AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning
Abstract
:1. Introduction
- Since the methods proposed here are tested on freely downloadable datasets (http://dmb.iasi.cnr.it/supbarcodes.php accessed on 17 November 2024) (split into training and test sets are available), using standard and deep learners (CNN and SVM) fed with various representations of DNA sequences, our system provides a baseline against which future researchers can compare results using ML-based taxonomic methods for classifying species using DNA barcodes;
- We offer and compare novel methods for representing DNA sequences in a way suitable for DNN training;
- We propose a method for creating ensembles by varying how the DNA sequence is represented;
- The datasets and all code developed for this project are available online, accessed on 17 November 2024, at https://github.com/LorisNanni/AI-powered-Biodiversity-Assessment-Species-Classification-via-DNA-Barcoding-and-Deep-Learning.
Related Works
2. Materials and Methods
2.1. DNA Barcoding Representations
2.1.1. 1-Hot
2.1.2. 2-Mer
2.1.3. 2-Mer-p
2.1.4. 2-Me-p-All
2.1.5. FCGR
- The nucleotide bases “A”, “T”, “G”, and “C” correspond to each corner of the square.
- The starting nucleotide in the sequence is situated midway between the square center and the letter-corresponding corner.
- The second nucleotide is positioned midway between the first nucleotide location and the letter-associated corner.
- Until every available space in the matrix is assigned, the process is repeated recursively.
2.1.6. Standardizing Sequence Length and Ensemble
- For all DNA representations, we train each network twenty times, thereby obtaining different outputs, since the training data are shuffled at every epoch for each training of a given network;
- For 2-Mer-p, twenty networks are trained, each using a unique physicochemical property to represent a pair of DNA bases. Overfitting is avoided by using only the first twenty properties available at http://lin-group.cn/server/iOri-PseKNC2.0/download.html (accessed on 17 November 2024), i.e., no ad-hoc dataset selection is performed.
2.2. Neural Network Architectures
- Convolution2d(3, 16, ‘Padding’, ‘same’): The size of the convolutional kernel/filter is 3 × 3. The number of filters is 16. ‘Padding’, ‘same’ means the padding is set so that the spatial dimensions of the input and output feature maps are the same.
- Batch normalization: The output of the previous layer is normalized, thus helping with training stability and convergence.
- Dropout: This CNN introduces dropout, a regularization technique to randomly set a fraction of input units to zero during training. Dropout helps prevent overfitting. The dropout rate is 0.5.
- Relu: A Rectified Linear Unit (ReLU) activation layer.
- Fully connected(8): The number of neurons in this fully connected layer is 8.
- Fully connected: The number of neurons in this layer is equal to the number of classes in the classification task. This layer produces the final output scores before applying softmax.
- Softmax: The softmax activation function is applied to the output, converting logits into probabilities.
- Convolution2d(5, 16, ‘Padding’, ‘same’): The size of the convolutional kernel/filter is 5 × 5. The number of filters is 16. ‘Padding’, ‘same’ means the padding is set so that the spatial dimensions of the input and output feature maps are the same.
- Relu: Rectified Linear Unit activation layer.
- Convolution2d(5, 36, ‘Padding’, ‘same’): CNN2 has another convolutional layer with size 5 × 5. The number of filters is 36.
- Relu: Another ReLU activation layer.
- Max pooling2d(2): This is a max pooling layer with a 2 × 2 pool size. Max pooling helps reduce spatial dimensions.
- Dropout(0.2): CNN2 also has a dropout layer with a dropout rate 0.2.
- Relu: Another ReLU activation layer.
- Fully connected(1024/reduce). A fully connected layer with 1024/reduce output neurons. The value of reduce is related to the dataset. We set it to ‘1’ and increase the value if and when encountering a GPU memory problem.
- Relu: ReLU activation layer.
- Fully connectedLayer(1024/reduce). Another fully connected layer/reducer with 1024 output neurons.
- Relu: Another ReLU activation layer.
- Fully connected(1024/reduce). Yet another fully connected layer with 1024/reduce output neurons.
- Relu: Another ReLU activation layer.
- Fully connected(numClasses): A fully connected layer with the number of neurons equal to the number of classes, as is typical of a CNN output layer.
- Softmax: The softmax activation layer normalizes the output into a probability distribution over the classes.
- flattenLayer: Converts the multi-dimensional input (such as a 2D image) into a 1D vector by flattening the spatial dimensions.
