Original: Novel fuzzy approach to Antimicrobial Peptide Activity Prediction: A tale of limited and imbalanced data that models won’t hear
Revised: Novel fuzzy approach to Antimicrobial Peptide Activity Prediction: A tale of limited and imbalanced data that models won’t hear

Original: Abstract
Revised: Abstract

Original: Antimicrobial peptides have gained immense attention in recent years due to their potential for developing novel antibacterial medicines, next-generation anticancer treatment regimes, etc.
Revised: Antimicrobial peptides have gained immense attention in recent years due to their potential for developing novel antibacterial medicines, next-gen anti-cancer treatment regimes, etc.

Original: Owing to the signiﬁcant cost and time required for wet lab-based AMP screening,researchers have framed the task  as an ML problem.
Revised: Owing to the signiﬁcant cost and time required for wet lab-based AMP screening, the task has been framed as an ML problem.

Original: However, traditional models rely on the unrealistic premise of large medical data availability to achieve signiﬁcant performance levels; otherwise, they overﬁt, decreasing model precision.
Revised: However, traditional models rely on the unrealistic premise of large medical data availability to achieve signiﬁcant performance levels; otherwise, they overﬁt, decreasing model precision.

Original: The collection of such labeledmedical data is a challenging and  expensive task in itself.
Revised: The collection of such labeled data is a challenging and an expensive task in itself.

Original: The current study is the ﬁrst to examine models in a real-world setting, training them on restricted and highly imbalanced data.
Revised: The current study is the ﬁrst to examine models in a real-world setting, training them on restricted and highly imbalanced data.

Original: A ‘Fuzzy Intelligence’ based model is proposed for short ( <aa) AMP activity prediction, and its ability to learn on limited and severely skewed high-dimensional space mapping is demonstrated over a set of experiments.
Revised: A Fuzzy Intelligence-based model is proposed for short ( < 30 aa) AMP activity prediction, and its ability to learn on limited and severely skewed high-dimensional space mappings is demonstrated over a set of experiments.

Original: The proposed model signiﬁcantly outperforms state-of-the-art ML models trained onthe same data.
Revised: The proposed model signiﬁcantly outperforms state-ofthe-art ML models trained on same data.

Original: The ﬁndings demonstrate the model’s efﬁcacy as a potential method for in silico AMP activity prediction.
Revised: The ﬁndings demonstrate the model’s efﬁcacy as a potential method for in silico AMP activity prediction.

Original:  Introduction
Revised:  Introduction

Original: Most organisms generate antimicrobial peptides as part of their innate immune response to pathogens.
Revised: Most organisms generate antimicrobial peptides as part of their innate immune response to pathogens.

Original: They are a signiﬁcant resource for drug development since they represent a broad repertory of antimicrobial agents.
Revised: They are a signiﬁcant resource for drug development since they represent a broad repertory of antimicrobial agents.

Original: To some extent, recent advances in peptide engineering and careful optimization have helped overcome AMP’s toxicity to human cells and its unstable nature [1].
Revised: To some extent, recent advances in peptide engineering and careful optimization have helped overcome AMP’s toxicity to human cells and its unstable nature [1].

Original: However, the high expense of peptide screening makes the AMP production process time-consuming and costly.
Revised: However, the high expense of peptide screening makes the AMP production process time-consuming and costly.

Original: Thus, in silico methods were developed.
Revised: Thus, in silico methods were developed.

Original: However, these require large volumes of costly labeled datasets to achieve signiﬁcant levels of performance.
Revised: However, these require large volumes of costly labeled datasets to achieve signiﬁcant levels of performance.

Original: Moreover, most deep learning and machine learning models require a balanced dataset to train.
Revised: Moreover, most deep learning and machine learning models require a balanced dataset to train.

Original: In actual practice, collecting a large number of positive samples is a difﬁcult task in itself.
Revised: In actual practice, collecting a large number of positive samples is a difﬁcult task in itself.

Original: The present work is the ﬁrst to address these issues formulating Antimicrobial peptide prediction task as a ‘fuzzy’ problem.
Revised: The present work is the ﬁrst to address these issues formulating Antimicrobial peptide prediction task as a ‘fuzzy’ problem.

