Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data
Abstract
:1. Introduction
- The proposed microbial sequence data processing pipeline;
- The utilization of frequency analysis for microbial sequence data transformation into text vectors;
- The proposed hierarchical clustering method over microbial sequence text vectors;
- The evaluation of the proposed method on an animal feces dataset.
2. Background
3. Microbial Sequence Data Pipeline
4. Materials and Methods
4.1. Dataset
4.2. Preprocessing
4.3. Frequency Analysis
Algorithm 1 General pseudocode of the FA sequence computation |
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4.4. Clustering
- Data selection;
- Feature selection;
- Similarity measure;
- Clustering algorithm;
- Cluster evaluation;
- Cluster interpretation.
4.5. Proposed Hierarchical Clustering Method
Algorithm 2 Hierarchical clustering algorithm |
|
Algorithm 3 General pseudocode of the steps of the proposed method |
|
4.6. Visualization
5. Results and Discussion
5.1. Analysis of the Groups
5.2. Analysis of the Beta Diversity
5.3. Quantitative Results Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Group | Number of Samples | Number of ZOTUs | |
---|---|---|---|
1 | Cat | 33 | 11.310 |
2 | Cattle | 79 | 28.328 |
3 | Cattle manure | 31 | 34.668 |
4 | Chicken | 35 | 19.861 |
5 | Dog | 43 | 12.506 |
6 | Fallow deer | 34 | 28.978 |
7 | Gull | 21 | 9.460 |
8 | Hedgehog | 28 | 6.164 |
9 | Horse | 36 | 28.835 |
10 | Human | 185 | 6.635 |
11 | Mouse | 26 | 6.660 |
12 | Nutria | 23 | 10.025 |
13 | Pig | 45 | 21.777 |
14 | Pig manure | 21 | 21.593 |
15 | Pigeon | 24 | 13.924 |
16 | Roe deer | 34 | 28.685 |
17 | Swan | 41 | 17.986 |
18 | Wild duck | 26 | 16.507 |
AA | AC | AG | … | TT | |
---|---|---|---|---|---|
Sample 1 | 0.07351 | 0.08578 | 0.05976 | … | 0.08654 |
Sample 2 | 0.07612 | 0.04586 | 0.05228 | … | 0.04661 |
… | … | … | … | … | … |
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Brezočnik, L.; Žlender, T.; Rupnik, M.; Podgorelec, V. Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data. Mathematics 2024, 12, 2717. https://doi.org/10.3390/math12172717
Brezočnik L, Žlender T, Rupnik M, Podgorelec V. Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data. Mathematics. 2024; 12(17):2717. https://doi.org/10.3390/math12172717
Chicago/Turabian StyleBrezočnik, Lucija, Tanja Žlender, Maja Rupnik, and Vili Podgorelec. 2024. "Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data" Mathematics 12, no. 17: 2717. https://doi.org/10.3390/math12172717