KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Jia, Yuran; Huang, Shan; Zhang, Tianjiao

doi:10.3389/fgene.2021.811158

Cited by 13 publications

(5 citation statements)

References 81 publications

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“…Images capture color and texture information from the material surface. These three types of information are fused using a feature set fusion method (Jia et al 2021). Features are extracted from each data source, and they are combined to form larger feature vectors.…”

Section: Information Fusion Methodsmentioning

confidence: 99%

Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

Yang,

Li,

et al. 2024

BioRes

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The quality detection of alfalfa hay is crucial for the development of animal husbandry. In this study, a method for quality detection of alfalfa hay based on the fusion of multisource information including near-infrared spectroscopy, image processing techniques, and electronic nose is proposed. After SG convolution smoothing, feature wavelengths were extracted using Competitive Adaptive Re-weighting Scheme and Successive Projections Algorithm from the spectral data. The image data were denoised using adaptive wavelet thresholding, and color and texture features were extracted using color histograms and random forest algorithms, respectively. Electronic nose data using principal component analysis was used for data dimensionality reduction. Support Vector Machine, Extreme Learning Machine, and Multi-Layer Perceptron were employed to establish quality detection models of alfalfa hay based on spectroscopy, image, gas information, and their combination, respectively. Experimental results demonstrate that the fusion of near-infrared spectroscopy, image data, and gas information effectively enhances the classification accuracy of the model. The accuracy of the test set reaches 100%, with root mean square error and determination coefficient values of 0.1728 and 0.9239, respectively, surpassing prediction models established solely on individual information. This study provides new insights into alfalfa hay quality detection.

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Section: Information Fusion Methodsmentioning

confidence: 99%

Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

Yang,

Li,

et al. 2024

BioRes

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“…In this way, multiple weak learners together develop a strong learner that demonstrates enhanced generalization capabilities, and reduces model bias and variance [41, 53]. Ensemble learning has been widely utilized in a variety of sequence analysis tasks, such as identification of Nitration and Succinylation in proteins [3, 4, 28], RNA Pseudouridine detection [21], DNA Methylcytosine prediction [22, 24], DNA-binding protein identification [39], prediction of cell-penetrating proteins [29, 30], anti-cancer protein detection [35], and lncRNA-protein interaction prediction [37]. Based on working paradigms, ensemble learning approaches can be broadly categorized into 3 different classes, namely, Bagging, Stacking and Boosting.…”

Section: Methodsmentioning

confidence: 99%

“…Feature fusion is the process of combining data from several contexts into a single entity that can enhance discriminative information and improves the performance of a computational model. Various feature fusion approaches have been utilized to improve the performance of diverse sequence analysis tasks such as protein sub-cellular localization prediction [36], RNA-protein interaction prediction [37], DNA-binding protein identification [39], and prediction of novel RNA transcripts [38].…”

Section: Feature Fusion and Selectionmentioning

confidence: 99%

“…Feature selection is the process of removing redundant, irrelevant and noisy features that can degrade the performance of predictors. Various feature selection approaches have been utilized in diverse Protein sequence analysis studies, such as, identification of Phosphorylation sites [26], Pupylation sites [27], Succinylation sites [28], cell-penetrating proteins [30], Methylation sites [31], anti-cancer proteins [32] and DNA binding proteins [39]. In the identification of Tyrosine nitration sites also, multiple studies have demonstrated the importance of feature selection methods to build robust computational models.…”

Section: Feature Fusion and Selectionmentioning

confidence: 99%

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NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

Datta

Asim

Dengel

et al. 2023

Preprint

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Post-translational modifications (PTMs) either enhance a protein activity in various sub-cellular processes, or degrade their activity which leads towards failure of intracellular processes. Tyrosine nitration (NT) modification degrades proteins activity that initiate and propagate various diseases including Neurodegenerative, Cardiovascular, Autoimmune diseases, and Carcinogenesis. Identification of NT modification support development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming, and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches remain fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. The paper in hand presents NTpred framework competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, that is utilized to train a Logistic Regression classifier. On BD1 benchmark dataset, the proposed framework outperform existing best performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on BD2 benchmark dataset, the proposed framework outperform existing best performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC.

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“…Furthermore, Harini et al in 2022 created a database named ProNAB for DNA and protein complexes [ 46 ]. Jia et al, in 2021, proposed KKDBP, a classifier for the prediction of DNA-binding proteins using multiple PSSM feature fusions and random forest as a classifier [ 47 ]. In 2021, Hu et al proposed TargetDBP+, which performed prediction of DNA-binding proteins using five convolutional features and SVM classifier [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

Barukab

Khan

et al. 2022

Applied Bionics and Biomechanics

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In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

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KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Cited by 13 publications

References 81 publications

Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

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