Original: The proposed method outperforms other state-of-the-art
Revised: The proposed method outperforms other state-of-the-art

Original: ML models by a signiﬁcant margin.
Revised: ML models by a signiﬁcant margin.

Original: Second, the model’s other novelty lies in its capacity to attain high classiﬁcation accuracy despite being trained on limited data samples, as proven experimentally, in contrast to previously proposed models that need large amounts of data that is difﬁcult and costly to obtain.
Revised: Second, the model’s other novelty lies in its capacity to attain high classiﬁcation accuracy despite being trained on limited data samples, as proven experimentally, in contrast to previously proposed models that need large amounts of data that is difﬁcult and costly to obtain.

Original: Third , another unique feature of the model lies in its robustness to high-class imbalance, commonly seen in real-world bioinformatics data, as demonstrated in the experiments performed.
Revised: Third , another unique feature of the model lies in its robustness to high-class imbalance, commonly seen in real-world bioinformatics data, as demonstrated in the experiments performed.

Original: Most models, on the other hand, perform inconsistently as the imbalance in classes increases.
Revised: Most models, on the other hand, perform inconsistently as the imbalance in classes increases.

Original:  Proposed Methodology
Revised:  Proposed Methodology

Original: Mapping the n − dimensional feature space.
Revised: Mapping the n − dimensional feature space.

Original: The generated feature vectors are mapped in the n − dimensional feature space.
Revised: The generated feature vectors are mapped in the n − dimensional feature space.

Original: For each sample, the feature vector is passed to the Classifying Neurons (CLN), which perform the classiﬁcation of the learned data using min-max hyperboxes [2].
Revised: For each sample, the feature vector is passed to the Classifying Neurons [Table] [Table] [Table]

Original: In this
Revised: Compensation

Original: [Table]
Revised: [Table]

Revised: (cid:6) (cid:2) (cid:24) (cid:6) (cid:3) (cid:24) (cid:6) (cid:4) (cid:23) (cid:28) (cid:2)(cid:29) if (cid:28) ≥ (cid:29) if (cid:28) (cid:29)

Original: [Table]
Revised: [Figure]

Revised: (cid:29)≤(cid:28)

Original: [Figure]
Revised: [Figure]

Revised: if (cid:28) (cid:2) (cid:19) (cid:28) (cid:2)(cid:28) (cid:29) if (cid:28) (cid:29)

Revised: [Figure]

Revised: [Figure]

Original: EDFDSQCVPTFENKVPEKQKGLPTCGETCTMDLLSLGWSS
Revised: ! (cid:2) ! (cid:3) ! (cid:10) . . . " (cid:4) " (cid:3) " (cid:2) . . .

Revised: (cid:1)#$%(cid:1)(cid:28) " (cid:17) (cid:17) (cid:2) &’ (cid:4) (cid:1) (cid:11) (cid:2) (cid:1) (cid:11) (cid:3) (cid:1) (cid:11) (cid:4) . . .

Original: [Figure]
Revised: [Figure]

Revised: EDFDSQCVPTFENKVPEKQKGLPTCGETCTMDLLSLGWSS

Revised: Anti-microbialPeptide Activity

Original:  space, discontinuous decision boundaries in the form of ‘Hyperboxes H ’ are created during model training [3].
Revised: Prediction [Figure] (CLN), which perform the classiﬁcation of the learned data using min-max hyperboxes [2]. In this space, discontinuous decision boundaries in the form of ‘Hyperboxes H ’ are created during model training [3].

Original: Deﬁning this decision boundary requires a min coordinate V
Revised: Deﬁning this decision boundary requires a min coordinate V

Original: j = ( v j 1 , v j 2 , ..., v jn ) , and a max coordinate W j = ( w j 1 , w j 2 , ..., w jn ) .
Revised: j = ( v j 1 , v j 2 , ..., v jn ) , and a max coordinate W j = ( w j 1 , w j 2 , ..., w jn ) .

Original: However, to reduce the number of model hyperparameters, a single parameter i.e., θ ∈ (0 , 1) is deﬁned which represents the hyperbox size.
Revised: However, to reduce the number of model hyperparameters, a single parameter i.e., θ ∈ (0 , 1) is deﬁned which represents the hyperbox size.

Original: During training, for the ﬁrst input, a point H is created.
Revised: During training, for the ﬁrst input, a point H is created.

