Use of machine learning and artificial intelligence techniques to improve prediction of gene function and drug efficacy BIO703

Annotated Bibliography

Use of machine learning and artificial intelligence techniques to improve prediction of gene function and drug efficacy

1.Huang, K., Cao X., Glass, L., Critchlow, C., Gibson, G., Jimeng S. (2021),

Machine learning applications for therapeutic tasks with genomics data,

Patterns, 2 (10), 100328.

The article by Huang et al (2021) investigates the interaction of genomics data with other forms of data such as electronic texts, images health records, proteins and compounds. In light of the knowledge that machine learning can be employed in extracting insights and identifying patterns from complex data, Huang et al (2021) evaluates a broad range of genomics applications of machine learning to facilitate efficacious and faster therapeutic interventions. However, the authors unearth a number of challenges in this process. The major ones include practical issues such as privacy, distrust of models, fairness as well as technical problems such as learning under divergent contexts especially in view of low resource limitations.

2. Sorayya, R., Sharareh, R., Niakan K., Soheila S. (2022) Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.,BioMed Research International, 2 (3), p 1-34, https://doi.org/10.1155/2022/7842566

The article by Sorayya et al (2022) sought to evaluate the techniques used in AI and ML alongside their effectiveness when applied to neoplasm precision medicine. The findings from this study indicates that the use of AI and ML methods and the associated techniques harbor positive implication in personalized and tailored medicine.

3. Fan, K, Cheng L, Li L. (2021) Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects, Brief Bioinform, 22(6):bbab271. doi: 10.1093/bib/bbab271. PMID: 34347041; PMCID: PMC8574962.

The article by Fan et al (2021) presents a structured overview of existing machine learning algorithms and existing databases particularly in deep learning systems in computational predicting models on anti-cancer drug synergy. Further, the authors detail extant model architectures while providing a unified framework for machine learning models. They also go on to discuss the limitations and contributions of the learning models and architectures while also providing a greenlight on the future design of computations models. Moreover, they undertake a deeper investigation and a comparison of various models in regard to their prediction performances.

4. Kolluri, S., Lin, J., Liu, R., Zhang, Y., Zhang, W. (2022) Machine Learning, and Artificial Intelligence in Pharmaceutical Research and Development: a Review, AAPS J., 24(1), 19.doi: 10.1208/s12248-021-00644-3.

The purpose of this article is to clarify basic concept while presenting use-cases and providing knowledge as well as solid perspective on the best use of machine learning (ML) and artificial intelligence (AI) in research and development.

5. Mehar, S., Rohan, G., Rashmi, K., Ambasta, P. (2022)
   Chapter Three - Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis, Progress in Molecular Biology and Translational Science, Academic Press, 190, (1), pp57-100.
   
   The article by Mehar et al (2022) demonstrates that transferring all research exploring the convergence between artificial intellect and accurate medicine will help to solve the challenges facing precision medicine .It summarized big data analytics and the consolidation of biomaterial in precision medicine. Further, the authors also highlight the challenges and opportunities of artificial intelligence in precision medicine.

6. Zielinski., A. (2021) AI and the future of pharmaceutical research. Computers and Society 2 (1), pp 10-38. Available at: https://www.researchgate.net/publication/353116783_AI_and_the_future_of_pharmaceutical_research (retrieved on 24th January, 2023).
   
   In this article, Zielinski (2021) investigates how developments in artificial intelligence within the pharmaceutical industry are likely to affect advancements of new drugs in the future years. Accordingly, the authors postulate that the emerging revolution in Artificial intelligence and Machine Learning will help the pharmaceutical industry from a productivity disaster to peak productivity, further enabling a surge of safe and affordable medicines.


7. Ziaurrehman T., Vh-Koskela, M., & Aittokallio, T.(2021)Artificial intelligence, machine learning, and drug repurposing in cancer,Expert Opinion on Drug Discovery,16 (9),pp.977-989,DOI:10.1080/17460441.2021.1883585

In this article, the authors investigate the role of supervised AI and ML methods to make use of information resources and databases that are publicly available. Their emphasis is on the utilization of comprehensive target activity profiles that facilitate the process of systematic repurposing through extension of the drugs target profile to incorporate potent off-targets with therapeutic latent for a new clue. Accordingly, the authors affirm the invaluability of genomic data to aligning drug therapies focusing particular aberrations either utilizing the intended repurposed drugs or intended medical indications.

8. You, Y., Lai, X., Pan, Y.(2022)Artificial intelligence in cancer target identification and drug discovery,Sig Transduct Target Ther7 (2), p. 156. https://doi.org/10.1038/s41392-022-00994-0

In this article You et al (2022) reviews and analyzes mechanisms through which AI is employed in identifying new and emerging anti-cancer targets as well as in the drug discovery process. Accordingly, the authors postulate that the AI and MLmodels have availed quantitative frameworks to understand the association between cancer and network characteristics. Consequently, this has facilitated easier identification of possible anti-cancer targets alongside the discovery of new drug possibilities.

