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Ahmad S, Raza K. An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives. J Drug Target 2024; 32:635-646. [PMID: 38662768 DOI: 10.1080/1061186x.2024.2347358] [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: 02/10/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024]
Abstract
There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.
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Affiliation(s)
- Shaban Ahmad
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
| | - Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
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2
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Stephan S, Galland S, Labbani Narsis O, Shoji K, Vachenc S, Gerart S, Nicolle C. Agent-based approaches for biological modeling in oncology: A literature review. Artif Intell Med 2024; 152:102884. [PMID: 38703466 DOI: 10.1016/j.artmed.2024.102884] [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: 07/01/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
CONTEXT Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.
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Affiliation(s)
- Simon Stephan
- UTBM, CIAD UMR 7533, Belfort, F-90010, France; Université de Bourgogne, CIAD UMR 7533, Dijon, F-21000, France.
| | | | | | - Kenji Shoji
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Sébastien Vachenc
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Stéphane Gerart
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
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Liang P, Chen J, Yao L, Hao Z, Chang Q. A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes. Biomedicines 2023; 11:biomedicines11051479. [PMID: 37239150 DOI: 10.3390/biomedicines11051479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes.
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Affiliation(s)
- Pengchen Liang
- Shanghai Key Laboratory of Gastric Neoplasms, Department of Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200020, China
- School of Microelectronics, Shanghai University, Shanghai 201800, China
| | - Jianguo Chen
- School of Software Engineering, Sun Yat-sen University, Zhuhai 528478, China
| | - Lei Yao
- School of Microelectronics, Shanghai University, Shanghai 201800, China
| | - Zezhou Hao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Qing Chang
- Shanghai Key Laboratory of Gastric Neoplasms, Department of Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200020, China
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Thafar MA, Albaradei S, Uludag M, Alshahrani M, Gojobori T, Essack M, Gao X. OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features. Front Genet 2023; 14:1139626. [PMID: 37091791 PMCID: PMC10117673 DOI: 10.3389/fgene.2023.1139626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.
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Affiliation(s)
- Maha A. Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- College of Computers and Information Technology, Computer Science Department, Taif University, Taif, Saudi Arabia
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Mona Alshahrani
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
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Huang RX, Siriwanna D, Cho WC, Wan TK, Du YR, Bennett AN, He QE, Liu JD, Huang XT, Chan KHK. Lung adenocarcinoma-related target gene prediction and drug repositioning. Front Pharmacol 2022; 13:936758. [PMID: 36081949 PMCID: PMC9445420 DOI: 10.3389/fphar.2022.936758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.
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Affiliation(s)
- Rui Xuan Huang
- >Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Damrongrat Siriwanna
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - William C. Cho
- >Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
| | - Tsz Kin Wan
- >Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yan Rong Du
- >Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong, China
| | - Adam N. Bennett
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Qian Echo He
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Jun Dong Liu
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Xiao Tai Huang
- >School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Kei Hang Katie Chan
- >Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
- >Department of Epidemiology and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States
- *Correspondence: Kei Hang Katie Chan,
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Rahman MM, Islam MR, Rahman F, Rahaman MS, Khan MS, Abrar S, Ray TK, Uddin MB, Kali MSK, Dua K, Kamal MA, Chellappan DK. Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance. Bioengineering (Basel) 2022; 9:bioengineering9080335. [PMID: 35892749 PMCID: PMC9332125 DOI: 10.3390/bioengineering9080335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/09/2022] [Accepted: 07/18/2022] [Indexed: 01/07/2023] Open
Abstract
Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
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Affiliation(s)
- Md. Mominur Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Rezaul Islam
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Firoza Rahman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Saidur Rahaman
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Md. Shajib Khan
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Sayedul Abrar
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Tanmay Kumar Ray
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Mohammad Borhan Uddin
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Most. Sumaiya Khatun Kali
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun 248007, India
| | - Mohammad Amjad Kamal
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh; (M.M.R.); (M.R.I.); (F.R.); (M.S.R.); (M.S.K.); (S.A.); (T.K.R.); (M.B.U.); (M.S.K.K.); (M.A.K.)