Original: Subsequently, the model tries to accommodate remaining training samples { a h , C i } in the previously constructed hyperboxes belonging to the same class C i , provided the H size does not exceeds a speciﬁed maximum limit (‘ θ ’)
Revised: Subsequently, the model tries to accommodate remaining training samples { a h , C i } in the previously constructed hyperboxes belonging to the same class C i , provided the H size does not exceeds a speciﬁed maximum limit (‘ θ ’)

Original: Else, a new H is added to CLN , i.e., for a new training sample { a h , C i } , a hyperbox { b j , C j } is found such that C j = C i or C j = C o which has the highest membership value and satisfying- (1) θ max ≥ 1 n (cid:80) ni
Revised: Else, a new H is added to CLN , i.e., for a new training sample { a h , C i } , a hyperbox { b j , C j } is found such that C j = C i or C j = C o which has the highest membership value and satisfying- (1) θ max ≥ 1 n (cid:80) ni

Original: =1 (max( w ji , a hi ) − min( v ji , a hi )) , (2) b
Revised: =1 (max( w ji , a hi ) − min( v ji , a hi )) , (2) b

Original: j is not associated with any OCN / CCN and (3) if C i =
Revised: j is not associated with any OCN / CCN and (3) if C i =

Original: C 0 or C j = C 0 then µ j > 0 .
Revised: C 0 or C j = C 0 then µ j > 0 .

Original: Following, the min-max coordinates of b j are adjusted such that, V newji = min( V oldji , a hi ) ; W newji = max( W oldji , a hi ) , where i = 1 , 2 , ..., n , and third , if
Revised: Following, the min-max coordinates of b j are adjusted such that, V newji = min( V oldji , a hi ) ; W newji = max( W oldji , a hi ) , where i = 1 , 2 , ..., n , and third , if

Original: C j = C 0 and C i (cid:54)
Revised: C j = C 0 and C i (cid:54)

Original: =
Revised: =

Original: C 0 then C j = C i .
Revised: C 0 then C j = C i .

Original: Moreover, if no suitable b j is present then a novel point hyperbox H for class C i is created with V j = W j = a h .
Revised: Moreover, if no suitable b j is present then a novel point hyperbox H for class C i is created with V j = W j = a h .

Original: In CLNs, the neuron b j represents hyperbox fuzzy set i.e., =
Revised: In CLNs, the neuron b j represents hyperbox fuzzy set i.e., =

Original: A h , V j , W j , f ( A h , V j , W j ) ∀ ( A h ∈ I n ) .
Revised: A h , V j , W j , f ( A h , V j , W j ) ∀ ( A h ∈ I n ) .

Original: In the middle layer of the classiﬁer, the input nodes and the hyperbox nodes are connected together.
Revised: In the middle layer of the classiﬁer, the input nodes and the hyperbox nodes are connected together.

Original: These connections represent the min-max coordinates V and W of the n − dimensional hyperbox fuzzy set [2].
Revised: These connections represent the min-max coordinates V and W of the n − dimensional hyperbox fuzzy set [2].

Original: During training, the neurons in the middle layer are created dynamically.
Revised: During training, the neurons in the middle layer are created dynamically.

Original: Connection between the hyperbox node b j to a class node C j is represented by matrix U , where, u ij = 1 if b j ∈ C
Revised: Connection between the hyperbox node b j to a class node C j is represented by matrix U , where, u ij = 1 if b j ∈ C

Original: j else u ij = 0 .
Revised: j else u ij = 0 .

Original: In CLN nodes, to compute the class memberships, activation function by [4] is used to assign membership value = 1 when the test sample falls within the hyperbox.
Revised: In CLN nodes, to compute the class memberships, activation function by [4] is used to assign membership value = 1 when the test sample falls within the hyperbox.

Original: In other cases, when the test sample lies outside H , the model calculates the membership-value on the basis of its distance from the extreme coordinates of H .
Revised: In other cases, when the test sample lies outside H , the model calculates the membership-value on the basis of its distance from the extreme coordinates of H .

Original: Increasing the Fuzziness control parameter ( γ ) leads to more fuzzy classiﬁcation, while decreasing it leads to a crisp classiﬁcation.
Revised: Increasing the Fuzziness control parameter ( γ ) leads to more fuzzy classiﬁcation, while decreasing it leads to a crisp classiﬁcation.

