1
|
Wu CY, Xu ZX, Li N, Qi DY, Hao ZH, Wu HY, Gao R, Jin YT. Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods. Comput Biol Chem 2024; 113:108207. [PMID: 39265463 DOI: 10.1016/j.compbiolchem.2024.108207] [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: 06/28/2024] [Revised: 08/20/2024] [Accepted: 09/06/2024] [Indexed: 09/14/2024]
Abstract
Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the development of computational methods to accurately identify positive and negative regulation of apoptosis remains a significant challenge. This work proposes a machine learning model based on multi-feature fusion to effectively identify the roles of positive and negative regulation of apoptosis. Initially, we constructed a reliable benchmark dataset containing 200 positive regulation of apoptosis and 241 negative regulation of apoptosis proteins. Subsequently, we developed a classifier that combines the support vector machine (SVM) with pseudo composition of k-spaced amino acid pairs (PseCKSAAP), composition transition distribution (CTD), dipeptide deviation from expected mean (DDE), and PSSM-composition to identify these proteins. Analysis of variance (ANOVA) was employed to select optimized features that could yield the maximum prediction performance. Evaluating the proposed model on independent data revealed and achieved an accuracy of 0.781 with an AUROC of 0.837, demonstrating our model's potent capabilities.
Collapse
Affiliation(s)
- Cheng-Yan Wu
- Key Laboratory of Magnetism and Magnetic Materials at Universities of Inner Mongolia Autonomous Region, Baotou Teacher's College, Baotou 014010, China.
| | - Zhi-Xue Xu
- Key Laboratory of Magnetism and Magnetic Materials at Universities of Inner Mongolia Autonomous Region, Baotou Teacher's College, Baotou 014010, China.
| | - Nan Li
- Key Laboratory of Magnetism and Magnetic Materials at Universities of Inner Mongolia Autonomous Region, Baotou Teacher's College, Baotou 014010, China.
| | - Dan-Yang Qi
- Key Laboratory of Magnetism and Magnetic Materials at Universities of Inner Mongolia Autonomous Region, Baotou Teacher's College, Baotou 014010, China.
| | - Zhi-Hong Hao
- Key Laboratory of Magnetism and Magnetic Materials at Universities of Inner Mongolia Autonomous Region, Baotou Teacher's College, Baotou 014010, China.
| | - Hong-Ye Wu
- Key Laboratory of Magnetism and Magnetic Materials at Universities of Inner Mongolia Autonomous Region, Baotou Teacher's College, Baotou 014010, China.
| | - Ru Gao
- The People's Hospital of Wenjiang, Chengdu, Sichuan 611130, China.
| | - Yan-Ting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| |
Collapse
|
2
|
Li H, Su D, Zhang X, He Y, Luo X, Xiong Y, Zou M, Wei H, Wen S, Xi Q, Zuo Y, Yang L. Machine learning-based prediction of diabetic patients using blood routine data. Methods 2024; 229:156-162. [PMID: 39019099 DOI: 10.1016/j.ymeth.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/23/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024] Open
Abstract
Diabetes stands as one of the most prevalent chronic diseases globally. The conventional methods for diagnosing diabetes are frequently overlooked until individuals manifest noticeable symptoms of the condition. This study aimed to address this gap by collecting comprehensive datasets, including 1000 instances of blood routine data from diabetes patients and an equivalent dataset from healthy individuals. To differentiate diabetes patients from their healthy counterparts, a computational framework was established, encompassing eXtreme Gradient Boosting (XGBoost), random forest, support vector machine, and elastic net algorithms. Notably, the XGBoost model emerged as the most effective, exhibiting superior predictive results with an area under the receiver operating characteristic curve (AUC) of 99.90% in the training set and 98.51% in the testing set. Moreover, the model showcased commendable performance during external validation, achieving an overall accuracy of 81.54%. The probability generated by the model serves as a risk score for diabetes susceptibility. Further interpretability was achieved through the utilization of the Shapley additive explanations (SHAP) algorithm, identifying pivotal indicators such as mean corpuscular hemoglobin concentration (MCHC), lymphocyte ratio (LY%), standard deviation of red blood cell distribution width (RDW-SD), and mean corpuscular hemoglobin (MCH). This enhances our understanding of the predictive mechanisms underlying diabetes. To facilitate the application in clinical and real-life settings, a nomogram was created based on the logistic regression algorithm, which can provide a preliminary assessment of the likelihood of an individual having diabetes. Overall, this research contributes valuable insights into the predictive modeling of diabetes, offering potential applications in clinical practice for more effective and timely diagnoses.
