. [PMID: 38101670 DOI: 10.1016/j.ijbiomac.2023.128802] [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: 10/23/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 12/17/2023]
Abstract
Heat shock proteins (HSPs) are crucial cellular stress proteins that react to environmental cues, ensuring the preservation of cellular functions. They also play pivotal roles in orchestrating the immune response and participating in processes associated with cancer. Consequently, the classification of HSPs holds immense significance in enhancing our understanding of their biological functions and in various diseases. However, the use of computational methods for identifying and classifying HSPs still faces challenges related to accuracy and interpretability. In this study, we introduced MulCNN-HSP, a novel deep learning model based on multi-scale convolutional neural networks, for identifying and classifying of HSPs. Comparative results showed that MulCNN-HSP outperforms or matches existing models in the identification and classification of HSPs. Furthermore, MulCNN-HSP can extract and analyze essential features for the prediction task, enhancing its interpretability. To facilitate its accessibility, we have made MulCNN-HSP available at http://cbcb.cdutcm.edu.cn/HSP/. We hope that MulCNN-HSP will contribute to advancing the study of HSPs and their roles in various biological processes and diseases.
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Affiliation(s)
- Guiyang Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Mingrui Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Qiang Tang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Fanbo Meng
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Pengmian Feng
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
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Jiang M, Wu Q, Guo H, Lu X, Chen S, Liu L, Chen S. Shikimate-Derived Meroterpenoids from the Ascidian-Derived Fungus Amphichorda felina SYSU-MS7908 and Their Anti-Glioma Activity. JOURNAL OF NATURAL PRODUCTS 2023; 86:2651-2660. [PMID: 37967166 DOI: 10.1021/acs.jnatprod.3c00664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
Glioma is a clinically heterogeneous type of brain tumor with a poor prognosis. Current treatment approaches have limited effectiveness in treating glioma, highlighting the need for novel drugs. One approach is to explore marine natural products for their therapeutic potential. In this study, we isolated nine shikimate-derived diisoprenyl-cyclohexene/ane-type meroterpenoids (1-9), including four new ones, amphicordins A-D (1-4) from the ascidian-derived fungus Amphichorda felina SYSU-MS7908, and further semisynthesized four derivatives (10-13). Their structures were extensively characterized using 1D and 2D NMR, modified Mosher's method, HR-ESIMS, NMR and ECD calculations, and X-ray crystallography. Notably, amphicordin C (3) possesses a unique benzo[g]chromene (6/6/6) skeleton in this meroterpenoid family. In an anti-glioma assay, oxirapentyn A (7) effectively inhibited the proliferation, migration, and invasion of glioma cells and induced their apoptosis. Furthermore, an in silico analysis suggested that oxirapentyn A has the potential to penetrate the blood-brain barrier. These findings highlight the potential of oxirapentyn A as a candidate for the development of novel anti-glioma drugs.
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Affiliation(s)
- Minghua Jiang
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
- Southern Marine Sciences and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
| | - Qilin Wu
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
| | - Heng Guo
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
| | - Xin Lu
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
| | - Shuihao Chen
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
| | - Lan Liu
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
- Southern Marine Sciences and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
- Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Zhuhai 519000, China
| | - Senhua Chen
- School of Marine Sciences, Sun Yat-sen University, Zhuhai 519000, China
- Southern Marine Sciences and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
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7
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Sabry SA, Abd El Razek AM, Nabil M, Khedr SM, El-Nahas HM, Eissa NG. Brain-targeted delivery of Valsartan using solid lipid nanoparticles labeled with Rhodamine B; a promising technique for mitigating the negative effects of stroke. Drug Deliv 2023; 30:2179127. [PMID: 36794404 PMCID: PMC10003139 DOI: 10.1080/10717544.2023.2179127] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
The brain is a vital organ that is protected from the general circulation and is distinguished by the presence of a relatively impermeable blood brain barrier (BBB). Blood brain barrier prevents the entry of foreign molecules. The current research aims to transport valsartan (Val) across BBB utilizing solid lipid nanoparticles (SLNs) approach to mitigate the adverse effects of stroke. Using a 32-factorial design, we could investigate and optimize the effect of several variables in order to improve brain permeability of valsartan in a target-specific and sustained-release manner, which led to alleviation of ischemia-induced brain damage. The impact of each of the following independent variables was investigated: lipid concentration (% w/v), surfactant concentration (% w/v), and homogenization speed (RPM) on particle size, zeta potential (ZP), entrapment efficiency (EE) %, and cumulative drug release percentage (CDR) %. TEM images revealed a spherical form of the optimized nanoparticles, with particle size (215.76 ± 7.63 nm), PDI (0.311 ± 0.02), ZP (-15.26 ± 0.58 mV), EE (59.45 ± 0.88%), and CDR (87.59 ± 1.67%) for 72 hours. SLNs formulations showed sustained drug release, which could effectively reduce the dose frequency and improve patient compliance. DSC and X-ray emphasize that Val was encapsulated in the amorphous form. The in-vivo results revealed that the optimized formula successfully delivered Val to the brain through intranasal rout as compared to a pure Val solution and evidenced by the photon imaging and florescence intensity quantification. In a conclusion, the optimized SLN formula (F9) could be a promising therapy for delivering Val to brain, alleviating the negative consequences associated with stroke.
