Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning.
The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a m...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012716 |
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| author | Mohammad Neamul Kabir Li Rong Wang Wilson Wen Bin Goh |
| author_facet | Mohammad Neamul Kabir Li Rong Wang Wilson Wen Bin Goh |
| author_sort | Mohammad Neamul Kabir |
| collection | DOAJ |
| description | The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect. This leads to more accurate insights and a stronger understanding of complex biological systems. In the realm of personalized AI and precision medicine, the importance of dissimilarities is paramount. Personalized AI builds local models for each sample by identifying a network of neighboring samples. However, if the neighboring samples are too similar, it becomes difficult to identify factors critical to disease onset for the individual, limiting the effectiveness of personalized interventions or treatments. This paper discusses the "similarity of dissimilarities" concept, using protein function prediction, ML, and personalized AI as key examples. Integrating this approach into an analysis allows for the design of better, more meaningful experiments and the development of smarter validation methods, ensuring that the models learn in a meaningful way. |
| format | Article |
| id | doaj-art-79f4a88908bc415293642fb959c19977 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-79f4a88908bc415293642fb959c199772025-02-03T21:30:52ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101271610.1371/journal.pcbi.1012716Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning.Mohammad Neamul KabirLi Rong WangWilson Wen Bin GohThe "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect. This leads to more accurate insights and a stronger understanding of complex biological systems. In the realm of personalized AI and precision medicine, the importance of dissimilarities is paramount. Personalized AI builds local models for each sample by identifying a network of neighboring samples. However, if the neighboring samples are too similar, it becomes difficult to identify factors critical to disease onset for the individual, limiting the effectiveness of personalized interventions or treatments. This paper discusses the "similarity of dissimilarities" concept, using protein function prediction, ML, and personalized AI as key examples. Integrating this approach into an analysis allows for the design of better, more meaningful experiments and the development of smarter validation methods, ensuring that the models learn in a meaningful way.https://doi.org/10.1371/journal.pcbi.1012716 |
| spellingShingle | Mohammad Neamul Kabir Li Rong Wang Wilson Wen Bin Goh Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. PLoS Computational Biology |
| title | Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. |
| title_full | Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. |
| title_fullStr | Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. |
| title_full_unstemmed | Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. |
| title_short | Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning. |
| title_sort | exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning |
| url | https://doi.org/10.1371/journal.pcbi.1012716 |
| work_keys_str_mv | AT mohammadneamulkabir exploitingthesimilarityofdissimilaritiesforbiomedicalapplicationsandenhancedmachinelearning AT lirongwang exploitingthesimilarityofdissimilaritiesforbiomedicalapplicationsandenhancedmachinelearning AT wilsonwenbingoh exploitingthesimilarityofdissimilaritiesforbiomedicalapplicationsandenhancedmachinelearning |