- selfAttentionLayer(8, 64): A layer that applies self-attention, which allows the network to focus on different parts of the input. Parameters: Number of attention heads = 8. Size of the projection = 64.
- bilstmLayer(100): Bidirectional Long Short-Term Memory layer; a recurrent layer that can process sequences in both forward and backward directions. Each BiLSTM cell has 100 hidden units.
- batchNormalizationLayer: It improves model convergence and stabilizes the training process by standardizing the inputs to each layer.
- fullyConnectedLayer(numClasses): A fully connected layer that maps the output from the BiLSTM layer to the number of classes in the classification task.
- Softmax: The softmax activation layer normalizes the output into a probability distribution over the classes.
3. Datasets
3.1. Simulated Dataset
3.2. Real Datasets
4. Results
4.1. Ablation Experiments
- CNN1 + CNN2, the fusion by sum rule between the ensembles of CNN1 and CNN2, both trained using 2-Mer-p;
- CNN1, CNN2 and ATT, ensemble, combined by sum rule, of 20 CNN1/CNN2 or 20 ATT, coupled with 2-Mer-p;
- FCGR, the images created using FCGR used for building an ensemble, combined by sum rule, of 20 CNN1;
- X + Y, the sum between the approaches X and Y.
- An average EUC obtained by coupling 2-Mer-p and CNN1 of 0.118;
- An average EUC obtained by coupling 2-Mer-p and CNN2 of 0.090.
CNN2 | CNN1 | CNN1 + CNN2 | ATT | CNN1 + ATT | FCGR | CNN1 + ATT + FCGR | |
---|---|---|---|---|---|---|---|
Cypraeidae | 0.098 | 0.089 | 0.085 | 0.079 | 0.080 | 0.125 | 0.091 |
Drosophila | 0.126 | 0.138 | 0.119 | 0.130 | 0.130 | 0.223 | 0.130 |
Inga | 0.139 | 0.276 | 0.227 | 0.215 | 0.173 | 0.281 | 0.267 |
Bats | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fishes | 0.127 | 0.135 | 0.117 | 0.123 | 0.135 | 0 | 0 |
Birds | 0.059 | 0.059 | 0.053 | 0.030 | 0.045 | 0.119 | 0.045 |
Average | 0.092 | 0.116 | 0.100 | 0.096 | 0.094 | 0.125 | 0.088 |
4.2. Comparative Studies
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Ne | Individual | Seq. Length | Species |
---|---|---|---|---|
Ne1000 | 1000 | 20 | 650 | 50 |
Ne10000 | 10,000 | 20 | 650 | 50 |
Ne50000 | 50,000 | 20 | 650 | 50 |
Dataset | Type | Training/Test Nums | Seq. Length | Species | Gene Region | Reference |
---|---|---|---|---|---|---|
Cypraeidae | Invertebrates | 1656/352 | 614 | 211 | COI | [22] |
Drosophila | Invertebrates | 499/116 | 663 | 19 | COI | [23] |
Inga | Plants | 786/122 | 1838 | 63 | trnD-trnT, ITS | [24] |
Bats | Vertebrates | 695/144 | 659 | 96 | COI | [25] |
Fishes | Vertebrates | 515/111 | 718 | 82 | COI | [26] |
Birds | Vertebrates | 1306/317 | 691 | 150 | COI | [4] |
EUC-CNN1 | 1-Hot | 2-Mer | 2-Mer-p | 2-Me-p-All |
---|---|---|---|---|
Cypraeidae | 0.101 | 0.103 | 0.089 | 0.088 |
Drosophila | 0.125 | 0.158 | 0.138 | 0.221 |
Inga | 0.255 | 0.276 | 0.276 | 0.268 |
Bats | 0 | 0 | 0 | 0 |
Fishes | 0.123 | 0.118 | 0.135 | 0.135 |
Birds | 0.050 | 0.043 | 0.059 | 0.057 |
