Protein Secondary Structure Prediction using Hybrid Recurrent Neural Networks

Ema, Romana Rahman; Khatun, Akhi; Hossain, Md. Alam; Akhond, Mostafijur Rahman; Hossain, Nazmul; Arafat, Md. Yasir

doi:10.3844/jcssp.2022.599.611

Cited by 6 publications

(5 citation statements)

References 19 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As described by Graves et al [14], the Bi-LSTM network can access both past (via forward states) and future input features (via backward states) for a given time. Bi-LSTM has been applied in protein sequence identification, but the accuracy rate is minimal [15][16][17][18].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier

Roslidar,

Brilianty,

Alhamdi

et al. 2024

IJEEI

View full text Add to dashboard Cite

The primary nutrient that is crucial for identifying biochemical processes and biological norms in living cells is protein. Proteins are usually centered around one or a few functions which are defined by their family type. Hence, identification and classification are needed to separate the proteins according to their structure and families. In this work, we built a model to classify families of protein sequences. We used the protein sequences dataset consists of various macromolecules of biological significance. The classifier is built up using deep learning of Bi-LSTM. We began the research by collecting the dataset from the Protein Data Bank of the Research Collaboratory for Structural Bioinformatics, pre-processing the data using tokenizing, and modeling the classifier based on deep learning network of Bi-LSTM. As we get the best accuracy rate of the trained model, we figure out the model performance using the evaluation metrics of learning curve, accuracy rate, and loss. The results show that Deep Bi-LSTM provides excellent performance with fit learning curve, 99% accuracy rate, and 0.042 loss.

show abstract

Section: Recurrent Neural Networkmentioning

confidence: 99%

Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier

Roslidar,

Brilianty,

Alhamdi

et al. 2024

IJEEI

View full text Add to dashboard Cite

show abstract

“…The [14] investigates hybrid RNN models for protein secondary structure prediction, a key task in determining protein configurations from amino acid sequences. This study introduced a Hybrid Recurrent Neural Networks (HRNN) approach, incorporating GRU, LSTM, and their bidirectional variants, BGRU and BLSTM, within a two-dimensional RNN (2D-RNN) framework.…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

Babichev,

Liakh,

Kalinina

2024

IEEE Access

View full text Add to dashboard Cite

This manuscript explores the application of deep learning (DL) techniques for classifying gene expression data. A key aspect of our research is the comparative analysis of various DL neural network architectures, including Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Recurrent Neural Networks (RNN), as well as hybrid models that combine these networks. We applied the Bayesian optimization algorithm using 5-fold cross-validation for optimal hyperparameter tuning, which is crucial for DL algorithm performance. Significantly, we have advanced the methods for applying RNNs in processing gene expression data, particularly focusing on LSTM and GRU types. Our study introduces also a novel hybrid quality criterion for data classification, calculated as a weighted sum of partial quality criteria, incorporating an integrated F1-score derived through the Harrington desirability method. Furthermore, we investigate hybrid models that leverage various DL methods, enhancing decision-making objectivity in sample identification. This model uses a step-by-step information processing procedure, initially applying different DL models to gene expression data and subsequently processing these through a CART-based classifier for final decision-making. Our experiments, performed on gene expression data from patients with eight cancer types and one subset with normal samples (without cancer), demonstrated that GRU-RNN-based models, particularly a two-layer GRU-RNN, achieved the highest classification efficacy, with an accuracy of 97.8% on the test dataset. The performance of this model exceeded that of other models, whose accuracy varied between 96.6% and 97.3%. Comparative analysis with other studies in this field suggests that the proposed techniques demonstrate higher efficacy compared to similar research regarding the application of DL models for cancer-type diagnosis.

show abstract

“…Proteins are the building blocks of amino acid sequences [1] [2]. Generally, there are four types of protein structure's which are primary, secondary, tertiary, and quadratic structure.…”

Section: Introductionmentioning

confidence: 99%

“…The regular secondary structure has two types, including α -helices (H) and β-sheet (E) and the irregular secondary structure has more types, including tight turns, Random coils, Bulges, etc. By using only their basic structure, the PSSP method is a series of bioinformatics technique aimed at predicting the secondary structure of protein sequences or residues [2] [3]. In molecular biology, the most essential and crucial problems are the prediction of the protein secondary structures using machine learning approaches [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Protein Secondary Structure Prediction based on CNN and Machine Learning Algorithms

Ema¹,

Khatun²,

Adnan³

et al. 2022

IJACSA

View full text Add to dashboard Cite

One of the most important topics in computational biology is protein secondary structure prediction. Primary, secondary, tertiary, and quaternary structure are the four levels of complexity that can be used to characterize the entire structure of a protein that are totally ordered by the amino acid sequences. The polypeptide backbone of a protein's local configuration is referred to as a secondary structure. In this paper, three prediction algorithms have been proposed which will predict the protein secondary structure based on machine learning. These prediction methods have been improved by the model structure of convolutional neural networks (CNN). The Rectified Linear Units (ReLU) has been used as the activation function. The 2D CNN has been trained with machine learning algorithms, including Support Vector Machine, Naive Bays and Random Forest. The SVM is used to correctly classify the unseen data. Naïve Bays (NB) and Random Forest (RF) are also applied to solve the prediction problems for not only classification problems but also regression problems. The 2D CNN, hybrid of 2D CNN -SVM, CNN-RF and CNN-NB have been proposed in this experiment. These different methods are implemented with the RS126, 25PDB and CB513 dataset. Further, all prediction Q3 accuracy is compared and improved with their datasets.

show abstract

Protein Secondary Structure Prediction using Hybrid Recurrent Neural Networks

Cited by 6 publications

References 19 publications

Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier

Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier

Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

Protein Secondary Structure Prediction based on CNN and Machine Learning Algorithms

Contact Info

Product

Resources

About