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Affiliation(s)
- Shereen A Sabry
- Department of Pharmaceutics, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Amal M Abd El Razek
- Department of Pharmaceutics, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Mohamed Nabil
- Pharmacology Department, Faculty of Pharmacy, New Valley University, Kharga, Egypt
| | - Shaimaa M Khedr
- Pharmaceutical and Fermentation Industries Development Centre (PFIDC), City of Scientific Research and Technological Applications (SRTA-City), New Borg El-Arab, Alexandria, Egypt
| | - Hanan M El-Nahas
- Department of Pharmaceutics, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Noura G Eissa
- Department of Pharmaceutics, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt.,Science Academy, Badr University in Cairo, Badr City, Cairo, Egypt
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8
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Shaker B, Lee J, Lee Y, Yu MS, Lee HM, Lee E, Kang HC, Oh KS, Kim HW, Na D. A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds. Bioinformatics 2023; 39:btad577. [PMID: 37713469 PMCID: PMC10560102 DOI: 10.1093/bioinformatics/btad577] [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: 05/09/2023] [Revised: 08/30/2023] [Accepted: 09/14/2023] [Indexed: 09/17/2023] Open
Abstract
MOTIVATION Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.
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Affiliation(s)
- Bilal Shaker
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Jingyu Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Yunhyeok Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Myeong-Sang Yu
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Eunee Lee
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, Epilepsy Research Institute, Seoul 03722, Republic of Korea
| | - Hoon-Chul Kang
- Department of Anatomy College of Medicine, Yonsei University, Seoul 03722, Republic of Korea
| | - Kwang-Seok Oh
- Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
| | - Hyung Wook Kim
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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Jang WD, Jang J, Song JS, Ahn S, Oh KS. PredPS: Attention-based graph neural network for predicting stability of compounds in human plasma. Comput Struct Biotechnol J 2023; 21:3532-3539. [PMID: 37484492 PMCID: PMC10362732 DOI: 10.1016/j.csbj.2023.07.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/02/2023] [Accepted: 07/05/2023] [Indexed: 07/25/2023] Open
Abstract
Stability of compounds in the human plasma is crucial for maintaining sufficient systemic drug exposure and considered an essential factor in the early stages of drug discovery and development. The rapid degradation of compounds in the plasma can result in poor in vivo efficacy. Currently, there are no open-source software programs for predicting human plasma stability. In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and open-source datasets. The PredPS outperformed the two machine learning and two deep learning algorithms that were used for comparison indicating its stability-predicting efficiency. PredPS achieved an area under the receiver operating characteristic curve of 90.1%, accuracy of 83.5%, sensitivity of 82.3%, and specificity of 84.6% when evaluated using 5-fold cross-validation. In the early stages of drug discovery, PredPS could be a helpful method for predicting the human plasma stability of compounds. Saving time and money can be accomplished by adopting an in silico-based plasma stability prediction model at the high-throughput screening stage. The source code for PredPS is available at https://bitbucket.org/krict-ai/predps and the PredPS web server is available at https://predps.netlify.app.