Original: Decision Boundary Error Minimization.
Revised: Decision Boundary Error Minimization.

Original: Since the high dimensional feature space contains all the learned peptide features, a possible case of H overlap can occur.
Revised: Since the high dimensional feature space contains all the learned peptide features, a possible case of H overlap can occur.

Original: A reﬂex section is introduced which contains the Overlap Compensation Neurons ( OCN ) and Containment Compensation Neurons ( CCN ) which becomes active only when a case of hyperbox overlap and containment is encountered respectively [5].
Revised: A reﬂex section is introduced which contains the Overlap Compensation Neurons ( OCN ) and Containment Compensation Neurons ( CCN ) which becomes active only when a case of hyperbox overlap and containment is encountered respectively [5].

Original: If V ki < W ki < V ji < W ji or V ji < W ji < V ki < W ki is true for any i ∈ 1 , ..., n , then hyperboxes ( b k , b j ) are isolated.
Revised: If V ki < W ki < V ji < W ji or V ji < W ji < V ki < W ki is true for any i ∈ 1 , ..., n , then hyperboxes ( b k , b j ) are isolated.

Original: If the isolation condition does not hold, the feature space must be checked for containment, i.e., if V ki < V ji < W ji < W ji or V ji < V ki < W ki < W ji is true for any i ∈ 1 , ..., n , then Hyperboxes are contained.
Revised: If the isolation condition does not hold, the feature space must be checked for containment, i.e., if V ki < V ji < W ji < W ji or V ji < V ki < W ki < W ji is true for any i ∈ 1 , ..., n , then Hyperboxes are contained.

Original: In such case, a CCN node is formed dynamically.
Revised: In such case, a CCN node is formed dynamically.

Original: If hyperboxes are not contained, then an OCN node is created.
Revised: If hyperboxes are not contained, then an OCN node is created.

Original: Moreover to check for hyperbox overlap, initially the value of δ old is set as 1 .
Revised: Moreover to check for hyperbox overlap, initially the value of δ old is set as 1 .

Original: A set of 4 cases are formed in for overlap in which, if v ji < v ki < w ji < w ki , then δ new = min( w ji − v ki , δ old ) .
Revised: A set of 4 cases are formed in for overlap in which, if v ji < v ki < w ji < w ki , then δ new = min( w ji − v ki , δ old ) .

Original: Second, if v ki < v ji < w ki < w ji , then δ new = min( w ki − v ji , δ old ) .
Revised: Second, if v ki < v ji < w ki < w ji , then δ new = min( w ki − v ji , δ old ) .

Original: Third, if v ji < v ki ≤ w ki < w ji , then δ new = min(min( w ki − v ji , w ji − v ki ) ,
Revised: Third, if v ji < v ki ≤ w ki < w ji , then δ new = min(min( w ki − v ji , w ji − v ki ) ,

Original: δ old ) .
Revised: δ old ) .

Original: Fourth, v ki < v ji ≤ w ji < w ki , then δ new = min(min( w ki − v ji , w ji − v ki ) , δ old ) .
Revised: Fourth, v ki < v ji ≤ w ji < w ki , then δ new = min(min( w ki − v ji , w ji − v ki ) , δ old ) .

Original: If overlaps exist (i.e., one of the above condition is true) and ( δ new − δ old ) > 0 , then, ∆ =
Revised: If overlaps exist (i.e., one of the above condition is true) and ( δ new − δ old ) > 0 , then, ∆ =

Original: i else ∆ = − 1 [5].
Revised: i else ∆ = − 1 [5].

