AFP-SRC: identification of antifreeze proteins using sparse representation classifier

Uddin

Ali

et al. 2023

J. Chem. Inf. Model.

The development of intracellular ice in the bodies of cold-blooded living organisms may cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero temperature environments. Additionally, AFPs are implemented in biotechnological, industrial, agricultural, and medical fields. Machine learning-based predictors were presented for AFP identification. However, more accurate predictors are still highly desirable for boosting the AFP prediction. This work presents a novel approach, named AFP-SPTS, for the correct prediction of AFPs. We explored the discriminative features with four schemes, namely, dipeptide deviation from the expected mean (DDE), reduced amino acid alphabet (RAAA), grouped dipeptide composition (GDPC), and a novel representative method, called pseudo-position-specific scoring matrix tri-slicing (PseTS-PSSM). Considering the advantages of ensemble learning strategy, we fused each feature vector into different combinations and trained the models with five machine learning algorithms, i.e., multilayer perceptron (MLP), extremely randomized tree (ERT), decision tree (DT), random forest (RF), and AdaBoost. Among all models, PseTS-PSSM + RAAA with an extremely randomized tree attained the best outcomes. The proposed predictor (AFP-SPTS) boosted the accuracies of AFPs in the literature by 1.82 and 4.1%.

Section: Resultsmentioning

confidence: 99%

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Uddin

Ali

et al. 2023

J. Chem. Inf. Model.

“…Light GBM is implemented for model training and prediction. Light GBM was first introduced by Microsoft 15 . Compared with GBDTs, Decision Tree, and Random Forest, Light GBM has many advantages such as early stopping, bagging, regularization, multiple loss functions, parallel training, and sparse optimization 16 .…”

Section: Methodsmentioning

confidence: 99%

“…They used autoencoder with Composition of K-spaced amino acid pairs and achieved a balanced accuracy of 0.903 14 . In another work, Usman et al constructed AFP-SRC improved method 15 . Similarly, PoGB-pred approach is developed by Alim et al They employed PseAAC, AAC, and DPC as feature descriptors and PCA for reducing the feature dimension 16 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of antifreeze proteins using machine learning

Uddin

Ali

et al. 2022

Sci Rep

Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.

“…The protein features are made compatible with the machine learning algorithms by encoding them numerically. Several encoding schemes have been utilized by the researchers in accordance with the adopted machine learning method [13,[15][16][17]. In this study, we use a well known feature encoding method called the composition of k-spaced amino acid pairs (CKSAAP).…”

Section: Features and Latent Space Encodingmentioning

confidence: 99%

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features

Usman

Park

et al. 2021

CIMB

Self Cite

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.