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Affiliation(s)
- Woo Dae Jang
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
| | - Jidon Jang
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
| | - Jin Sook Song
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
| | - Sunjoo Ahn
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
- Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, Daejeon 34129, Republic of Korea
| | - Kwang-Seok Oh
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
- Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, Daejeon 34129, Republic of Korea
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10
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Lin Y, Sun M, Zhang J, Li M, Yang K, Wu C, Zulfiqar H, Lai H. Computational identification of promoters in Klebsiella aerogenes by using support vector machine. Front Microbiol 2023; 14:1200678. [PMID: 37250059 PMCID: PMC10215528 DOI: 10.3389/fmicb.2023.1200678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/18/2023] [Indexed: 05/31/2023] Open
Abstract
Promoters are the basic functional cis-elements to which RNA polymerase binds to initiate the process of gene transcription. Comprehensive understanding gene expression and regulation depends on the precise identification of promoters, as they are the most important component of gene expression. This study aimed to develop a machine learning-based model to predict promoters in Klebsiella aerogenes (K. aerogenes). In the prediction model, the promoter sequences in K. aerogenes genome were encoded by pseudo k-tuple nucleotide composition (PseKNC) and position-correlation scoring function (PCSF). Numerical features were obtained and then optimized using mRMR by combining with support vector machine (SVM) and 5-fold cross-validation (CV). Subsequently, these optimized features were inputted into SVM-based classifier to discriminate promoter sequences from non-promoter sequences in K. aerogenes. Results of 10-fold CV showed that the model could yield the overall accuracy of 96.0% and the area under the ROC curve (AUC) of 0.990. We hope that this model will provide help for the study of promoter and gene regulation in K. aerogenes.
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Affiliation(s)
- Yan Lin
- Key Laboratory for Animal Disease-Resistance Nutrition of the Ministry of Agriculture, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, China
| | - Meili Sun
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Junjie Zhang
- Key Laboratory for Animal Disease-Resistance Nutrition of the Ministry of Agriculture, Animal Nutrition Institute, Sichuan Agricultural University, Chengdu, China
| | - Mingyan Li
- Chifeng Product Quality Inspection and Testing Centre, Chifeng, China
| | - Keli Yang
- Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji, China
| | - Chengyan Wu
- Baotou Teacher’s College, Inner Mongolia University of Science and Technology, Baotou, China
| | - Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China
| | - Hongyan Lai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
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11
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Zhang GZ, Gao YL. BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks. Comput Biol Chem 2023; 103:107833. [PMID: 36812824 DOI: 10.1016/j.compbiolchem.2023.107833] [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: 08/21/2022] [Revised: 12/29/2022] [Accepted: 02/15/2023] [Indexed: 02/19/2023]
Abstract
Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction.
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Affiliation(s)
- Guo-Zheng Zhang
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Ying-Lian Gao
- Qufu Normal University Library, Qufu Normal University, Rizhao, China.
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12
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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:691-702. [PMID: 36923950 PMCID: PMC10009646 DOI: 10.1016/j.omtn.2023.02.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.
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Affiliation(s)
- Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sanyin Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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13
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Dorahy G, Chen JZ, Balle T. Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs. Molecules 2023; 28:molecules28031324. [PMID: 36770990 PMCID: PMC9921936 DOI: 10.3390/molecules28031324] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.
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Affiliation(s)
- Georgia Dorahy
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Jake Zheng Chen
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Thomas Balle
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
- Correspondence:
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14
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Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction. Int J Mol Sci 2023; 24:ijms24031815. [PMID: 36768139 PMCID: PMC9915725 DOI: 10.3390/ijms24031815] [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: 12/14/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Drug distribution is an important process in pharmacokinetics because it has the potential to influence both the amount of medicine reaching the active sites and the effectiveness as well as safety of the drug. The main causes of 90% of drug failures in clinical development are lack of efficacy and uncontrolled toxicity. In recent years, several advances and promising developments in drug distribution property prediction have been achieved, especially in silico, which helped to drastically reduce the time and expense of screening undesired drug candidates. In this study, we provide comprehensive knowledge of drug distribution background, influencing factors, and artificial intelligence-based distribution property prediction models from 2019 to the present. Additionally, we gathered and analyzed public databases and datasets commonly utilized by the scientific community for distribution prediction. The distribution property prediction performance of five large ADMET prediction tools is mentioned as a benchmark for future research. On this basis, we also offer future challenges in drug distribution prediction and research directions. We hope that this review will provide researchers with helpful insight into distribution prediction, thus facilitating the development of innovative approaches for drug discovery.