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Enzymoics, 7 Peterlee Place, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Dinesh Kumar Chellappan
- Department of Life Sciences, School of Pharmacy, International Medical University, Bukit Jalil, Kuala Lumpur 57000, Malaysia
- Correspondence:
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Wang F, Ding Y, Lei X, Liao B, Wu FX. Identifying Gene Signatures for Cancer Drug Repositioning Based on Sample Clustering. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:953-965. [PMID: 32845842 DOI: 10.1109/tcbb.2020.3019781] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Drug repositioning is an important approach for drug discovery. Computational drug repositioning approaches typically use a gene signature to represent a particular disease and connect the gene signature with drug perturbation profiles. Although disease samples, especially from cancer, may be heterogeneous, most existing methods consider them as a homogeneous set to identify differentially expressed genes (DEGs)for further determining a gene signature. As a result, some genes that should be in a gene signature may be averaged off. In this study, we propose a new framework to identify gene signatures for cancer drug repositioning based on sample clustering (GS4CDRSC). GS4CDRSC first groups samples into several clusters based on their gene expression profiles. Second, an existing method is applied to the samples in each cluster for generating a list of DEGs. Then a weighting approach is used to identify an intergrated gene signature from all the lists of DEGs. The integrated gene signature is used to connect with drug perturbation profiles in the Connectivity Map (CMap)database to generate a list of drug candidates. GS4CDRSC has been tested with several cancer datasets and existing methods. The computational results show that GS4CDRSC outperforms those methods without the sample clustering and weighting approaches in terms of both number and rate of predicted known drugs for specific cancers.
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Huang S, Ding Y. Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning.. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180819666220114114752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning.
Objective:
Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning.
Methods:
Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP.
Results:
The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively.
Conclusion:
Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.
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Affiliation(s)
- Songtao Huang
- School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
- Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
| | - Yanrui Ding
- school of Science, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
- Key Laboratory of Industrial Biotechnology, Jiangnan University, Wuxi, Jiangsu, 214122, P.R. China
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Morilla I. Repairing the human with artificial intelligence in oncology. Artif Intell Cancer 2021; 2:60-68. [DOI: 10.35713/aic.v2.i5.60] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale. This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular. In this work, we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer, reinforcement or federated learning. Complementary, we also introduce the recently popular method of topological data analysis that improves the performance of learning models.
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Affiliation(s)
- Ian Morilla
- Laboratoire Analyse, Géométrie et Applications - Institut Galilée, Sorbonne Paris Nord University, Paris 75006, France
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Sadeghi SS, Keyvanpour MR. An Analytical Review of Computational Drug Repurposing. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:472-488. [PMID: 31403439 DOI: 10.1109/tcbb.2019.2933825] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.
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Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach. Cancers (Basel) 2019; 11:cancers11122007. [PMID: 31842486 PMCID: PMC6966646 DOI: 10.3390/cancers11122007] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/01/2019] [Accepted: 12/09/2019] [Indexed: 12/11/2022] Open
Abstract
The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.
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Karuppasamy R, Veerappapillai S, Maiti S, Shin WH, Kihara D. Current progress and future perspectives of polypharmacology : From the view of non-small cell lung cancer. Semin Cancer Biol 2019; 68:84-91. [PMID: 31698087 DOI: 10.1016/j.semcancer.2019.10.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 10/22/2019] [Accepted: 10/28/2019] [Indexed: 12/17/2022]
Abstract
A pre-eminent subtype of lung carcinoma, Non-small cell lung cancer accounts for paramount causes of cancer-associated mortality worldwide. Undeterred by the endeavour in the treatment strategies, the overall cure and survival rates for NSCLC remain substandard, particularly in metastatic diseases. Moreover, the emergence of resistance to classic anticancer drugs further deteriorates the situation. These demanding circumstances culminate the need of extended and revamped research for the establishment of upcoming generation cancer therapeutics. Drug repositioning introduces an affordable and efficient strategy to discover novel drug action, especially when integrated with recent systems biology driven stratagem. This review illustrates the trendsetting approaches in repurposing along with their numerous success stories with an emphasize on the NSCLC therapeutics. Indeed, these novel hits, in combination with conventional anticancer agents, will ideally make their way the clinics and strengthen the therapeutic arsenal to combat drug resistance in the near future.
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Affiliation(s)
- Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
| | - Shanthi Veerappapillai
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sayoni Maiti
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Woong-Hee Shin
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, United States; Department of Chemistry Education, Sunchon National University, Suncheon 57922, Republic of Korea
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907, United States; Department of Computer Science, Purdue University, West Lafayette, IN, 47907, United States; Purdue University, Center for Cancer Research, West Lafayette, IN, 47907, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45229, United States
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13
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Wu G, Zhao T, Kang D, Zhang J, Song Y, Namasivayam V, Kongsted J, Pannecouque C, De Clercq E, Poongavanam V, Liu X, Zhan P. Overview of Recent Strategic Advances in Medicinal Chemistry. J Med Chem 2019; 62:9375-9414. [PMID: 31050421 DOI: 10.1021/acs.jmedchem.9b00359] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introducing novel strategies, concepts, and technologies that speed up drug discovery and the drug development cycle is of great importance both in the highly competitive pharmaceutical industry as well as in academia. This Perspective aims to present a "big-picture" overview of recent strategic innovations in medicinal chemistry and drug discovery.