Original: For checking hyperbox containment, if overlap exists and is minimum along ∆ dimension, the hyperboxes are contracted using the following given conditions i.e., if v j ∆ < v k ∆ < w j ∆ < w k ∆ , then, v newk ∆ =
Revised: For checking hyperbox containment, if overlap exists and is minimum along ∆ dimension, the hyperboxes are contracted using the following given conditions i.e., if v j ∆ < v k ∆ < w j ∆ < w k ∆ , then, v newk ∆ =

Original: w newj ∆ =
Revised: w newj ∆ =

Original: ( w oldj ∆ + v oldk ∆ ) / 2 ; second, if, v k ∆ < v j ∆ < w k ∆ < w j ∆ , then v newk ∆ = w newj ∆ =
Revised: ( w oldj ∆ + v oldk ∆ ) / 2 ; second, if, v k ∆ < v j ∆ < w k ∆ < w j ∆ , then v newk ∆ = w newj ∆ =

Original: ( w oldk ∆ + v oldj ∆ ) / 2 ; third, v k ∆ < v j ∆ ≤ w j ∆ < w k ∆ and w k ∆ − v j ∆ < w j ∆ − v k ∆ , then v newj ∆ = w oldk ∆ else w newj ∆ = v oldk ∆ ; fourth, v j ∆ < v k ∆ ≤ w k ∆ < w j ∆ and w k ∆ − v j ∆ < w j ∆ − v k ∆ , then w newj ∆ = v oldk ∆ else v newj ∆ = w oldk ∆ [5].
Revised: ( w oldk ∆ + v oldj ∆ ) / 2 ; third, v k ∆ < v j ∆ ≤ w j ∆ < w k ∆ and w k ∆ − v j ∆ < w j ∆ − v k ∆ , then v newj ∆ = w oldk ∆ else w newj ∆ = v oldk ∆ ; fourth, v j ∆ < v k ∆ ≤ w k ∆ < w j ∆ and w k ∆ − v j ∆ < w j ∆ − v k ∆ , then w newj ∆ = v oldk ∆ else v newj ∆ = w oldk ∆ [5].

Original: The Reﬂex mechanism is biologically inspired from that of the human brain which takes control of the body in hazardous conditions unconsciously.
Revised: The Reﬂex mechanism is biologically inspired from that of the human brain which takes control of the body in hazardous conditions unconsciously.

Original: The Reﬂex mechanism helps in obtaining more explainable class memberships with high accuracy.
Revised: The Reﬂex mechanism helps in obtaining more explainable class memberships with high accuracy.

Original:  Experimental Settings
Revised:  Experimental Settings

Original: Dataset.
Revised: Dataset.

Original: Dataset was taken from [1, 6] to access the performance of the proposed model.
Revised: Dataset was taken from [1, 6] to access the performance of the proposed model.

Original: We calculated amino acid features based on its composition, following which, the redundant peptide sequence samples with > 0 .
Revised: We calculated amino acid features based on its composition, following which, the redundant peptide sequence samples with > 0 .

Original: 99 similarity were removed from the dataset.
Revised: 99 similarity were removed from the dataset.

Original: Baseline Model Training.
Revised: Baseline Model Training.

Original: The work is implemented using Keras [7] with Tensorﬂow [8] as backend.
Revised: The work is implemented using Keras [7] with Tensorﬂow [8] as backend.

Original: Nvidia K80 GPU with 12 GB RAM workbench was used for conducting the experiments.
Revised: Nvidia K80 GPU with 12 GB RAM workbench was used for conducting the experiments.

Original: Various state-of-the-art ML classiﬁers are implemented on the same dataset to compare the classiﬁcation results.
Revised: Various state-of-the-art ML classiﬁers are implemented on the same dataset to compare the classiﬁcation results.

Original: ‘Accuracy’ was used as the metric for optimizing the hyperparameters used for training.
Revised: ‘Accuracy’ was used as the metric for optimizing the hyperparameters used for training.

Original: ‘zscore’ was used as the normalization method which is calculated as z = ( x − u ) /s .
Revised: ‘zscore’ was used as the normalization method which is calculated as z = ( x − u ) /s .

Original: ‘yeo-johnson’ transformation was applied while training the ML classiﬁers for comparison.
Revised: ‘yeo-johnson’ transformation was applied while training the ML classiﬁers for comparison.

Original: Evaluation Metrics.
Revised: Evaluation Metrics.

Original: Accuracy along with Matthews Correlation Coefﬁcient (MCC) is used as the evaluation metric.
Revised: Accuracy along with Matthews Correlation Coefﬁcient (MCC) is used as the evaluation metric.

Original: MCC ∈ [ − 1 , 1] accounts for true and false positives and negatives, and is widely considered as a fair metric that can be applied to datasets with imbalanced classes and limited training samples [9].
Revised: MCC ∈ [ − 1 , 1] accounts for true and false positives and negatives, and is widely considered as a fair metric that can be applied to datasets with imbalanced classes and limited training samples [9].

