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Singh S, Pandey AK, Prajapati VK. From genome to clinic: The power of translational bioinformatics in improving human health. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:1-25. [PMID: 38448133 DOI: 10.1016/bs.apcsb.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.
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Affiliation(s)
- Satyendra Singh
- Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, Ajmer, Rajasthan, India
| | - Anurag Kumar Pandey
- College of Biotechnology, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, Uttar Pradesh, India
| | - Vijay Kumar Prajapati
- Department of Biochemistry, University of Delhi South Campus, Dhaula Kuan, New Delhi, India.
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Lualdi M, Fasano M. Statistical analysis of proteomics data: A review on feature selection. J Proteomics 2019; 198:18-26. [DOI: 10.1016/j.jprot.2018.12.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 11/27/2018] [Accepted: 12/05/2018] [Indexed: 12/19/2022]
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Noleto-Filho EM, Dos Santos Gauy AC, Pennino MG, Gonçalves-de-Freitas E. Bayesian analysis improves experimental studies about temporal patterning of aggression in fish. Behav Processes 2017; 145:18-26. [PMID: 28970036 DOI: 10.1016/j.beproc.2017.09.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 08/16/2017] [Accepted: 09/26/2017] [Indexed: 11/25/2022]
Abstract
This study aims to describe a Bayesian Hierarchical Linear Model (HLM) approach for longitudinal designs in fish's experimental aggressive behavior studies as an alternative to classical methods In particular, we discuss the advantages of Bayesian analysis in dealing with combined variables, non-statistically significant results and required sample size using an experiment of angelfish (Pterophyllum scalare) species as case study. Groups of 3 individuals were subjected to daily observations recorded for 10min during 5days. The frequencies of attacks, displays and the total attacks (attacks+displays) of each record were modeled using Monte Carlo Markov chains. In addition, a Bayesian HLM was performed for measuring the rate of increase/decrease of the aggressive behavior during the time and to assess the probability of difference among days. Results highlighted that using the combined variable of total attacks could lead to biased conclusions as displays and attacks showed an opposite pattern in the experiment. Moreover, depending of the study, this difference in pattern can happen more clearly or more subtly. Subtle changes cannot be detected when p-values are implemented. On the contrary, Bayesian methods provide a clear description of the changes even when patterns are subtle. Additionally, results showed that the number of replicates (15 or 11) invariant the study conclusions as well that using a small sample size could be more evident within the overlapping days, that includes the social rank stability. Therefore, Bayesian analysis seems to be a richer and an adequate statistical approach for fish's aggressive behavior longitudinal designs.
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Affiliation(s)
- Eurico Mesquita Noleto-Filho
- Universidade Estadual Paulista Júlio Mesquita Filho (UNESP/IBILCE), Zoology and Botany Department, R. Cristóvão Colombo, 2265, CEP 15054-000, São José do Rio Preto, SP, Brazil; Aquaculture Center of Sao Paulo State University (CAUNESP), Brazil.
| | - Ana Carolina Dos Santos Gauy
- Universidade Estadual Paulista Júlio Mesquita Filho (UNESP/IBILCE), Zoology and Botany Department, R. Cristóvão Colombo, 2265, CEP 15054-000, São José do Rio Preto, SP, Brazil; Aquaculture Center of Sao Paulo State University (CAUNESP), Brazil.
| | - Maria Grazia Pennino
- Fishing Ecology Management and Economics (FEME), Universidade Federal do Rio Grande do Norte - UFRN. Depto. de Ecologia, Natal, RN, Brazil; Statistical Modeling Ecology Group (SMEG), Departament d'Estadística i Investigació Operativa, Universitat de València, C/Dr. Moliner 50, Burjassot, 46100 Valencia, Spain; Instituto Español de Oceanografía, Centro Oceanográfico de Murcia, C/Varadero 1. San Pedro del Pinatar, 30740, Murcia, Spain.
| | - Eliane Gonçalves-de-Freitas
- Universidade Estadual Paulista Júlio Mesquita Filho (UNESP/IBILCE), Zoology and Botany Department, R. Cristóvão Colombo, 2265, CEP 15054-000, São José do Rio Preto, SP, Brazil; Aquaculture Center of Sao Paulo State University (CAUNESP), Brazil.
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Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW. Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer 2015; 6:54-65. [PMID: 25553089 PMCID: PMC4278915 DOI: 10.7150/jca.10631] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 11/14/2014] [Indexed: 12/12/2022] Open
Abstract
The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.
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Affiliation(s)
- Jinlian Wang
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
- 7. Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiming Zuo
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
- 6. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
| | - Yan-gao Man
- 2. Bon Secours Cancer Institute, Richmond VA, USA
| | | | - Alexander Stojadinovic
- 2. Bon Secours Cancer Institute, Richmond VA, USA
- 3. Division of Surgical Oncology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Meng Liu
- 4. Department of Public Health School of Hunter College, City University of New York, NYC, USA
| | - Xiaowei Yang
- 4. Department of Public Health School of Hunter College, City University of New York, NYC, USA
| | - Rency S. Varghese
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Mahlet G Tadesse
- 5. Department of Mathematics and Statistics, Georgetown University, Washington DC, USA
| | - Habtom W Ressom
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
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Rekhi R, Qutub AA. Systems approaches for synthetic biology: a pathway toward mammalian design. Front Physiol 2013; 4:285. [PMID: 24130532 PMCID: PMC3793170 DOI: 10.3389/fphys.2013.00285] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 09/19/2013] [Indexed: 01/08/2023] Open
Abstract
We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches—stochasticity, complexity, and scale—with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.
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Affiliation(s)
- Rahul Rekhi
- Department of Bioengineering, Rice University Houston, TX, USA
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Sonis ST, Antin JH, Tedaldi MW, Alterovitz G. SNP-based Bayesian networks can predict oral mucositis risk in autologous stem cell transplant recipients. Oral Dis 2013; 19:721-7. [DOI: 10.1111/odi.12146] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Accepted: 05/13/2013] [Indexed: 12/22/2022]
Affiliation(s)
| | - JH Antin
- Dana-Farber Cancer Institute; Boston; MA; USA
| | - MW Tedaldi
- Dana-Farber Cancer Institute; Boston; MA; USA
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Abstract
Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.
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Affiliation(s)
- Dong-Yeon Cho
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland, United States of America
| | - Yoo-Ah Kim
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland, United States of America
| | - Teresa M. Przytycka
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland, United States of America
- * E-mail:
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Koster ES, Raaijmakers JAM, Koppelman GH, Postma DS, van der Ent CK, Koenderman L, Bracke M, Maitland-van der Zee AH. Pharmacogenetics of anti-inflammatory treatment in children with asthma: rationale and design of the PACMAN cohort. Pharmacogenomics 2009; 10:1351-61. [DOI: 10.2217/pgs.09.79] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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