Collapse
Affiliation(s)
- Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xinpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuanyuan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xu Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Min Zou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Huiyan Wei
- Biotechnology Experimental Center, Harbin Medical University, Harbin 150081, China
| | - Shaoran Wen
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Qilemuge Xi
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China.
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| |
Collapse
|
3
|
Zhu H, Hao H, Yu L. Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation. BMC Biol 2024; 22:172. [PMID: 39148051 PMCID: PMC11328394 DOI: 10.1186/s12915-024-01968-0] [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: 04/23/2024] [Accepted: 08/01/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health. Identifying microbes associated with diseases is crucial for early disease diagnosis and advancing precision medicine. RESULTS Considering that the information about changes in microbial quantities under fine-grained disease states helps to enhance a comprehensive understanding of the overall data distribution, this study introduces MSignVGAE, a framework for predicting microbe-disease sign associations using signed message propagation. MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities. A novel strategy for propagating signed message in signed networks addresses heterogeneity and consistency among nodes connected by signed edges. Additionally, we utilize the idea of denoising autoencoder to handle the noise in similarity feature information, which helps overcome biases in the fused similarity data. MSignVGAE represents microbe-disease associations as a heterogeneous graph using similarity information as node features. The multi-class classifier XGBoost is utilized to predict sign associations between diseases and microbes. CONCLUSIONS MSignVGAE achieves AUROC and AUPR values of 0.9742 and 0.9601, respectively. Case studies on three diseases demonstrate that MSignVGAE can effectively capture a comprehensive distribution of associations by leveraging signed information.
Collapse
Affiliation(s)
- Huan Zhu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Hongxia Hao
- School of Computer Science and Technology, Xidian University, Xi'an, China.
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
| |
Collapse
|
4
|
Meng C, Pei Y, Bu Y, Liu Q, Li Q, Zou Q, Zhang Y. IIFS2.0: An Improved Incremental Feature Selection Method for Protein Sequence Processing Based on a Caching Strategy. J Mol Biol 2024:168741. [PMID: 39122168 DOI: 10.1016/j.jmb.2024.168741] [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: 06/05/2024] [Revised: 07/08/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
The purpose of feature selection in protein sequence recognition problems is to select the optimal feature set and use it as training input for classifiers and discover key sequence features of specific proteins. In the feature selection process, relevant features associated with the target task will be retained, and irrelevant and redundant features will be removed. Therefore, in an ideal state, a feature combination with smaller feature dimensions and higher performance indicators is desired. This paper proposes an algorithm called IIFS2.0 based on the cache elimination strategy, which takes the local optimal combination of cached feature subsets as a breakthrough point. It searches for a new feature combination method through the cache elimination strategy to avoid the drawbacks of human factors and excessive reliance on feature sorting results. We validated and analyzed its effectiveness on the protein dataset, demonstrating that IIFS2.0 significantly reduces the dimensionality of feature combinations while also improving various evaluation indicators. In addition, we provide IIFS2.0 on https://112.124.26.17:8006/ for researchers to use.
Collapse
Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, China
| | - Yue Pei
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongbo Bu
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, China
| | - Qing Liu
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, China; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China.
| | - Ying Zhang
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China.
| |
Collapse
|
5
|
Geng G, Wang L, Xu Y, Wang T, Ma W, Duan H, Zhang J, Mao A. MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction. Methods 2024; 228:22-29. [PMID: 38754712 DOI: 10.1016/j.ymeth.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 05/09/2024] [Accepted: 05/12/2024] [Indexed: 05/18/2024] Open
Abstract
Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.
Collapse
Affiliation(s)
- Guannan Geng
- Department of Endocrinology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lizhuang Wang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yanwei Xu
- Beidahuang Group Neuropsychiatric Hospital, Jiamusi, China; Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Tianshuo Wang
- School of Software, Shandong University, Jinan, China
| | - Wei Ma
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Jiahui Zhang
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China.
| | - Anqiong Mao
- The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Department of Anesthesiology, Luzhou, China.
