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Song C, Zhang L. Intelligent Design of Antithrombotic Peptide Targeting Collagen. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2024; 40:9661-9668. [PMID: 38664943 DOI: 10.1021/acs.langmuir.4c00543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Binding of blood components to collagen was proved to be a key step in thrombus formation. Intelligent Design of Protein Matcher (IDProMat), a neural network model, was then developed based on the principle of seq2seq to design an antithrombotic peptide targeting collagen. The encoding and decoding of peptide sequence data and the interaction patterns of peptide chains at the interface were studied, and then, IDProMat was applied to the design of peptides to cover collagen. The 99.3% decrease in seq2seq loss and 58.3% decrease in MLP loss demonstrated that IDProMat learned the interaction patterns between residues at the binding interface. An efficient peptide, LRWNSYY, was then designed using this model. Validations on its binding on collagen and its inhibition of platelet adhesion were obtained using docking, MD simulations, and experimental approaches.
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Affiliation(s)
- Changwei Song
- Department of Biochemical Engineering and Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, People's Republic of China
| | - Lin Zhang
- Department of Biochemical Engineering and Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (MOE), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, People's Republic of China
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2
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Toufikuzzaman M, Hassan Samee MA, Sohel Rahman M. CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction. Brief Bioinform 2024; 25:bbad530. [PMID: 38388680 PMCID: PMC10883906 DOI: 10.1093/bib/bbad530] [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: 08/04/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/24/2024] Open
Abstract
CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models' performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall.
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Affiliation(s)
- Md Toufikuzzaman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Md Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - M Sohel Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
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3
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Sui J, Chen J, Chen Y, Iwamori N, Sun J. Identification of plant vacuole proteins by using graph neural network and contact maps. BMC Bioinformatics 2023; 24:357. [PMID: 37740195 PMCID: PMC10517492 DOI: 10.1186/s12859-023-05475-x] [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: 05/13/2023] [Accepted: 09/12/2023] [Indexed: 09/24/2023] Open
Abstract
Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .
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Affiliation(s)
- Jianan Sui
- School of Information Science and Engineering, University of Jinan, Jinan, China
| | - Jiazi Chen
- Laboratory of Zoology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan
| | - Yuehui Chen
- School of Artificial Intelligence Institute and Information Science and Engineering, University of Jinan, Jinan, China.
| | - Naoki Iwamori
- Laboratory of Zoology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan
| | - Jin Sun
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
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4
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Wang H, Sun J, Liu M, Zheng CH, Xia J, Cheng N. frDSM: An Ensemble Predictor With Effective Feature Representation for Deleterious Synonymous Mutation in Human Genome. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:371-377. [PMID: 35420988 DOI: 10.1109/tcbb.2022.3167468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
With the discovery of causality between synonymous mutations and diseases, it has become increasingly important to identify deleterious synonymous mutations for better understanding of their functional mechanisms. Although several machine learning methods have been proposed to solve the task, an effective feature representation method that can make use of the inner difference and relevance between deleterious and benign synonymous mutations is still challenging considering the vast number of synonymous mutations in human genome. In this work, we developed a robust and accurate predictor called frDSM for deleterious synonymous mutation prediction using logistic regression. More specifically, we introduced an effective feature representation learning method which exploits multiple feature descriptors from different perspectives including functional scores obtained from previously computational methods, evolutionary conservation, splicing and sequence feature descriptors, and these features descriptors were input into the 76 XGBoost classifiers to obtain the predictive probabilities values. These probabilities were concatenated to generate the 76-dimension new feature vector, and feature selection method was used to remove redundant and irrelevant features. Experimental results show that frDSM enables robust and accurate prediction than the competing prediction methods with 31 optimal features, which demonstrated the effectiveness of the feature representation learning method. frDSM is freely available at http://frdsm.xialab.info.
