Paper: Biological Sequence Analysis Using B ́ezier Curve (ICLR-2024)

Reviewer comment (Experiments):
For a paper about 2D images, there are very few images. The algorithm is complicated enough to warrant at least one example where the authors illustrate the creation an image from a simple sequence. Moreover, they could compare this to other image production methods and demonstrate why they think their images are superior.

Author response:
We agree with the reviewer for the inclusion of more images. The main reason for not including more images was the page size issue during the original submission. In the revised version of the paper, we have now included an image for the Spike2CGR baseline in Figure 7 in the appendix. Similarly, we also include the images for different methods in Figures 8, 9, and 10 for the Human DNA, Music, and SMILES string datasets, respectively (in the appendix). We will further include more images in the camera-ready version including a visualization showing how the image is created from start to end. Since such an interactive image would not work in the PDF format, we will include that on our website, which we cannot do right now to maintain anonymity.

Reviewer comment (Evaluation):
Train time is a rather weak method for understanding the compute requirements of each method, it would be better to report training flops, and even scatter plot by training flops.

Author response:
The training time is just to get an idea regarding how the vision model behaves for different types of images as input along with how they behave compared to each other in terms of runtime vs. classification results. Since the vision models are just used for downstream task and are not part of our image representation learning idea, that is why we did not included training flops for those models.

Reviewer comment (Experiments):
What about a transformer on the sequence itself, rather than just a ViT on images? Seems like an important comparison to make.

Author response:
We want to mention that we tried the ``ProteinBERT`` model on the sequences but the results were not very encouraging compared to the baseline results shown in the paper. Therefore, we decided to use other models as shown in the paper. Moreover, the transformer architectures are domain-specific. Since our idea can be generalized to different types of sequence data, we believe that such a comparison would not be that important. Also, the main focus of this paper is to observe the performance of vision models in terms of sequence classification. Therefore, considering all scenarios, we decided to not include transformer models that take sequences as input.

Reviewer comment (Novelty):
From the machine learning perspective, the paper does not have a significant contribution. While the problem is important and the results are very promising, the technical novelty is limited. Perhaps this piece of work can be better appreciated by the bioinformatics community.

Author response:
We respectfully would like to disagree with the reviewer. The main idea of this paper is to transform any type of sequence into an image-based representation so that we can open the doors of applying vision models for better classification. Since there is more sequence-based data available for the biological sequences, we decided to use different types of such protein sequences to show the performance of the proposed method. However, the prove thethe relevance to ICLR community point, we did the following modifications

To show the generalizability, we now included three more datasets that are non-protein sequences. Their details are shown in Table 1 in the revised paper. Firstly, we use human DNA data, which contains nucleotide sequences. The second data that we use now comprises SMILES strings, which are comprised of drug molecules. The third data that we use is the music sequence data, where the audio sequences belong to 10 unique music genres. Now, using protein sequences (as in the initial submission) along with the nucleotide sequences, SMILES strings, and music sequences datasets helps us to show that the proposed method can generalize on sequences from different domains. Here please note that the majority of the baselines (other than RandomCGR) cannot operate on SMILES and Music data since the number of unique characters is>20 and the baseline methods cannot operate on such complex datasets by design. The results for the human DNA data, SMILES string data, and Music sequence data are shown in Table 5, 6, and 7 in the revised version. Compared to Spike2CGR, we can observe that the proposed method outperforms in the majority of the scenarios. Similarly, on all new datasets, the proposed method performance significantly improves for the pre-trained vision models.

Considering that the ICLR community is interested in the "Learning Representation" work, we are proposing a "representation learning" method that takes sequences as input and transforms it into image-based "Representation", which can then be used for downstream classification task. However, to better show a connection to the machine learning domain, we now included Spike2CGR[1] as one of the baselines (detail included in Table 2 in the revised paper), which works on a similar problem domain for the protein sequences and has recently been published in the "Springer Machine Learning Journal". We believe that considering such a method as a baseline could help to justify that the problem is indeed important for the machine learning community as that work is published in one of the highly rated machine learning journals.

We now believe that considering a recent method as a baseline from ML journal along with the new datasets from non-protein sequence domain could help to show the generalizability of proposed method and relevance to the ICLR community.

Reviewer comment (Novelty):
The proposed approach is not a learning algorithm, where the representation is learned automatically. Instead, the approach is a transformation of individual data points into a different representation, which is shown to be more effective for machine learning algorithms.

Author response:
We would like to clarify that the learning of images from the sequences is done in a completely unsupervised way. We proposed an algorithm that "learns" a representation in the form of the image where we have sequences as an input.

Reviewer comment (Presentation):
The resulting images are not presented, even though the manuscript claims that these are more interpretable and meaningful. A qualitative comparison of how the images look like with respect to previous attempts for a given sequence (or a portion of it), would be helpful.

Author response:
We already presented the images for the Bezier curve in Figure 5 in the appendix. Moreover, we provide more images for the baselines as well in Figure 7 in the appendix. We would request the reviewer to please check the images along with further results and discussion mentioned in the appendix. We did not mention such an analysis in the main paper due to space limitations.

