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Ma R, Yuan H, Wang Y, Wang T, Wang J. Risk factors for hemoglobin decline in gastric cancer patients undergoing postoperative chemotherapy: a retrospective analysis. Am J Transl Res 2024; 16:1701-1710. [PMID: 38883365 PMCID: PMC11170587 DOI: 10.62347/cllz7409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 12/06/2023] [Indexed: 06/18/2024]
Abstract
OBJECTIVE To investigate the independent risk factors for a decreased hemoglobin level in gastric cancer patients undergoing adjuvant chemotherapy. METHODS A retrospective study was conducted on 142 gastric cancer patients who received chemotherapy between May 2017 and May 2021 at the Gansu Provincial Cancer Hospital. All patients were subjected to the same regimen of adjuvant chemotherapy combining platinum/taxane and fluorouracil. The correlation between patients' clinicopathological features and the decreased hemoglobin during adjuvant chemotherapy was analyzed. Logistic and LASSO regression analyses were employed to screen for independent risk factors for decreased hemoglobin during adjuvant chemotherapy. RESULTS Univariate analysis revealed that intraoperative bleeding, pre-chemotherapy anemia, and hypoalbuminemia were risk factors for the decreased hemoglobin in patients during adjuvant chemotherapy (all P < 0.05). Both logistic and LASSO regression analyses corroborated these factors as influential factors in the decrease of hemoglobin (P < 0.05). In addition, both logistic and LASSO regression models demonstrated similar performance in this aspect. The nomogram model was subjected to internal validation, resulting in a C-index of 0.712 (0.629-0.796). The calibration curves exhibited satisfactory alignment with the ideal curve. CONCLUSION Intraoperative blood loss, pre-chemotherapy anemia, and hypoalbuminemia are independent risk factors for hemoglobin reduction following chemotherapy. Moreover, both the logistic and LASSO regression models exhibited equivalent performance in this context. These findings bear substantial clinical implications, aiding physicians in the management of anemia in patients undergoing chemotherapy.
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Affiliation(s)
- Ruofei Ma
- Department of Gastric Tumor Surgery, Gansu Provincial Cancer Hospital, Gansu Provincial Academic Institute for Medical Research No. 2 Xiaoxihu East Street, Qilihe District, Lanzhou 730050, Gansu, China
| | - Huqin Yuan
- Department of Gastric Tumor Surgery, Gansu Provincial Cancer Hospital, Gansu Provincial Academic Institute for Medical Research No. 2 Xiaoxihu East Street, Qilihe District, Lanzhou 730050, Gansu, China
| | - Yaling Wang
- Department of Respiratory Oncology, Gansu Provincial Cancer Hospital, Gansu Provincial Academic Institute for Medical Research No. 2 Xiaoxihu East Street, Qilihe District, Lanzhou 730050, Gansu, China
| | - Tao Wang
- Department of Gastric Tumor Surgery, Gansu Provincial Cancer Hospital, Gansu Provincial Academic Institute for Medical Research No. 2 Xiaoxihu East Street, Qilihe District, Lanzhou 730050, Gansu, China
| | - Jijun Wang
- Department of Gastric Tumor Surgery, Gansu Provincial Cancer Hospital, Gansu Provincial Academic Institute for Medical Research No. 2 Xiaoxihu East Street, Qilihe District, Lanzhou 730050, Gansu, China
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Kang WY, Gao YL, Wang Y, Li F, Liu JX. KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder. IEEE J Biomed Health Inform 2024; 28:3178-3185. [PMID: 38408006 DOI: 10.1109/jbhi.2024.3369650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep Auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.
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Bhattacharjee S, Saha B, Saha S. Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques. Comput Biol Med 2024; 174:108413. [PMID: 38608323 DOI: 10.1016/j.compbiomed.2024.108413] [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: 09/27/2023] [Revised: 02/13/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND AND OBJECTIVES Lifestyle-related diseases (LSDs) impose a substantial economic burden on patients and health care services. LSDs are chronic in nature and can directly affect the heart and lungs. Therapeutic interventions only based on symptoms can be crucial for prompt treatment initiation in LSDs, as symptoms are the first information available to clinicians. So, this work aims to apply unsupervised machine learning (ML) techniques for developing models to predict drugs from symptoms for LSDs, with a specific focus on pulmonary and heart diseases. METHODS The drug-disease and disease-symptom associations of 143 LSDs, 1271 drugs, and 305 symptoms were used to compute direct associations between drugs and symptoms. ML models with four different algorithms - K-Means, Bisecting K-Means, Mean Shift, and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) - were developed to cluster the drugs using symptoms as features. The optimal model was saved in a server for the development of a web application. A web application was developed to perform the prediction based on the optimal model. RESULTS The Bisecting K-means model showed the best performance with a silhouette coefficient of 0.647 and generated 138 drug clusters. The drugs within the optimal clusters showed good similarity based on i) gene ontology annotations of the gene targets, ii) chemical ontology annotations, and iii) maximum common substructure of the drugs. In the web application, the model also provides a confidence score for each predicted drug while predicting from a new set of input symptoms. CONCLUSION In summary, direct associations between drugs and symptoms were computed, and those were used to develop a symptom-based drug prediction tool for LSDs with unsupervised ML models. The ML-based prediction can provide a second opinion to clinicians to aid their decision-making for early treatment of LSD patients. The web application (URL - http://bicresources.jcbose.ac.in/ssaha4/sdldpred) can provide a simple interface for all end-users to perform the ML-based prediction.
