From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918270138843136 |
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| author | Wang, Tianyang Chen, Silin Wang, Yunze Zhang, Yichao Song, Xinyuan Bi, Ziqian Liu, Ming Niu, Qian Liu, Junyu Feng, Pohsun Sun, Xintian Zhang, Charles Chen, Keyu Li, Ming Fei, Cheng Yan, Lawrence KQ Bao, Riyang Qin, Ziyuan Jiang, Chong Jiang, Zekun Peng, Benji |
| author_facet | Wang, Tianyang Chen, Silin Wang, Yunze Zhang, Yichao Song, Xinyuan Bi, Ziqian Liu, Ming Niu, Qian Liu, Junyu Feng, Pohsun Sun, Xintian Zhang, Charles Chen, Keyu Li, Ming Fei, Cheng Yan, Lawrence KQ Bao, Riyang Qin, Ziyuan Jiang, Chong Jiang, Zekun Peng, Benji |
| contents | The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimental approaches to ensure their biological relevance. This review explores the various methods and technologies used for experimental validation, including gene expression analysis, protein-protein interaction verification, and pathway validation. We also discuss the challenges involved in translating computational predictions to experimental settings and highlight the importance of collaboration between bioinformatics and experimental research. Finally, emerging technologies, such as CRISPR gene editing, next-generation sequencing, and artificial intelligence, are shaping the future of bioinformatics validation and driving more accurate and efficient biological discoveries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings Wang, Tianyang Chen, Silin Wang, Yunze Zhang, Yichao Song, Xinyuan Bi, Ziqian Liu, Ming Niu, Qian Liu, Junyu Feng, Pohsun Sun, Xintian Zhang, Charles Chen, Keyu Li, Ming Fei, Cheng Yan, Lawrence KQ Bao, Riyang Qin, Ziyuan Jiang, Chong Jiang, Zekun Peng, Benji Genomics Computational Engineering, Finance, and Science The integration of bioinformatics predictions and experimental validation plays a pivotal role in advancing biological research, from understanding molecular mechanisms to developing therapeutic strategies. Bioinformatics tools and methods offer powerful means for predicting gene functions, protein interactions, and regulatory networks, but these predictions must be validated through experimental approaches to ensure their biological relevance. This review explores the various methods and technologies used for experimental validation, including gene expression analysis, protein-protein interaction verification, and pathway validation. We also discuss the challenges involved in translating computational predictions to experimental settings and highlight the importance of collaboration between bioinformatics and experimental research. Finally, emerging technologies, such as CRISPR gene editing, next-generation sequencing, and artificial intelligence, are shaping the future of bioinformatics validation and driving more accurate and efficient biological discoveries. |
| title | From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings |
| topic | Genomics Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2502.03478 |