Reviewer comment (Presentation):
The paper devotes much space in tables with dataset and methods descriptions. This space could be better utilized with different analysis and other results.

Author response:
The reviewer rightfully pointed out that some tables can go to the appendix and space could be better utilized with different analyses. In our initial submission, we tried to include important information in the main paper and moved the remaining content to the appendix. However, since we now included new results for the three new datasets along with further discussion based on the reviewer's comments, we will adjust the content as per instructions in the final camera-ready version.

Reviewer comment (Theory):
The proposed representations might not encode signal about sequential information as amino acid order is not explicitly encoded, which may prove to be necessary in some usecases.

Author response:
The reviewer rightfully pointed out that order information could be important in some cases, e.g. virus classification where there are virus and non-virus mutations in the biological sequences. However, we want to mention that our the specific scenario, the order of the sequence is not important. The order information comes into play when we want to design numerical vector-based representation. For the image transformation, we are only interested in capturing the mutations, which are a kind of differentiating factor among different class images.

Reviewer comment (Evaluation):
(minor) There is a notable computational overhead to the proposed approach, although at a benefit of improved performance.

Author response:
We thank the reviewer for pointing this out. We want to mention that the proposed method is not parameter-heavy. Since we only use two parameters, i.e.  $m$ (the number of points along each curve) and $ite$ (line 10 in Algorithm 1), which is the number of deviation pair points, the image generation using the proposed method is very fast.

Reviewer comment (Experiments):
How does the performance scale with much longer input sequences? At what sequence length does the image encoding become unwieldy?

Author response:
Since the proposed method does not have a lot of parameters, having longer-length sequences does not affect scalability performance much. The maximum length for any sequence we tried is for the Human DNA dataset (included in Table 1 of the revised version), where the max length is $18921$. On such bigger length sequences, the image generation time was not affected much. We also want to mention that we do plan to make our code open-source. The main reason for not doing it as part of submission is to maintain anonymity. In the final camera-ready version of the paper, we will provide a URL to the GitHub repository containing our code.

Reviewer comment (Novelty):
In its current format, the paper may not be a good fit for ICLR audience, and the featurization is specific to a small domain of problem.

Author response:
We respectfully would like to disagree with the reviewer. The main idea of this paper is to transform any type of sequence into an image-based representation so that we can open the doors of applying vision models for better classification. Since there is more sequence-based data available for the biological sequences, we decided to use different types of such protein sequences to show the performance of the proposed method. However, the prove the generalizability and the relevance to ICLR community point, we did the following two modifications

``Generalizability``: To show the generalizability, we now included three more datasets that are non-protein sequences. Their details are shown in Table 1 in the revised paper. Firstly, we use human DNA data, which contains nucleotide sequences. The second data that we use now comprises SMILES strings, which are comprised of drug molecules. The third data that we use is the music sequence data, where the audio sequences belong to 10 unique music genres. Now, using protein sequences (as in the initial submission) along with the nucleotide sequences, SMILES strings, and music sequences datasets helps us to show that the proposed method can generalize on sequences from different domains. Here please note that the majority of the baselines (other than RandomCGR) cannot operate on SMILES and Music data since the number of unique characters is>20 and the baseline methods cannot operate on such complex datasets by design. The results for the human DNA data, SMILES string data, and Music sequence data are shown in Table 5, 6, and 7 in the revised version. Compared to Spike2CGR, we can observe that the proposed method outperforms in the majority of the scenarios. Similarly, on all new datasets, the proposed method performance significantly improves for the pre-trained vision models.

``Relevance to ICLR community:`` Considering that the community is interested in the "Learning Representation" work, we are proposing a "representation learning" method that takes sequences as input and transforms it into image-based "Representation", which can then be used for downstream classification task. However, to better show a connection to the machine learning domain, we now included Spike2CGR[1] as one of the baselines (detail included in Table 2 in the revised paper), which works on a similar problem domain for the protein sequences and has recently been published in the "Springer Machine Learning Journal". We believe that considering such a method as a baseline could help to justify that the problem is indeed important for the machine learning community as that work is published in one of the highly rated machine learning journals.

We now believe that considering a recent method as baseline from ML journal along with the new datasets from non-protein sequence domain could help to show the generalizability of proposed method and relevance to the ICLR community.

Reviewer comment (Reproducibility):
How much hyper-parameter optimization is needed for this approach? Are these B'ezier features easy to generate.

Author response:
For the Bézier Curve Based Image Generation, there are only two parameters. The first parameter is $m$, which is the number of points along each curve. The second parameter is $ite$ (line 10 in Algorithm 1), which is the number of deviation pair points. Hence our model is not parameter-heavy. However, please note that the hyperparameters for the vision models is different from the Bézier Curve model as the vision models are only used for the downstream classification task. The details regarding hyperparameters of vision models is already given in Section 4.3.1.