Original: A perfect forecast has a coefﬁcient of +1 , an average random prediction has a coefﬁcient of 0 and an inverse prediction has a coefﬁcient of − 1 .
Revised: A perfect forecast has a coefﬁcient of +1 , an average random prediction has a coefﬁcient of 0 and an inverse prediction has a coefﬁcient of − 1 .

Original:  Experiments, Results and Discussion
Revised:  Experiments, Results and Discussion

Original: Exp-01: Limited Data Subset Conﬁguration.
Revised: Exp-01: Limited Data Subset Conﬁguration.

Original: In the ﬁrst set of experiments, the model is trained on limited data subset conﬁgurations, i.e., n = 300 , 200 , 100 , where n is the number of samples.
Revised: In the ﬁrst set of experiments, the model is trained on limited data subset conﬁgurations, i.e., n = 300 , 200 , 100 , where n is the number of samples.

Original: The obtained results are compared with state-of-the-art ML models trained on the same number of data samples (see Table 01).
Revised: The obtained results are compared with state-of-the-art ML models trained on the same number of data samples (see Table 01).

Original: Here, accuracy and MCC were used as the evaluation metrics for performance comparison.
Revised: Here, accuracy and MCC were used as the evaluation metrics for performance comparison.

Original: Further, it is to be noted that the same data samples, feature selection, and pre-processing techniques are used while implementing the baseline models for a fair comparison of results.
Revised: Further, it is to be noted that the same data samples, feature selection, and pre-processing techniques are used while implementing the baseline models for a fair comparison of results.

Original: From Table 1, it is evident that the proposed model outperforms all other models for n = 300 and 200 .
Revised: From Table 1, it is evident that the proposed model outperforms all other models for n = 300 and 200 .

Original: This demonstrates the strong ability of the model to achieve high performance on limited datasets.
Revised: This demonstrates the strong ability of the model to achieve high performance on limited datasets.

Original: Moreover, most ML models demonstrate inconsistent performance with variations in dataset size.
Revised: Moreover, most ML models demonstrate inconsistent performance with variations in dataset size.

Original: Though Support Vector Machine achieves comparable accuracy (although a low
Revised: Though Support Vector Machine achieves comparable accuracy (although a low

Original: MCC score than the proposed model) for n = 300 , its performance drops sharply for n = 200 , with an MCC reduction of ≈ 40% .
Revised: MCC score than the proposed model) for n = 300 , its performance drops sharply for n = 200 , with an MCC reduction of ≈ 40% .

Original: In the case of stochastic gradient descent (SGD), the model has low performance ( 57% accuracy and a low MCC score of 0 . 15 ) on n = 300 .
Revised: In the case of stochastic gradient descent (SGD), the model has low performance ( 57% accuracy and a low MCC score of 0 . 15 ) on n = 300 .

Original: However, for
Revised: However, for

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: n = 100 , the metric suddenly increases to 90% accuracy with a 0 .
Revised: n = 100 , the metric suddenly increases to 90% accuracy with a 0 .

Original: 82 MCC score.
Revised: 82 MCC score.

Original: This is due to the model over-ﬁtting on limited training data.
Revised: This is due to the model over-ﬁtting on limited training data.

Original: Furthermore, it is seen that as the number of training samples varies, even the second-best performing model loses consistency, i.e., for n = 300 , SVM is the second best-performed model, its performance decreases for n = 200 where the Random
Revised: Furthermore, it is seen that as the number of training samples varies, even the second-best performing model loses consistency, i.e., for n = 300 , SVM is the second best-performed model, its performance decreases for n = 200 where the Random

Original: Forest takes its position and similarly for n = 100 , SGD performs the best (which was ironically 7 th best-performed model for n = 300 ).
Revised: Forest takes its position and similarly for n = 100 , SGD performs the best (which was ironically 7 th best-performed model for n = 300 ).

Original: This illustrates that no single ML model exhibits robust and consistent performance while training on limited data.
Revised: This illustrates that no single ML model exhibits robust and consistent performance while training on limited data.