| |
Collapse
|
6
|
Xu J, Yang Y, Zhao J, Li D, Zheng S, Gu J, Wang M. Innovative approaches for coronary heart disease management: integrating biomedical sensors, deep learning, and stellate ganglion modulation. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 39023137 DOI: 10.1080/10255842.2024.2378099] [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: 02/15/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024]
Abstract
Coronary heart disease (CHD) is a significant global health concern, necessitating continuous advancements in treatment modalities to improve patient outcomes. Traditional Chinese medicine (TCM) offers alternative therapeutic approaches, but integration with modern biomedical technologies remains relatively unexplored. This study aimed to assess the efficacy of a combined treatment approach for CHD, integrating traditional Chinese medicinal interventions with modern biomedical sensors and stellate ganglion modulation. The objective was to evaluate the impact of this combined treatment on symptom relief, clinical outcomes, hemorheological indicators, and inflammatory biomarkers. A randomized controlled trial was conducted on 117 CHD patients with phlegm-turbidity congestion and excessiveness type. Patients were divided into a combined treatment group (CTG) and a traditional Chinese medicinal group (CMG). The CTG group received a combination of herbal decoctions, thread-embedding therapy, and stellate ganglion modulation, while the CMG group only received traditional herbal decoctions. The CTG demonstrated superior outcomes compared to the CMG across multiple parameters. Significant reductions in TCM symptom scores, improved clinical effects, reduced angina manifestation, favorable changes in hemorheological indicators, and decreased serum inflammatory biomarkers were observed in the CTG post-intervention. The combination of traditional Chinese medicinal interventions with modern biomedical sensors and stellate ganglion modulation has shown promising results in improving symptoms, clinical outcomes, and inflammatory markers in CHD patients. This holistic approach enhances treatment efficacy and patient outcomes. Further research and advancements in sensor technology are needed to optimize this approach.
Collapse
Affiliation(s)
- Jun Xu
- The People's Hospital of SND, Suzhou, Jiangsu, China
| | - Ying Yang
- The People's Hospital of SND, Suzhou, Jiangsu, China
| | - Jinrong Zhao
- The People's Hospital of SND, Suzhou, Jiangsu, China
| | - Dengke Li
- The Affiliated Traditional Chinese Medicine Hospital of Ningxia Medical University, Wuzhong, Ningxia, China
| | - Shuang Zheng
- Sichuan Nursing Vocational College, Chengdu, Sichuan, China
| | - Jinhui Gu
- Suzhou Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China
| | - Mingming Wang
- The People's Hospital of SND, Suzhou, Jiangsu, China
| |
Collapse
|
7
|
Basith S, Pham NT, Manavalan B, Lee G. SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features. Int J Biol Macromol 2024; 273:133085. [PMID: 38871100 DOI: 10.1016/j.ijbiomac.2024.133085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 05/20/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024]
Abstract
Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens.
Collapse
Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
| | - Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea; Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea.
| |
Collapse
|
8
|
Lin W, Xie X, Luo Z, Chen X, Cao H, Fang X, Song Y, Yuan X, Liu X, Du R. Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning. Arthritis Res Ther 2024; 26:92. [PMID: 38725078 PMCID: PMC11080238 DOI: 10.1186/s13075-024-03330-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVE The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics. METHODS We retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation. RESULTS Among all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5-25%), normal (25-75%), high (75-95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE. CONCLUSION Machine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.
Collapse
Affiliation(s)
- Wenxun Lin
- Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Xie
- Department of Rheumatology and Immunology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China
- Clinical Medical Research Center for Systemic Autoimmune Diseases in Hunan Province, Changsha, Hunan, P.R. China
| | - Zhijun Luo
- Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoqi Chen
- Department of Rheumatology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Heng Cao
- Department of Rheumatology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Xun Fang
- Department of Rheumatology, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, China
| | - You Song
- Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xujing Yuan
- Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaojing Liu
- Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rong Du
- Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| |
Collapse
|
9
|
Chen W, Zhang Y, Wu W, Yang H, Huang W. Machine learning-based predictive model for abdominal diseases using physical examination datasets. Comput Biol Med 2024; 173:108249. [PMID: 38531251 DOI: 10.1016/j.compbiomed.2024.108249] [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: 02/08/2024] [Revised: 02/21/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
Abdominal ultrasound is a key non-invasive imaging method for diagnosing liver, kidney, and gallbladder diseases, despite its clinical significance, not all individuals can undergo abdominal ultrasonography during routine health check-ups due to limitations in equipment, cost, and time. This study aims to use basic physical examination data to predict the risk of diseases of the liver, kidney, and gallbladder that can be diagnosed via abdominal ultrasound. Basic physical examination data contain gender, age, height, weight, BMI, pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, fasting blood glucose (FBG), and uric acid-we established seven single-label predictive models and one multi-label predictive model. These models were specifically designed to predict a range of abdominal diseases. The single-label models, utilizing the XGBoost algorithm, targeted diseases such as fatty liver (with an Area Under the Curve (AUC) of 0.9344), liver deposits (AUC: 0.8221), liver cysts (AUC: 0.7928), gallbladder polyps (AUC: 0.7508), kidney stones (AUC: 0.7853), kidney cysts (AUC: 0.8241), and kidney crystals (AUC: 0.7536). Furthermore, a comprehensive multi-label model, capable of predicting multiple conditions simultaneously, was established by FCN and achieved an AUC of 0.6344. We conducted interpretability analysis on these models to enhance their understanding and applicability in clinical settings. The insights gained from this analysis are crucial for the development of targeted disease prevention strategies. This study represents a significant advancement in utilizing physical examination data to predict ultrasound results, offering a novel approach to early diagnosis and prevention of abdominal diseases.
