From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transforma...

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Veröffentlicht in:Molecular biotechnology Jg. 67; H. 4; S. 1269 - 1289
Hauptverfasser: Mukherjee, Arnab, Abraham, Suzanna, Singh, Akshita, Balaji, S., Mukunthan, K. S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2025
Springer Nature B.V
Schlagworte:
Algorithms
artificial intelligence
Big Data
Biochemistry
Biological analysis
Biological research
Biological Techniques
biomedical research
Biotechnology
Cell Biology
Chemistry
Chemistry and Materials Science
Computational Biology - methods
Data analysis
data collection
diagnostic techniques
domain
Drug development
Drug discovery
Drug Discovery - methods
drugs
genome
Genomics - methods
Heterogeneity
Human Genetics
Humans
Informatics
landscapes
Machine Learning
Medical research
metabolome
Metabolomics - methods
Molecular Targeted Therapy
Multiomics
Pharmacology
Protein Science
R&D
Research & development
Review Paper
therapeutics
Targeted therapeutics
Big data
Network pharmacology
Multi-omics
Machine learning
ISSN:1073-6085, 1559-0305, 1559-0305
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Abstract In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
AbstractList In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
Author Abraham, Suzanna
Mukherjee, Arnab
Singh, Akshita
Balaji, S.
Mukunthan, K. S.
Author_xml – sequence: 1
  givenname: Arnab
  surname: Mukherjee
  fullname: Mukherjee, Arnab
  organization: Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education
– sequence: 2
  givenname: Suzanna
  surname: Abraham
  fullname: Abraham, Suzanna
  organization: Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education
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  givenname: Akshita
  surname: Singh
  fullname: Singh, Akshita
  organization: Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education
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  givenname: S.
  surname: Balaji
  fullname: Balaji, S.
  organization: Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education
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  givenname: K. S.
  orcidid: 0000-0002-2147-5182
  surname: Mukunthan
  fullname: Mukunthan, K. S.
  email: mukunthan.ks@manipal.edu, mukunthanselvam@gmail.com
  organization: Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38565775$$D View this record in MEDLINE/PubMed
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ISSN 1073-6085
1559-0305
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IsDoiOpenAccess true
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Issue 4
Keywords Targeted therapeutics
Big data
Network pharmacology
Multi-omics
Machine learning
Language English
License 2024. The Author(s).
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SubjectTerms Algorithms
artificial intelligence
Big Data
Biochemistry
Biological analysis
Biological research
Biological Techniques
biomedical research
Biotechnology
Cell Biology
Chemistry
Chemistry and Materials Science
Computational Biology - methods
Data analysis
data collection
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drugs
genome
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Heterogeneity
Human Genetics
Humans
Informatics
landscapes
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metabolome
Metabolomics - methods
Molecular Targeted Therapy
Multiomics
Pharmacology
Protein Science
R&D
Research & development
Review Paper
therapeutics
Title From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies
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https://www.ncbi.nlm.nih.gov/pubmed/38565775
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