I intend to explore the integration of computational biology and artificial intelligence (AI) with laboratory and experimental work, encompassing animal models, cell culture, clinical trials, and molecular studies. As a clinical biochemistry student with a keen interest in AI, I believe this interdisciplinary approach holds immense potential for advancement and innovation.
However, I face the challenge of identifying relevant literature in this emerging field. I would greatly appreciate guidance on effective keywords and search strategies to navigate this landscape of research and achieve my research goals.
Hello , you can refer to the article links that have been pasted below. They are a bit specific towards cancer but it might give you a direction for your study:
https://www.hindawi.com/journals/bmri/2022/7205241/
https://aacrjournals.org/cancerdiscovery/article/11/4/900/665825/Artificial-Intelligence-in-Cancer-Research-and
https://pubmed.ncbi.nlm.nih.gov/33396976/
Article Application of artificial intelligence models and optimizati...
Article Clinical applications of artificial intelligence and machine...
You can look for more articles according to your study on search engines like pubmed, springer, google scholar etc.
Your interdisciplinary approach at the intersection of computational biology, artificial intelligence, and clinical biochemistry is promising. To navigate the literature effectively, consider the following keywords and search strategies:
1. **Keywords for Computational Biology and AI:**
- Computational Biology
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Bioinformatics
- Computational Genomics
- Systems Biology
- Data Mining in Biology
2. **Combining Keywords:**
- Use Boolean operators (AND, OR, NOT) to combine keywords effectively. For example:
- "Computational Biology AND Artificial Intelligence"
- "Clinical Biochemistry AND Machine Learning"
3. **Specific Techniques:**
- Include specific AI techniques or methodologies:
- "Deep Learning in Clinical Trials"
- "Machine Learning in Molecular Studies"
- "Data Mining in Biochemical Research"
4. **Biomedical Applications:**
- Tailor your search to biomedical applications:
- "AI in Translational Medicine"
- "Computational Biology in Drug Discovery"
- "Machine Learning in Clinical Diagnostics"
5. **Animal Models and Cell Culture:**
- Specify your interest in laboratory and experimental work:
- "AI in Animal Models for Clinical Biochemistry"
- "Machine Learning in Cell Culture Experiments"
6. **Clinical Applications:**
- Focus on clinical aspects:
- "AI in Clinical Biochemistry"
- "Computational Biology in Precision Medicine"
- "Machine Learning in Clinical Trials for Biochemical Studies"
7. **Journals and Conferences:**
- Explore relevant journals and conferences in the field. Search within specific journals known for publishing interdisciplinary research.
8. **Research Databases:**
- Utilize databases like PubMed, IEEE Xplore, and ScienceDirect. Experiment with different combinations of keywords to refine your search.
9. **Review Articles:**
- Look for review articles that provide comprehensive overviews of the integration of AI and computational biology in clinical biochemistry.
10. **Collaborative Platforms:**
- Explore collaborative platforms or databases where researchers in interdisciplinary fields share their work, such as ResearchGate or Google Scholar.
I think there are several ways that they could be integrated. Each of those ways will have different ways to best approach this and a different literature review. For example what is the computational biology you are interested in - an DNA analysis is very different to molecular simulations or simulating a artificial hip. Biological laboratories can produce very different data a spead sheet is different to a image and the types of AI are different. It is common in computational science to want to compare simulation/computational results to experimental/observational results. I imagine this would be a good starting point for what you are interested in. In this case I think what you would want to do is use AI to segment or analyse images so that you have something to compare back to the simulation results. Can you give some examples of what you want to compare or research questions you want to be able to answer.
- Badges
- Science topic
- Similar topics
- Biological Science
- Immunology
- Antibodies