9. Maserati, E. (2022) Integration of Artificial Intelligence and CRISPR/Cas9 System for Vaccine Design, Cancer Informatics, 2 (1), pp.1-20.

In this article, Maserati (2022) argues that integrating two approaches, that is; CRISPR/Cas9 and Artificial Intelligence could be among the most effective means of designing a vaccine. The AI approaches include epitope prediction, knowledge discovery and modeling-based applications such as machine learning. The author continues to note that the utilization of AI capacity for editing genomes through CRISPR/Cas9 facilitates the modification of molecular cloning and gene mutations. He finds that this approach has high level of efficiency and accuracy in detecting the role of genes related to cancer as well as in exploring drug targets while pointedly increasing the genomic comprehension of cancer.

10. He, D., Liu, Q., Wu, Y.(2022) A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening.Nat Mach Intell4, 879892 (2022). https://doi.org/10.1038/s42256-022-00541-0

In this article, He et al (2022) explains the functioning and efficacy of a deconfounding autoencoder (CODE-AE) which is a new context-aware with a capability of extracting inherent biological signals camouflaged by patterns which are context specific alongside confounding factors. The authors analysis finds that CODE-AE which is a form of AI improved the robustness and accuracy over recent advancements in predictions of patient specific clinical drug responses specifically from cell-line compound screens.

11. Moingeon, P. (2021) Applications of Artificial Intelligence to Drug Development: Why is This a Revolution?, American Review, Available at: https://www.americanpharmaceuticalreview.com/Featured-Articles/580340-Applications-of-Artificial-Intelligence-to-Drug-Development-Why-is-This-a-Revolution/ (retrieved on 24^th January, 2023)

In this article, Moingeon (2021) argues that the revolutionalization and intergration of AI and Machine Learning applications in drug discovery has significantly shortened the drug discovery process while also reducing failure rates during the process. This has been made possible through strengthening the justification behind the selection of drug candidates and targets. According to the author, AI has brought unparalled level of insights to both the features of drug candidates and patient specificities, thus giving way to computational precision medicine. Computational precision medicine makes it possible for drug developers to tailor medicines precisely in line with the features of individual patients in regard to their physiology, their susceptibilities to environmental and genetic risks and the pathophysiology of their disease.

12. Paul, D., Sanap, G, Shenoy, S., Kalyane, D., Kalia K, Tekade, K. (2021) Artificial intelligence in drug discovery and development, Drug Discov Today. 26(1), pp.80-93. doi:10.1016/j. drudis.2020.10.010

This review highlights the utilization of AI and ML in different aspects within the pharmaceutical industry. This use includes drug discovery, repurposing, and development, improving productivity in the pharmaceutical processes, as well as clinical trials. The review finds that AI and ML plays a significant role in reducing human workload alongside achievement of intended goals within a short time frame. The authors also analyze the key techniques and tools used in AI, inherent challenges and how they can be leveraged alongside the future prospects of AI within the pharmaceutical sector.

13. Vollmer, S., Mateen, B., Bohner, G., Kiraly, F., Ghani, R., Johnson, P., Cumbers, S., Jonas, A., McAllister, K., Myles, P., Grainger,D., Birse, M., Branson, R., Moons, K., Collins, G., Ioannidis, J., Holmes, C., Hemmingway, H. (2020) Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness,BMJ368 (16), pl6927

In this article, Vollmer et al (2020) proposes 20 critical questions which work as a framework for research teams to inform the design, reporting, conduct for peer reviewers, editors in assessing the contributions of AI, ML and other contemporary statistical methods to the health literature and for policymakers, clinicians, and patients to appraise new findings with an aim of delivering benefits to patients health care. Accordingly, the authors note that these systems have a potential to improve the reliability of predicting prognosis, the accuracy of diagnosis, targeting treatments as well as enhancing the efficiency of health systems.

14. Patel, V., and Shah, M. (2021) A comprehensive study on artificial intelligence and machine learning in drug discovery and drug development, Intelligent Medicine, 2 (3), pp. 134-140 DOI:10.1016/j.imed.2021.10.001

The paper by Patel and Shah (2021) analyzes the utilization of AI and ML in augmenting drug discovery and development for the purpose of making them more accurate and efficient. This extensive review supports the function of AI and ML in facilitating the processes of drug discovery, development, reducing the cost of these processes and eliminating the necessity for clinical trials. This owes to the possibility of undertaking simulations using AI and ML. Furthermore, they also make it possible for researcher to study diverse molecules more deeply without the need for trials.

15. Quazi, S. (2021) Role of artificial intelligence and machine learning in bioinformatics: Drug discovery and drug repurposing. Preprints2021, 2021050346 doi: 10.20944/preprints202105.0346.v1.

In this paper, Quazi (2021) evaluates the role and efficacy of AI in transforming the field of pharmacology. Accordingly, they argue that the AI has a significant potential of designing more novel and efficient vaccines which has worked to reduce the cost of the whole process of vaccine development. Furthermore, they have also enhanced the prediction of the molecular structure and mechanism for increased possibility of developing novel drugs. High resolution imaging, electronic and clinical databases are being increasingly utilized in aiding the drug development field.