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15
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Wei C, Xiang X, Zhou X, Ren S, Zhou Q, Dong W, Lin H, Wang S, Zhang Y, Lin H, He Q, Lu Y, Jiang X, Shuai J, Jin X, Xie C. Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients. Front Microbiol 2023; 13:1090770. [PMID: 36713206 PMCID: PMC9877536 DOI: 10.3389/fmicb.2022.1090770] [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: 11/06/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Background Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients. Methods The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models. Results The radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only. Conclusion We successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.
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Affiliation(s)
- Chaoyi Wei
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xinli Xiang
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xiaobo Zhou
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Siyan Ren
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qingyu Zhou
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Wenjun Dong
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Haizhen Lin
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Saijun Wang
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yuyue Zhang
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Hai Lin
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Qingzu He
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Yuer Lu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xiaoming Jiang
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xiance Jin
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China,School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang Province, China,*Correspondence: Xiance Jin, ✉
| | - Congying Xie
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China,Congying Xie, ✉
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Wang J, Zhang N, Yuan S, Shang J, Dai L, Li F, Liu J. Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis. BMC Genomics 2022; 23:851. [PMID: 36564711 PMCID: PMC9789616 DOI: 10.1186/s12864-022-09027-0] [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: 09/14/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Abstract
In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.
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Affiliation(s)
- Juan Wang
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
| | - Nana Zhang
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
| | - Shasha Yuan
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
| | - Junliang Shang
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
| | - Lingyun Dai
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
| | - Feng Li
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
| | - Jinxing Liu
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, China
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17
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Wang W, Zhang L, Sun J, Zhao Q, Shuai J. Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field. Brief Bioinform 2022; 23:6775599. [PMID: 36305458 DOI: 10.1093/bib/bbac463] [Citation(s) in RCA: 117] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/10/2022] [Accepted: 09/27/2022] [Indexed: 12/14/2022] Open
Abstract
Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA-miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.
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Affiliation(s)
- Wenya Wang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianwei Shuai
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), and Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, 325001, China.,Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005, China.,National Institute for Data Science in Health and Medicine, and State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, 361005, China
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18
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Chen L, Lin D, Xu H, Li J, Lin L. WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19. Front Microbiol 2022; 13:1040252. [PMID: 36466666 PMCID: PMC9713947 DOI: 10.3389/fmicb.2022.1040252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/06/2022] [Indexed: 11/18/2022] Open
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV) has led to a huge health and economic crises. However, the research required to develop new drugs and vaccines is very expensive in terms of labor, money, and time. Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. Using the previously reported human drug virus database (HDVD), we proposed a model to predict possible drug regimens based on a weighted reconstruction-based linear label propagation algorithm (WLLP). For the drug–virus association matrix, we used the weighted K-nearest known neighbors method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses to obtain the final prediction results. In the framework of 10 times 10-fold cross-validated area under the receiver operating characteristic (ROC) curve (AUC), WLLP exhibited excellent performance with an AUC of 0.8828 ± 0.0037 and an area under the precision-recall curve of 0.5277 ± 0.0053, outperforming the other four models used for comparison. We also predicted effective drug regimens against SARS-CoV-2, and this case study showed that WLLP can be used to suggest potential drugs for the treatment of COVID-19.
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Affiliation(s)
- Langcheng Chen
- Center of Campus Network and Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
| | - Dongying Lin
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Haojie Xu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Jianming Li
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Lieqing Lin
- Center of Campus Network and Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
- *Correspondence: Lieqing Lin
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Chen M, Zhang X, Ju Y, Liu Q, Ding Y. iPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:13829-13850. [PMID: 36654069 DOI: 10.3934/mbe.2022644] [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: 06/17/2023]
Abstract
Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.
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Affiliation(s)
- Mingshuai Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Xin Zhang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China
| | - Qing Liu
- Department of Anesthesiology, Hospital (T.C.M) Affiliated to Southwest Medical University, Luzhou, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
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