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Affiliation(s)
- Gaochan Wu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Tong Zhao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Jian Zhang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Yuning Song
- Department of Clinical Pharmacy , Qilu Hospital of Shandong University , 250012 Ji'nan , China
| | - Vigneshwaran Namasivayam
- Pharmaceutical Institute, Pharmaceutical Chemistry II , University of Bonn , 53121 Bonn , Germany
| | - Jacob Kongsted
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Christophe Pannecouque
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Erik De Clercq
- Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy , K.U. Leuven , Herestraat 49 Postbus 1043 (09.A097) , B-3000 Leuven , Belgium
| | - Vasanthanathan Poongavanam
- Department of Physics, Chemistry, and Pharmacy , University of Southern Denmark , DK-5230 Odense M , Denmark
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences , Shandong University , 44 West Culture Road , 250012 Ji'nan , Shandong , P. R. China
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14
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Yu Q, Xu L, Chen L, Sun B, Yang Z, Lu K, Yang Z. Vinculin expression in non-small cell lung cancer. J Int Med Res 2019; 48:300060519839523. [PMID: 30947597 PMCID: PMC7140223 DOI: 10.1177/0300060519839523] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Qiuli Yu
- Department of Respiratory Medicine, the People's Hospital of Rugao, Rugao, Jiangsu, China.,Qiuli Yu and Liqin Xu contributed equally to this work
| | - Liqin Xu
- Department of Respiratory and Critical Care Medicine, the Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.,Qiuli Yu and Liqin Xu contributed equally to this work
| | - Long Chen
- Department of Respiratory Medicine, the People's Hospital of Rugao, Rugao, Jiangsu, China
| | - Baier Sun
- Department of Respiratory and Critical Care Medicine, the Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Zhiyun Yang
- Department of Respiratory Medicine, the People's Hospital of Rugao, Rugao, Jiangsu, China
| | - Kunqin Lu
- Department of Respiratory Medicine, the People's Hospital of Rugao, Rugao, Jiangsu, China
| | - Zhiyong Yang
- Department of Respiratory Medicine, the People's Hospital of Rugao, Rugao, Jiangsu, China
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15
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Belciug S, Gorunescu F. Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection. J Biomed Inform 2018; 83:159-166. [PMID: 29890313 DOI: 10.1016/j.jbi.2018.06.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 06/05/2018] [Accepted: 06/07/2018] [Indexed: 01/06/2023]
Abstract
Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models.
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Affiliation(s)
- Smaranda Belciug
- Department of Computer Science, University of Craiova, Craiova 200585, Romania.
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16
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Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018; 15:20170387. [PMID: 29618526 PMCID: PMC5938574 DOI: 10.1098/rsif.2017.0387] [Citation(s) in RCA: 811] [Impact Index Per Article: 135.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 03/07/2018] [Indexed: 11/12/2022] Open
Abstract
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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Affiliation(s)
- Travers Ching
- Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Daniel S Himmelstein
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brett K Beaulieu-Jones
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Gregory P Way
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Enrico Ferrero
- Computational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, UK
| | | | - Michael Zietz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Wei Xie
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Benjamin J Lengerich
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Johnny Israeli
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Jack Lanchantin
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Stephen Woloszynek
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Avanti Shrikumar
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Evan M Cofer
- Department of Computer Science, Trinity University, San Antonio, TX, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Christopher A Lavender
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Srinivas C Turaga
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - Amr M Alexandari
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - David J Harris
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | | | - Yanjun Qi
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Yifan Peng
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Laura K Wiley
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Marwin H S Segler
- Institute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University in Saint Louis, St Louis, MO, USA
| | - Austin Huang
- Department of Medicine, Brown University, Providence, RI, USA
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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17
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Grammer AC, Lipsky PE. Drug Repositioning Strategies for the Identification of Novel Therapies for Rheumatic Autoimmune Inflammatory Diseases. Rheum Dis Clin North Am 2017; 43:467-480. [DOI: 10.1016/j.rdc.2017.04.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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18
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Yu L, Wang B, Ma X, Gao L. The extraction of drug-disease correlations based on module distance in incomplete human interactome. BMC SYSTEMS BIOLOGY 2016; 10:111. [PMID: 28155709 PMCID: PMC5260043 DOI: 10.1186/s12918-016-0364-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Extracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete. RESULTS We present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results. CONCLUSION The overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China.
| | - Bingbo Wang
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
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