Original: On the contrary, the proposed model has consistent performance, demonstrated by a fairly constant metric on all subset conﬁgurations of limited data, compared to other models that either over-ﬁt or fail to classify the complex feature space on small training data.
Revised: On the contrary, the proposed model has consistent performance, demonstrated by a fairly constant metric on all subset conﬁgurations of limited data, compared to other models that either over-ﬁt or fail to classify the complex feature space on small training data.

Original: Exp-02: Imbalanced Data Subset Conﬁguration.
Revised: Exp-02: Imbalanced Data Subset Conﬁguration.

Original: Most antimicrobial peptide activity prediction datasets suffer from high-class imbalance (example, 75% − 65% of positive class distribution) since positive samples are difﬁcult to obtain compared to the negative ones.
Revised: Most antimicrobial peptide activity prediction datasets suffer from high-class imbalance (example, 75% − 65% of positive class distribution) since positive samples are difﬁcult to obtain compared to the negative ones.

Original: Most deep learning and ML models exhibit a reduction in performance on high class-imbalance.
Revised: Most deep learning and ML models exhibit a reduction in performance on high class-imbalance.

Original: To demonstrate the model’s robustness to class imbalance, we compare the model performance on three data subset conﬁgurations:
Revised: To demonstrate the model’s robustness to class imbalance, we compare the model performance on three data subset conﬁgurations:

Original: ﬁrst with a low imbalance with Imb = 0 .
Revised: ﬁrst with a low imbalance with Imb = 0 .

Original: 40 (i.e., 40% of all samples represent positives), second with high skewness of 0 . 30 and third with severely imbalanced class distribution with just 20% of all samples being positives.
Revised: 40 (i.e., 40% of all samples represent positives), second with high skewness of 0 . 30 and third with severely imbalanced class distribution with just 20% of all samples being positives.

Original: The obtained results (see Table 02) are compared with ML models trained on the same subset conﬁguration.
Revised: The obtained results (see Table 02) are compared with ML models trained on the same subset conﬁguration.

Original: Here, accuracy along with MCC is considered a better evaluation metric than accuracy alone.
Revised: Here, accuracy along with MCC is considered a better evaluation metric than accuracy alone.

Original: Lack of consistency is also evident on imbalanced data conﬁguration, with the SGD being the worst performed model for Imb = 0 .
Revised: Lack of consistency is also evident on imbalanced data conﬁguration, with the SGD being the worst performed model for Imb = 0 .

Original: 30 , while the same demonstrating second-best performance on the subset with Imb = 0 .
Revised: 30 , while the same demonstrating second-best performance on the subset with Imb = 0 .

Original: On the other hand, the proposed model shows consistent performance over all subset conﬁgurations.
Revised: On the other hand, the proposed model shows consistent performance over all subset conﬁgurations.

Original:  Conclusion and Future Work
Revised:  Conclusion and Future Work

Original: In this study, we present a fuzzy approach towards Antimicrobial peptide activity prediction.
Revised: In this study, we present a fuzzy approach towards Antimicrobial peptide activity prediction.

Original: The study demonstrates the effectiveness and consistency of the proposed model compared to currently used state-of-the-art ML models.
Revised: The study demonstrates the effectiveness and consistency of the proposed model compared to currently used state-of-the-art ML models.

Original: The presented model is capable of training on sparse datasets with high-class imbalance (as demonstrated through experiments). The proposed model signiﬁcantly outperforms state-of-the-art ML models trained on the same data. 
Revised: The presented model is capable of training on sparse datasets with high-class imbalance (as demonstrated through experiments) and signiﬁcantly outperforms state-of-the-art ML models trained on the same data. [Figure]

Original: The obtained results establish the model’s effectiveness and quantify that fuzzy classiﬁer-based models are more suited towards problems where the dataset is highly imbalanced or limited.
Revised: The obtained results establish the model’s effectiveness and quantify that fuzzy classiﬁer-based models are more suited towards problems where the dataset is highly imbalanced or limited.

Original: Currently, the present study is focused on amino acid compositions that consider the entire peptide sequence as the feature.
Revised: Currently, the present study is focused on amino acid compositions that consider the entire peptide sequence as the feature.

Original: In the future, we plan to investigate the encoding percentage composition frequency of dipeptide, tripeptide, etc.
Revised: In the future, we plan to investigate the encoding percentage composition frequency of dipeptide, tripeptide, etc.