Collapse
Affiliation(s)
- Wei Chen
- Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China; Four Provincial Marginal Traditional Chinese Medicine Hospitals (Quzhou Traditional Chinese Medicine Hospital) Affiliated to Zhejiang University of Traditional Chinese Medicine, Quzhou, China
| | - YuJie Zhang
- Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China
| | - Weili Wu
- Four Provincial Marginal Traditional Chinese Medicine Hospitals (Quzhou Traditional Chinese Medicine Hospital) Affiliated to Zhejiang University of Traditional Chinese Medicine, Quzhou, China
| | - Hui Yang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
| | - Wenxiu Huang
- Zhejiang Academy of Traditional Chinese Medicine Culture, Zhejiang Chinese Medical University, Hangzhou, China.
| |
Collapse
|
10
|
St. Pierre SR, Kaczmarski B, Peirlinck M, Kuhl E. Sex-specific cardiovascular risk factors in the UK Biobank. Front Physiol 2024; 15:1339866. [PMID: 39165282 PMCID: PMC11333928 DOI: 10.3389/fphys.2024.1339866] [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: 11/16/2023] [Accepted: 02/26/2024] [Indexed: 08/22/2024] Open
Abstract
The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of women compared to that of men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual's risk of developing cardiovascular disease based on the age, sex, cholesterol levels, blood pressure, diabetes status, and the smoking status. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular diseases. The UK Biobank is a large database that includes traditional risk factors and tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here, we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score and quantify the underdiagnosis of women compared to that of men. Strikingly, for a first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2× and 1.4× more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that out of the top 10 features across three sexes and four disease categories, traditional Framingham factors made up between 40% and 50%; electrocardiogram, 30%-33%; pulse wave analysis, 13%-23%; and magnetic resonance imaging and carotid ultrasound, 0%-10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management. Our analysis pipeline and trained classifiers are freely available at https://github.com/LivingMatterLab/CardiovascularDiseaseClassification.
Collapse
Affiliation(s)
- Skyler R. St. Pierre
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Bartosz Kaczmarski
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Mathias Peirlinck
- Department of BioMechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| |
Collapse
|
11
|
Gu ZF, Hao YD, Wang TY, Cai PL, Zhang Y, Deng KJ, Lin H, Lv H. Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur. BMC Biol 2024; 22:86. [PMID: 38637801 PMCID: PMC11027412 DOI: 10.1186/s12915-024-01883-4] [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: 01/03/2024] [Accepted: 04/05/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.
Collapse
Affiliation(s)
- Zhi-Feng Gu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu-Duo Hao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Tian-Yu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Pei-Ling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, 610106, PR China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, PR China
| | - Ke-Jun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Hao Lin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Hao Lv
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| |
Collapse
|
12
|
Zhang ZY, Zhang Z, Ye X, Sakurai T, Lin H. A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens. Int J Biol Macromol 2024; 265:130659. [PMID: 38462114 DOI: 10.1016/j.ijbiomac.2024.130659] [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: 01/11/2024] [Revised: 02/19/2024] [Accepted: 03/04/2024] [Indexed: 03/12/2024]
Abstract
Understanding the subcellular localization of lncRNAs is crucial for comprehending their regulation activities. The conventional detection of lncRNA subcellular location usually uses in situ detection techniques, which are resource intensive. Some machine learning-based algorithms have been proposed for lncRNA subcellular location prediction in mammals. However, due to the low level of conservation of lncRNA sequence, the performance of cross-species models remains unsatisfactory. In this study, we curated a novel dataset containing subcellular location information of lncRNAs in Homo sapiens. Subsequently, based on the BERT pre-trained language algorithm, we developed a model for lncRNA subcellular location prediction. Our model achieved a micro-average area under the receiver operating characteristic (AUROC) of 0.791 on the training set and an AUROC of 0.700 on the testing nucleus set. Additionally, we conducted cross-species validation and motif discovery to further investigate underlying patterns. In summary, our study provides valuable guidance and computational analysis tools for exploring the mechanisms of lncRNA subcellular localization and the dynamic spatial changes of RNA in abnormal physiological states.
