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AlSaieedi A, Salhi A, Tifratene F, Raies AB, Hungler A, Uludag M, Van Neste C, Bajic VB, Gojobori T, Essack M. DES-Tcell is a knowledgebase for exploring immunology-related literature. Sci Rep 2021; 11:14344. [PMID: 34253812 PMCID: PMC8275784 DOI: 10.1038/s41598-021-93809-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/24/2021] [Indexed: 12/02/2022] Open
Abstract
T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave’s disease (GD) and Hashimoto’s thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.
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Affiliation(s)
- Ahdab AlSaieedi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences (FAMS), King Abdulaziz University (KAU), Jeddah, 21589-80324, Saudi Arabia
| | - Adil Salhi
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Faroug Tifratene
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Arwa Bin Raies
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Arnaud Hungler
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Christophe Van Neste
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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Ljubic B, Pavlovski M, Roychoudhury S, Van Neste C, Salhi A, Essack M, Bajic VB, Obradovic Z. Genes and comorbidities of thyroid cancer. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2020; 2020:5904315. [PMID: 32308806 PMCID: PMC7142358 DOI: 10.1155/2020/5904315] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 02/21/2020] [Indexed: 12/27/2022]
Abstract
Normal cellular physiology and biochemical processes require undamaged RNA molecules. However, RNAs are frequently subjected to oxidative damage. Overproduction of reactive oxygen species (ROS) leads to RNA oxidation and disturbs redox (oxidation-reduction reaction) homeostasis. When oxidation damage affects RNA carrying protein-coding information, this may result in the synthesis of aberrant proteins as well as a lower efficiency of translation. Both of these, as well as imbalanced redox homeostasis, may lead to numerous human diseases. The number of studies on the effects of RNA oxidative damage in mammals is increasing by year due to the understanding that this oxidation fundamentally leads to numerous human diseases. To enable researchers in this field to explore information relevant to RNA oxidation and effects on human diseases, we developed DES-ROD, an online knowledgebase that contains processed information from 298,603 relevant documents that consist of PubMed abstracts and PubMed Central full-text articles. The system utilizes concepts/terms from 38 curated thematic dictionaries mapped to the analyzed documents. Researchers can explore enriched concepts, as well as enriched pairs of putatively associated concepts. In this way, one can explore mutual relationships between any combinations of two concepts from used dictionaries. Dictionaries cover a wide range of biomedical topics, such as human genes and proteins, pathways, Gene Ontology categories, mutations, noncoding RNAs, enzymes, toxins, metabolites, and diseases. This makes insights into different facets of the effects of RNA oxidation and the control of this process possible. The usefulness of the DES-ROD system is demonstrated by case studies on some known information, as well as potentially novel information involving RNA oxidation and diseases. DES-ROD is the first knowledgebase based on text and data mining that focused on the exploration of RNA oxidation and human diseases.
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Obradovic M, Essack M, Zafirovic S, Sudar‐Milovanovic E, Bajic VP, Van Neste C, Trpkovic A, Stanimirovic J, Bajic VB, Isenovic ER. Redox control of vascular biology. Biofactors 2020; 46:246-262. [PMID: 31483915 PMCID: PMC7187163 DOI: 10.1002/biof.1559] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 08/14/2019] [Indexed: 12/12/2022]
Abstract
Redox control is lost when the antioxidant defense system cannot remove abnormally high concentrations of signaling molecules, such as reactive oxygen species (ROS). Chronically elevated levels of ROS cause oxidative stress that may eventually lead to cancer and cardiovascular and neurodegenerative diseases. In this review, we focus on redox effects in the vascular system. We pay close attention to the subcompartments of the vascular system (endothelium, smooth muscle cell layer) and give an overview of how redox changes influence those different compartments. We also review the core aspects of redox biology, cardiovascular physiology, and pathophysiology. Moreover, the topic-specific knowledgebase DES-RedoxVasc was used to develop two case studies, one focused on endothelial cells and the other on the vascular smooth muscle cells, as a starting point to possibly extend our knowledge of redox control in vascular biology.
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Affiliation(s)
- Milan Obradovic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
| | - Magbubah Essack
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)ThuwalKingdom of Saudi Arabia
| | - Sonja Zafirovic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
| | - Emina Sudar‐Milovanovic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
| | - Vladan P. Bajic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
| | - Christophe Van Neste
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)ThuwalKingdom of Saudi Arabia
| | - Andreja Trpkovic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
| | - Julijana Stanimirovic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
| | - Vladimir B. Bajic
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)ThuwalKingdom of Saudi Arabia
| | - Esma R. Isenovic
- Laboratory of Radiobiology and Molecular GeneticsVinca Institute of Nuclear Sciences, University of BelgradeBelgradeSerbia
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Essack M, Salhi A, Stanimirovic J, Tifratene F, Bin Raies A, Hungler A, Uludag M, Van Neste C, Trpkovic A, Bajic VP, Bajic VB, Isenovic ER. Literature-Based Enrichment Insights into Redox Control of Vascular Biology. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2019; 2019:1769437. [PMID: 31223421 PMCID: PMC6542245 DOI: 10.1155/2019/1769437] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 04/11/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
In cellular physiology and signaling, reactive oxygen species (ROS) play one of the most critical roles. ROS overproduction leads to cellular oxidative stress. This may lead to an irrecoverable imbalance of redox (oxidation-reduction reaction) function that deregulates redox homeostasis, which itself could lead to several diseases including neurodegenerative disease, cardiovascular disease, and cancers. In this study, we focus on the redox effects related to vascular systems in mammals. To support research in this domain, we developed an online knowledge base, DES-RedoxVasc, which enables exploration of information contained in the biomedical scientific literature. The DES-RedoxVasc system analyzed 233399 documents consisting of PubMed abstracts and PubMed Central full-text articles related to different aspects of redox biology in vascular systems. It allows researchers to explore enriched concepts from 28 curated thematic dictionaries, as well as literature-derived potential associations of pairs of such enriched concepts, where associations themselves are statistically enriched. For example, the system allows exploration of associations of pathways, diseases, mutations, genes/proteins, miRNAs, long ncRNAs, toxins, drugs, biological processes, molecular functions, etc. that allow for insights about different aspects of redox effects and control of processes related to the vascular system. Moreover, we deliver case studies about some existing or possibly novel knowledge regarding redox of vascular biology demonstrating the usefulness of DES-RedoxVasc. DES-RedoxVasc is the first compiled knowledge base using text mining for the exploration of this topic.
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Affiliation(s)
- Magbubah Essack
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Adil Salhi
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Julijana Stanimirovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Faroug Tifratene
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Arwa Bin Raies
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Arnaud Hungler
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Mahmut Uludag
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Christophe Van Neste
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Andreja Trpkovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Vladan P. Bajic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Vladimir B. Bajic
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Esma R. Isenovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
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Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 2019; 166:4-21. [PMID: 31022451 DOI: 10.1016/j.ymeth.2019.04.008] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 03/23/2019] [Accepted: 04/15/2019] [Indexed: 12/13/2022] Open
Abstract
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.
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