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Kliuchnikov E, Klyshko E, Kelly MS, Zhmurov A, Dima RI, Marx KA, Barsegov V. Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules' Growth, Catastrophe, Shortening, and Rescue. Comput Struct Biotechnol J 2022; 20:953-974. [PMID: 35242287 PMCID: PMC8861655 DOI: 10.1016/j.csbj.2022.01.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 12/21/2022] Open
Abstract
Microtubules (MTs), a cellular structure element, exhibit dynamic instability and can switch stochastically from growth to shortening; but the factors that trigger these processes at the molecular level are not understood. We developed a 3D Microtubule Assembly and Disassembly DYnamics (MADDY) model, based upon a bead-per-monomer representation of the αβ-tubulin dimers forming an MT lattice, stabilized by the lateral and longitudinal interactions between tubulin subunits. The model was parameterized against the experimental rates of MT growth and shortening, and pushing forces on the Dam1 protein complex due to protofilaments splaying out. Using the MADDY model, we carried out GPU-accelerated Langevin simulations to access dynamic instability behavior. By applying Machine Learning techniques, we identified the MT characteristics that distinguish simultaneously all four kinetic states: growth, catastrophe, shortening, and rescue. At the cellular 25 μM tubulin concentration, the most important quantities are the MT length L , average longitudinal curvatureκ long , MT tip width w , total energy of longitudinal interactions in MT latticeU long , and the energies of longitudinal and lateral interactions required to complete MT to full cylinderU long add andU lat add . At high 250 μM tubulin concentration, the most important characteristics are L ,κ long , number of hydrolyzed αβ-tubulin dimersn hyd and number of lateral interactions per helical pitchn lat in MT lattice, energy of lateral interactions in MT latticeU lat , and energy of longitudinal interactions in MT tipu long . These results allow greater insights into what brings about kinetic state stability and the transitions between states involved in MT dynamic instability behavior.
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Affiliation(s)
| | - Eugene Klyshko
- Department of Chemistry, University of Massachusetts, Lowell, MA 01854, USA
| | - Maria S. Kelly
- Department of Chemistry, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Artem Zhmurov
- KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ruxandra I. Dima
- Department of Chemistry, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Kenneth A. Marx
- Department of Chemistry, University of Massachusetts, Lowell, MA 01854, USA
| | - Valeri Barsegov
- Department of Chemistry, University of Massachusetts, Lowell, MA 01854, USA
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6
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Wan H, Zhang J, Ding Y, Wang H, Tian G. Immunoglobulin Classification Based on FC* and GC* Features. Front Genet 2022; 12:827161. [PMID: 35140745 PMCID: PMC8819591 DOI: 10.3389/fgene.2021.827161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.
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Affiliation(s)
- Hao Wan
- Institute of Advanced Cross-field Science, College of Life Science, Qingdao University, Qingdao, China
| | - Jina Zhang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Hetian Wang
- Beidahuang Industry Group General Hospital, Harbin, China
- *Correspondence: Hetian Wang, ; Geng Tian,
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China
- *Correspondence: Hetian Wang, ; Geng Tian,
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Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review. SENSORS 2021; 21:s21248485. [PMID: 34960577 PMCID: PMC8703715 DOI: 10.3390/s21248485] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 11/25/2022]
Abstract
Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of ‘3N’ biosignals—nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
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8
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Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis. SENSORS 2021; 21:s21248163. [PMID: 34960257 PMCID: PMC8704049 DOI: 10.3390/s21248163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/28/2021] [Accepted: 12/03/2021] [Indexed: 11/23/2022]
Abstract
The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.
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Madhu P.K. R, Subbaiah J, Krithivasan K. RF‐LSTM‐based method for prediction and diagnosis of fouling in heat exchanger. ASIA-PAC J CHEM ENG 2021. [DOI: 10.1002/apj.2684] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Resma Madhu P.K.
- School of Electrical and Electronics Engineering SASTRA Deemed University Thanjavur India
| | - Jayalalitha Subbaiah
- School of Electrical and Electronics Engineering SASTRA Deemed University Thanjavur India
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10
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Akbari G, Nikkhoo M, Wang L, Chen CPC, Han DS, Lin YH, Chen HB, Cheng CH. Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach. SENSORS 2021; 21:s21124017. [PMID: 34200838 PMCID: PMC8230520 DOI: 10.3390/s21124017] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 11/30/2022]
Abstract
Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.
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Affiliation(s)
- Ghasem Akbari
- Department of Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin 341851416, Iran;
| | - Mohammad Nikkhoo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
- Bone and Joint Research Center, Chang Gung Memorial Hospital, Taoyuan 33333, Taiwan
| | - Lizhen Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;
| | - Carl P. C. Chen
- Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital at Linkou and College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Der-Sheng Han
- Department of Physical Medicine and Rehabilitation, Bei-Hu Branch, National Taiwan University Hospital, Taipei 10845, Taiwan;
| | - Yang-Hua Lin
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (Y.-H.L.); (H.-B.C.)
| | - Hung-Bin Chen
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (Y.-H.L.); (H.-B.C.)
| | - Chih-Hsiu Cheng
- Bone and Joint Research Center, Chang Gung Memorial Hospital, Taoyuan 33333, Taiwan
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan; (Y.-H.L.); (H.-B.C.)