Collapse
Affiliation(s)
- Zhao-Yue Zhang
- Tsukuba Life Science Innovation Program, University of Tsukuba, Tsukuba 3058577, Japan
| | - Zheng Zhang
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Hao Lin
- Center for Information Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| |
Collapse
|
13
|
Hu X, Zhang P, Liu D, Zhang J, Zhang Y, Dong Y, Fan Y, Deng L. IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network. Brief Bioinform 2024; 25:bbae179. [PMID: 38647155 PMCID: PMC11033953 DOI: 10.1093/bib/bbae179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/15/2023] [Accepted: 03/27/2024] [Indexed: 04/25/2024] Open
Abstract
Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.
Collapse
Affiliation(s)
- Xiaowen Hu
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Pan Zhang
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, 410078, ChangshaChina
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Jiaxuan Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego, 92093, CA, United States
| | - Yuanpeng Zhang
- School of Software, Xinjiang University, 830046, Urumqi, China
| | - Yihan Dong
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Yanhao Fan
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| |
Collapse
|
14
|
Zhou Z, Xiao C, Yin J, She J, Duan H, Liu C, Fu X, Cui F, Qi Q, Zhang Z. PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning. Comput Biol Med 2024; 171:108129. [PMID: 38342046 DOI: 10.1016/j.compbiomed.2024.108129] [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: 12/19/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/13/2024]
Abstract
DNA N6-methyladenine (6mA) modifications play a pivotal role in the regulation of growth, development, and diseases in organisms. As a significant epigenetic marker, 6mA modifications extensively participate in the intricate regulatory networks of the genome. Hence, gaining a profound understanding of how 6mA is intricately involved in these biological processes is imperative for deciphering the gene regulatory networks within organisms. In this study, we propose PSAC-6mA (Position-self-attention Capsule-6mA), a sequence-location-based self-attention capsule network. The positional layer in the model enables positional relationship extraction and independent parameter setting for each base position, avoiding parameter sharing inherent in convolutional approaches. Simultaneously, the self-attention capsule network enhances dimensionality, capturing correlation information between capsules and achieving exceptional results in feature extraction across multiple spatial dimensions within the model. Experimental results demonstrate the superior performance of PSAC-6mA in recognizing 6mA motifs across various species.
Collapse
Affiliation(s)
- Zheyu Zhou
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Cuilin Xiao
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Jinfen Yin
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Jiayi She
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Hao Duan
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Chunling Liu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Xiuhao Fu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Qi Qi
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China.
| |
Collapse
|
15
|
Zhang HQ, Liu SH, Li R, Yu JW, Ye DX, Yuan SS, Lin H, Huang CB, Tang H. MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier. ACS OMEGA 2024; 9:8439-8447. [PMID: 38405489 PMCID: PMC10882704 DOI: 10.1021/acsomega.3c09587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/27/2024]
Abstract
In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.
Collapse
Affiliation(s)
- Hong-Qi Zhang
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shang-Hua Liu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Rui Li
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Jun-Wen Yu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Dong-Xin Ye
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Hao Lin
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School
of Computer Science and Technology, Aba Teachers University, Aba 623002, China
| | - Hua Tang
- School
of Basic Medical Sciences, Southwest Medical
University, Luzhou 646000, China
- Central
Nervous System Drug Key Laboratory of Sichuan Province, Luzhou 646000, China
| |
Collapse
|
16
|
Niu M, Wang C, Chen Y, Zou Q, Xu L. Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2. Brief Bioinform 2024; 25:bbad537. [PMID: 38279648 PMCID: PMC10818166 DOI: 10.1093/bib/bbad537] [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: 10/16/2023] [Revised: 12/12/2023] [Accepted: 12/22/2023] [Indexed: 01/28/2024] Open
Abstract
Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA.