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Affiliation(s)
- Sudipto Bhattacharjee
- Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India.
| | - Banani Saha
- Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India.
| | - Sudipto Saha
- Department of Biological Sciences, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata, 700091, India.
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Jiang H, Huang Y, Li Q, Feng B. ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering. BMC Bioinformatics 2023; 24:417. [PMID: 37932672 PMCID: PMC10629177 DOI: 10.1186/s12859-023-05494-8] [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: 03/30/2023] [Accepted: 09/21/2023] [Indexed: 11/08/2023] Open
Abstract
MOTIVATION Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes. RESULT To achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.
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Affiliation(s)
- Hanjing Jiang
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Institute of Artificial Intelligence, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yabing Huang
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Qianpeng Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Boyuan Feng
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Institute of Artificial Intelligence, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
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Yang M, Yang B, Duan G, Wang J. ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis. Front Genet 2023; 14:1271311. [PMID: 37795241 PMCID: PMC10545866 DOI: 10.3389/fgene.2023.1271311] [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: 08/02/2023] [Accepted: 08/23/2023] [Indexed: 10/06/2023] Open
Abstract
Background: Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications. Methods: In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug-disease associations. First, we use a weighted k-nearest neighbor (WKNN) approach to increase the overall density of the drug-disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug-disease association pairs derived from the low-rank drug and low-rank disease tensors. Results: We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method.
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Affiliation(s)
- Mengyun Yang
- School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China
- School of Computer Science, Hunan First Normal University, Changsha, China
| | - Bin Yang
- School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
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Wang Y, Liu JX, Wang J, Shang J, Gao YL. A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations. J Comput Biol 2023; 30:937-947. [PMID: 37486669 DOI: 10.1089/cmb.2023.0078] [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] [Indexed: 07/25/2023] Open
Abstract
Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous network, nodes features were extracted from two perspectives: network topology and biological knowledge. Finally, the GRLGB classifier was applied to predict potential DDAs. GRLGB achieved satisfactory results on Bdataset and Fdataset through 10-fold cross-validation. To further prove the reliability of the GRLGB, case studies involving anxiety disorders and clozapine were conducted. The results suggest that GRLGB can identify novel DDAs.
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Affiliation(s)
- Ying Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Juan Wang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, Shandong, China
| | - Ying-Lian Gao
- Qufu Normal University Library, Qufu Normal University, Rizhao, Shandong, China
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Chen L, Chen K, Zhou B. Inferring drug-disease associations by a deep analysis on drug and disease networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14136-14157. [PMID: 37679129 DOI: 10.3934/mbe.2023632] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Kaiyu Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Bo Zhou
- Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
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Taguchi YH, Turki T. Integrated Analysis of Tissue-Specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases. Genes (Basel) 2022; 13:1097. [PMID: 35741859 PMCID: PMC9222230 DOI: 10.3390/genes13061097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 06/14/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
In the field of gene expression analysis, methods of integrating multiple gene expression profiles are still being developed and the existing methods have scope for improvement. The previously proposed tensor decomposition-based unsupervised feature extraction method was improved by introducing standard deviation optimization. The improved method was applied to perform an integrated analysis of three tissue-specific gene expression profiles (namely, adipose, muscle, and liver) for diabetes mellitus, and the results showed that it can detect diseases that are associated with diabetes (e.g., neurodegenerative diseases) but that cannot be predicted by individual tissue expression analyses using state-of-the-art methods. Although the selected genes differed from those identified by the individual tissue analyses, the selected genes are known to be expressed in all three tissues. Thus, compared with individual tissue analyses, an integrated analysis can provide more in-depth data and identify additional factors, namely, the association with other diseases.
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Affiliation(s)
- Y-H. Taguchi
- Department of Physics, Chuo University, Tokyo 112-8551, Japan
| | - Turki Turki
- Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
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Rintala TJ, Ghosh A, Fortino V. Network approaches for modeling the effect of drugs and diseases. Brief Bioinform 2022; 23:6608969. [PMID: 35704883 PMCID: PMC9294412 DOI: 10.1093/bib/bbac229] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/29/2022] [Accepted: 05/17/2021] [Indexed: 12/12/2022] Open
Abstract
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
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Affiliation(s)
- T J Rintala
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - Arindam Ghosh
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
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