- Correspondence: ; Tel.: +886-3211-8800-3714
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11
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Alam MZ, Masud MM, Rahman MS, Cheratta M, Nayeem MA, Rahman MS. Feature-ranking-based ensemble classifiers for survivability prediction of intensive care unit patients using lab test data. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2020.100495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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12
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Lv Z, Wang P, Zou Q, Jiang Q. Identification of Sub-Golgi protein localization by use of deep representation learning features. Bioinformatics 2020; 36:5600-5609. [PMID: 33367627 PMCID: PMC8023683 DOI: 10.1093/bioinformatics/btaa1074] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/10/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022] Open
Abstract
Motivation The Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology. Results we developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction. Availabilityand implementation A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, 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
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China
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Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.118] [Citation(s) in RCA: 312] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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14
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Zhang D, Guan ZX, Zhang ZM, Li SH, Dao FY, Tang H, Lin H. Recent Development of Computational Predicting Bioluminescent Proteins. Curr Pharm Des 2020; 25:4264-4273. [PMID: 31696804 DOI: 10.2174/1381612825666191107100758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/04/2019] [Indexed: 12/22/2022]
Abstract
Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.
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Affiliation(s)
- Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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15
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Muhammad Rafid AH, Toufikuzzaman M, Rahman MS, Rahman MS. CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning. BMC Bioinformatics 2020; 21:223. [PMID: 32487025 PMCID: PMC7268231 DOI: 10.1186/s12859-020-3531-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/04/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models. RESULTS In this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines). CONCLUSION CRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out of 4 cell lines. We believe that by exploring further, one can design better features only using the sgRNA sequences and can come up with a better method leveraging only traditional machine learning algorithms that can fully beat the deep learning models.
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Affiliation(s)
- Ali Haisam Muhammad Rafid
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Md Toufikuzzaman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Mohammad Saifur Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - M Sohel Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
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16
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Brasil S, Pascoal C, Francisco R, dos Reis Ferreira V, A. Videira P, Valadão G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter? Genes (Basel) 2019; 10:genes10120978. [PMID: 31783696 PMCID: PMC6947640 DOI: 10.3390/genes10120978] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/19/2019] [Accepted: 11/20/2019] [Indexed: 02/06/2023] Open
Abstract
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.
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Affiliation(s)
- Sandra Brasil
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Carlota Pascoal
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Rita Francisco
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Vanessa dos Reis Ferreira
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- Correspondence:
| | - Paula A. Videira
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Gonçalo Valadão
- Instituto de Telecomunicações, 1049-001 Lisboa, Portugal;
- Departamento de Ciências e Tecnologias, Autónoma Techlab–Universidade Autónoma de Lisboa, 1169-023 Lisboa, Portugal
- Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, 1959-007 Lisboa, Portugal
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Lv Z, Jin S, Ding H, Zou Q. A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features. Front Bioeng Biotechnol 2019; 7:215. [PMID: 31552241 PMCID: PMC6737778 DOI: 10.3389/fbioe.2019.00215] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 08/22/2019] [Indexed: 02/01/2023] Open
Abstract
To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.
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Affiliation(s)
- Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Lin J, Chen H, Li S, Liu Y, Li X, Yu B. Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier. Artif Intell Med 2019; 98:35-47. [DOI: 10.1016/j.artmed.2019.07.005] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Revised: 03/03/2019] [Accepted: 07/18/2019] [Indexed: 12/14/2022]
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Zhao W, Li GP, Wang J, Zhou YK, Gao Y, Du PF. Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. J Theor Biol 2019; 473:38-43. [DOI: 10.1016/j.jtbi.2019.04.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/22/2019] [Accepted: 04/29/2019] [Indexed: 12/11/2022]
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Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9102148] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.
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Rahman MS, Rahman MK, Saha S, Kaykobad M, Rahman MS. Antigenic: An improved prediction model of protective antigens. Artif Intell Med 2019; 94:28-41. [DOI: 10.1016/j.artmed.2018.12.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 10/31/2018] [Accepted: 12/28/2018] [Indexed: 10/27/2022]
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Askari E, Setarehdan SK, Sheikhani A, Mohammadi MR, Teshnehlab M. Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis. Artif Intell Med 2018; 89:40-50. [DOI: 10.1016/j.artmed.2018.05.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 05/18/2018] [Accepted: 05/22/2018] [Indexed: 11/26/2022]
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Walczak S. The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijccp.2018070103] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.
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DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC. J Theor Biol 2018; 452:22-34. [PMID: 29753757 DOI: 10.1016/j.jtbi.2018.05.006] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 04/21/2018] [Accepted: 05/04/2018] [Indexed: 11/21/2022]
Abstract
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding. A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.
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