Collapse
Affiliation(s)
- Mengting Niu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150000, China
| | - Yaojia Chen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No.4 Block 2 North Jianshe Road, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No.4 Block 2 North Jianshe Road, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
| |
Collapse
|
17
|
Momanyi BM, Zhou YW, Grace-Mercure BK, Temesgen SA, Basharat A, Ning L, Tang L, Gao H, Lin H, Tang H. SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations. Curr Res Struct Biol 2023; 7:100122. [PMID: 38188542 PMCID: PMC10771890 DOI: 10.1016/j.crstbi.2023.100122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/30/2023] [Accepted: 12/24/2023] [Indexed: 01/09/2024] Open
Abstract
Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.
Collapse
Affiliation(s)
- Biffon Manyura Momanyi
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Wei Zhou
- School of Health Care Technology, Chengdu Neusoft University, Chengdu, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Sebu Aboma Temesgen
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ahmad Basharat
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lin Ning
- School of Health Care Technology, Chengdu Neusoft University, Chengdu, China
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lixia Tang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hui Gao
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- Basic Medicine Research Innovation Center for Cardiometabolic Diseases, Ministry of Education, Luzhou, 646000, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, 646000, China
| |
Collapse
|
18
|
Ren L, Ning L, Yang Y, Yang T, Li X, Tan S, Ge P, Li S, Luo N, Tao P, Zhang Y. MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19. Comput Biol Med 2023; 167:107661. [PMID: 37925911 DOI: 10.1016/j.compbiomed.2023.107661] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/07/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent. In this study, we developed MetaboliteCOVID, a manually curated database of COVID-19-related metabolite markers. The database currently comprises 665 manually selected entries of significantly altered metabolites associated with early diagnosis, disease severity, prognosis, and drug response in COVID-19, encompassing 337 metabolites. Additionally, the database offers a user-friendly interface, containing abundant information for querying, browsing, and analyzing COVID-19-related abnormal metabolites in different body fluids. In summary, we believe that this database will effectively facilitate research on the functions and mechanisms of COVID-19-related metabolic biomarkers, thereby advancing both basic and clinical research on COVID-19. MetaboliteCOVID is free available at: https://cellknowledge.com.cn/MetaboliteCOVID.
Collapse
Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China; Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Yu Yang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Ting Yang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Xinyu Li
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Shanshan Tan
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Peixin Ge
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Shun Li
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, WenChuan, Sichuan, 623002, China.
| | - Pei Tao
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Sichuan, 611731, China.
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| |
Collapse
|
19
|
Meng C, Yuan Y, Zhao H, Pei Y, Li Z. IIFS: An improved incremental feature selection method for protein sequence processing. Comput Biol Med 2023; 167:107654. [PMID: 37944304 DOI: 10.1016/j.compbiomed.2023.107654] [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: 09/01/2023] [Revised: 10/09/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
MOTIVATION Discrete features can be obtained from protein sequences using a feature extraction method. These features are the basis of downstream processing of protein data, but it is necessary to screen and select some important features from them as they generally have data redundancy. RESULT Here, we report IIFS, an improved incremental feature selection method that exploits a new subset search strategy to find the optimal feature set. IIFS combines nonadjacent sorting features to prevent the drawbacks of data explosion and excessive reliance on feature sorting results. The comparative experimental results on 27 feature sorting data show that IIFS can find more accurate and important features compared to existing methods.The IIFS approach also handles data redundancy more efficiently and finds more representative and discriminatory features while ensuring minimal feature dimensionality and good evaluation metrics. Moreover, we wrap this method and deploy it on a web server for access at http://112.124.26.17:8005/.
Collapse
Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin, 150001, China
| | - Haiyan Zhao
- College of Integration of Traditional Chinese and Western Medicine to Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Yue Pei
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhi Li
- Department of Spleen and Stomach Diseases, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China.
| |
Collapse
|
20
|
Liu B, Yang Z, Liu Q, Zhang Y, Ding H, Lai H, Li Q. Computational prediction of allergenic proteins based on multi-feature fusion. Front Genet 2023; 14:1294159. [PMID: 37928245 PMCID: PMC10622758 DOI: 10.3389/fgene.2023.1294159] [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: 09/15/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.
Collapse
Affiliation(s)
- Bin Liu
- Department of Anesthesiology, The Fourth People’s Hospital of Sichuan Province, Chengdu, Sichuan, China
| | - Ziman Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Liu
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hui Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongyan Lai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
- Research Center of Integrated Traditional Chinese and Western Medicine, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, Sichuan, China
| |
Collapse
|