Average | 0.109 | 0.116 | 0.116 | 0.128 |
EUC-CNN2 | 1-Hot | 2-Mer | 2-Mer-p | 2-Me-p-All |
---|---|---|---|---|
Cypraeidae | 0.171 | 0.113 | 0.098 | 0.104 |
Drosophila | 0.142 | 0.138 | 0.126 | 0.130 |
Inga | 0.208 | 0.145 | 0.139 | 0.445 |
Bats | 0 | 0 | 0 | 0 |
Fishes | 0.127 | 0.122 | 0.127 | 0.110 |
Birds | 0.052 | 0.097 | 0.059 | 0.084 |
Average | 0.117 | 0.103 | 0.092 | 0.146 |
Ac | CNN1 | CNN2 | CNN1 + CNN2 | ATT | FCGR | CNN1 + ATT | CNN1 + ATT + FCGR |
---|---|---|---|---|---|---|---|
Cypraeidae | 96.88 | 96.31 | 96.59 | 96.31 | 96.31 | 96.59 | 96.59 |
Drosophila | 99.14 | 99.14 | 99.14 | 99.14 | 99.14 | 99.14 | 99.14 |
Inga | 93.39 | 92.56 | 93.39 | 93.39 | 95.04 | 94.21 | 95.04 |
Bats | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Fishes | 95.50 | 95.50 | 95.50 | 95.50 | 100 | 95.50 | 98.20 |
Birds | 95.58 | 96.53 | 97.16 | 98.11 | 94.95 | 98.11 | 98.11 |
Average | 96.74 | 96.67 | 96.96 | 97.07 | 97.57 | 97.25 | 97.85 |
Ac | CNN1 | CNN2 | CNN1 + CNN2 | ATT | FCGR | CNN1 + ATT | CNN1 + ATT + FCGR |
---|---|---|---|---|---|---|---|
Cypraeidae | 96.59 | 96.02 | 96.59 | 96.31 | 96.59 | 96.59 | 96.59 |
Drosophila | 99.14 | 99.14 | 99.14 | 99.14 | 99.14 | 99.14 | 99.14 |
Inga | 95.04 | 91.74 | 92.56 | 93.39 | 95.04 | 94.21 | 94.21 |
Bats | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Fishes | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Birds | 97.16 | 96.53 | 97.48 | 97.48 | 94.95 | 98.11 | 98.11 |
Average | 97.98 | 97.23 | 97.62 | 97.72 | 97.62 | 98.00 | 98.00 |
EUC | CNN1 | CNN2 | ||
---|---|---|---|---|
Mean | Std | Mean | Std | |
Cypraeidae | 0.168 | 0.048 | 0.158 | 0.957 |
Drosophila | 0.162 | 0.043 | 0.116 | 0.013 |
Inga | 0.599 | 0.252 | 0.620 | 0.214 |
Bats | 0 | 0 | 0 | 0 |
Fishes | 0.349 | 0.446 | 0.130 | 0.047 |
Birds | 0.796 | 0.288 | 0.343 | 0.188 |
Average | 0.346 | 0.179 | 0.228 | 0.236 |
Ac | CNN1 | CNN2 | ||
---|---|---|---|---|
Mean | Std | Mean | Std | |
Cypraeidae | 95.71 | 0.61 | 95.67 | 0.61 |
Drosophila | 99.05 | 0.24 | 99.14 | 0 |
Inga | 91.98 | 2.24 | 92.27 | 1.12 |
Bats | 99.97 | 0.16 | 99.97 | 0.16 |
Fishes | 95.36 | 0.33 | 95.23 | 0.54 |
Birds | 90.68 | 1.51 | 93.64 | 1.44 |
Average | 95.45 | 0.84 | 95.98 | 0.64 |
SVM | 1-Hot | 2-Mer | 2-Mer-p |
---|---|---|---|
Cypraeidae | 0.104 | 0.124 | 0.115 |
Drosophila | 0.470 | 0.454 | 0.430 |
Inga | 1.767 | 1.361 | 1.443 |
Bats | 0 | 0 | 0 |
Fishes | 0.135 | 0.144 | 0.135 |
Birds | 0.018 | 0.179 | 0.021 |
Average | 0.416 | 0.377 | 0.357 |
Simulated | Ac | F1 |
---|---|---|
CNN1 | 94.53 | 94.68 |
CNN2 | 94.48 | 94.70 |
ATT | 94.63 | 94.73 |
FCGR | 94.65 | 94.75 |
CNN1 + CNN2 | 94.47 | 94.68 |
CNN1 + ATT | 94.59 | 94.79 |
CNN1 + ATT + FCGR | 94.66 | 94.68 |
CNN1 (unfiltered) | 94.94 | 95.20 |
ATT (unfiltered) | 95.21 | 95.43 |
CNN1 + ATT (unfiltered) | 95.18 | 95.42 |
F1 Score—Aligned Data | CNN1 | ATT | CNN1 + ATT | FCGR | CNN1 + ATT + FCGR |
---|---|---|---|---|---|
Cypraeidae | 98.00 | 97.82 | 98.00 | 97.93 | 98.00 |
