| Submission declined on 3 December 2024 by 331dot (talk). This submission appears to read more like an advertisement than an entry in an encyclopedia. Encyclopedia articles need to be written from a neutral point of view, and should refer to a range of independent, reliable, published sources, not just to materials produced by the creator of the subject being discussed. This is important so that the article can meet Wikipedia's verifiability policy and the notability of the subject can be established. If you still feel that this subject is worthy of inclusion in Wikipedia, please rewrite your submission to comply with these policies.
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
Comment: External links should not be in the body of the text, only as references. If you are associated with the lab, that needs to be disclosed, see WP:COI and WP:PAID. 331dot (talk) 10:32, 3 December 2024 (UTC)
Biological mining refers to the process of retrieving biological information from the world by integrating computational biology, genomics, and machine learning. This approach aims to discover and develop new antibiotics and other useful molecules.[1]
In 2019, the De la Fuente Lab team proposed a digital approach to explore existing biological data repositories, such as genomes, metagenomes, and proteomes, as a means to expedite antibiotic discovery.[2] By utilizing advancements in artificial intelligence, this biological mining strategy facilitates the rapid identification of new molecules on a larger scale, moving beyond the slow pace of traditional experimental methods. To implement this, artificial intelligence models have been developed and trained on standardized experimental datasets to predict the antimicrobial properties of specific amino acid sequences, establishing a new paradigm in the field of antibiotic discovery. This research has also led to the identification of a novel class of peptides termed encrypted peptides.
Through the process of biological mining, researchers have identified hidden antimicrobial agents within diverse genomes, encompassing both living and extinct species, such as guavanine-2,[3] mamutusin, and neandertalin.[4]
References
edit- ^ Fuente-Nunez, Cesar de la (2024-11-26). "Mining biology for antibiotic discovery". PLOS Biology. 22 (11): e3002946. doi:10.1371/journal.pbio.3002946. ISSN 1545-7885. PMID 39591471.
- ^ Fuente-Nunez, Cesar de la (2024-11-26). "Mining biology for antibiotic discovery". PLOS Biology. 22 (11): e3002946. doi:10.1371/journal.pbio.3002946. ISSN 1545-7885. PMID 39591471.
- ^ Porto, William F.; Irazazabal, Luz; Alves, Eliane S. F.; Ribeiro, Suzana M.; Matos, Carolina O.; Pires, Állan S.; Fensterseifer, Isabel C. M.; Miranda, Vivian J.; Haney, Evan F.; Humblot, Vincent; Torres, Marcelo D. T.; Hancock, Robert E. W.; Liao, Luciano M.; Ladram, Ali; Lu, Timothy K. (2018-04-16). "In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design". Nature Communications. 9 (1): 1490. Bibcode:2018NatCo...9.1490P. doi:10.1038/s41467-018-03746-3. ISSN 2041-1723. PMC 5902452. PMID 29662055.
- ^ Wan, Fangping; Torres, Marcelo D. T.; Peng, Jacqueline; de la Fuente-Nunez, Cesar (July 2024). "Deep-learning-enabled antibiotic discovery through molecular de-extinction". Nature Biomedical Engineering. 8 (7): 854–871. doi:10.1038/s41551-024-01201-x. ISSN 2157-846X. PMC 11310081. PMID 38862735.