Drosophila | 99.75 | 99.75 | 99.75 | 99.75 | 99.75 |
Inga | 95.35 | 95.36 | 94.69 | 95.80 | 95.35 |
Bats | 100 | 100 | 100 | 100 | 100 |
Fishes | 100 | 100 | 100 | 100 | 100 |
Birds | 97.94 | 98.67 | 98.99 | 97.49 | 98.99 |
Average | 98.50 | 98.60 | 98.57 | 98.49 | 98.68 |
F1 Score—Unaligned Data | CNN1 | ATT | CNN1 + ATT | FCGR | CNN1 + ATT + FCGR |
Cypraeidae | 98.36 | 97.82 | 98.00 | 98.12 | 98.00 |
Drosophila | 99.75 | 99.75 | 99.75 | 99.75 | 99.75 |
Inga | 94.04 | 94.21 | 94.69 | 96.08 | 95.80 |
Bats | 100 | 100 | 100 | 100 | 100 |
Fishes | 96.08 | 96.54 | 96.08 | 100 | 99.35 |
Birds | 96.94 | 99.11 | 99.11 | 97.34 | 99.11 |
Average | 97.52 | 97.90 | 97.93 | 98.55 | 98.67 |
Accuracy | CNN1 + ATT Unaligned Data | CNN1 + ATT Aligned Data | CNN1 + ATT + FCGR Unaligned Data | CNN1 + ATT + FCGR Aligned Data | [9] | [8] | [27] | [10] |
---|---|---|---|---|---|---|---|---|
Cypraeidae | 96.59 | 96.59 | 96.59 | 96.59 | 94.32 | 96.31 | 95.45 | 96.88 |
Drosophila | 99.14 | 99.14 | 99.14 | 99.14 | 98.28 | 99.14 | 99.14 | 99.14 |
Inga | 94.21 | 94.21 | 95.04 | 94.21 | 89.83 | 93.44 | 95.11 | 92.62 |
Bats | 100 | 100 | 100 | 100 | 100 | 99.71 | 99.31 | 98.61 |
Fishes | 95.50 | 100 | 98.20 | 100 | 95.50 | 100 | 100 | 99.10 |
Birds | 98.11 | 98.11 | 98.11 | 98.11 | 98.42 | 97.48 | 97.16 | --- |
Average | 97.26 | 98.00 | 97.85 | 98.00 | 96.06 | 97.49 | 97.69 | --- |
CNN1 + ATT + FCGR | [9] | [8] | [27] | ||
---|---|---|---|---|---|
Ne1000 | Accuracy | 96.90 | 96.53 | 96.74 | 96.32 |
F1-score | 96.80 | --- | 96.53 | --- | |
Ne10000 | Accuracy | 96.92 | 96.77 | 96.57 | 96.60 |
F1-score | 96.85 | --- | 96.32 | --- | |
Ne50000 | Accuracy | 94.66 | 93.92 | 94.21 | 93.09 |
F1-score | 94.68 | --- | 93.89 | --- |
Species, Ac—EUC—F1 | Genus, Ac—EUC—F1 | |
---|---|---|
CNN1 | 98.95—0.0053—0.9890 | 82.15—1.403—0.8504 |
ATT | 98.61—0.0103—0.9846 | 90.02—1.063—0.9157 |
FCGR | 99.98—0.0000—0.9994 | 77.93—1.466—0.8462 |
CNN1 + ATT | 98.91—0.0064—0.9891 | 88.33—0.996—0.9062 |
CNN1 + ATT + FCGR | 99.90—0.0001—0.9991 | 89.87—0.898—0.9270 |
[27] | 96.15—not reported—not reported | 83.24—not reported—not reported |
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Nanni, L.; Cuza, D.; Brahnam, S. AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Technologies 2024, 12, 240. https://doi.org/10.3390/technologies12120240
Nanni L, Cuza D, Brahnam S. AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Technologies. 2024; 12(12):240. https://doi.org/10.3390/technologies12120240
Chicago/Turabian StyleNanni, Loris, Daniela Cuza, and Sheryl Brahnam. 2024. "AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning" Technologies 12, no. 12: 240. https://doi.org/10.3390/technologies12120240
APA StyleNanni, L., Cuza, D., & Brahnam, S. (2024). AI-Powered Biodiversity Assessment: Species Classification via DNA Barcoding and Deep Learning. Technologies, 12(12), 240. https://doi.org/10.3390/technologies12120240