scholarly journals Reducing Dimensionality in Remote Homology Detection

2021 ◽  
Vol 9 (12) ◽  
pp. 1052-1054
Author(s):  
S. Dinesh
Keyword(s):  
Protein Sequence ◽  
Protein Sequences ◽  
High Dimensional ◽  
Homology Detection ◽  
Protein Families ◽  
Classification Techniques ◽  
Remote Homology ◽  
High Dimensional Datasets ◽  
Remote Homology Detection

Abstract: Homology detection plays a major role in bioinformatics. Different type of methods is used for Homology detection. Here we extract the information from protein sequences and then uses the various algorithm to predict the similarity between protein families. SVM most commonly used the algorithm in homology detection. Classification techniques are not suitable for homology detection because theyare not suitable for high dimensional datasets. Soreducing the higher dimensionality is very important than easily can predict the similarity of protein families. Keywords: Homology detection, Protein, Sequence, Reducing dimensionality, BLAST, SCOP.

Cascaded walks in protein sequence space: use of artificial sequences in remote homology detection between natural proteins

2012 ◽  
Vol 8 (8) ◽  
pp. 2076 ◽  
Author(s):  
S. Sandhya ◽  
R. Mudgal ◽  
C. Jayadev ◽  
K. R. Abhinandan ◽  
R. Sowdhamini ◽  
...  
Keyword(s):  
Sequence Space ◽  
Protein Sequence ◽  
Space Use ◽  
Homology Detection ◽  
Remote Homology ◽  
Protein Sequence Space ◽  
Remote Homology Detection

scholarly journals Sequence-Order Frequency Matrix - Sampling and Machine learning with Smith-Waterman (SOFM-SMSW) for Protein Remote Homology Detection

2021 ◽  
Author(s):  
Sajithra Nakshathram ◽  
Ramyachitra Duraisamy ◽  
Manikandan Pandurangan
Keyword(s):  
Machine Learning ◽  
Structural Alignment ◽  
Protein Sequences ◽  
Support Vector ◽  
Local Alignment ◽  
Homology Detection ◽  
Remote Homology ◽  
Matrix Sampling ◽  
N Gram ◽  
Remote Homology Detection

Abstract Background: Protein Remote Homology Detection (PRHD) is used to find the homologous proteins which are similar in function and structure but sharing low sequence identity. In general, the Sequence-Order Frequency Matrix (SOFM) was used for protein remote homology detection. In the SOFM Top-n-gram (SOFM-Top) algorithm, the probability of substrings was calculated based on the highest probability value of substrings. Moreover, SOFM-Smith Waterman (SOFM-SW) algorithm combines the SOFM with local alignment for protein remote homology detection. However, the computation complexity of SOFM based PRHD is high since it processes all protein sequences in SOFM.Objective: Sequence-Order Frequency Matrix - Sampling and Machine learning with Smith-Waterman (SOFM-SMSW) algorithm is proposed for predicting the protein remote homology. The SOFM-SMSW algorithm used the PVS method to select the optimum target sequences based on the uniform distribution measure.Method: This research work considers the most important sequences for PRHD by introducing Proportional Volume Sampling (PVS). After sampling the protein sequences, a feature vector is constructed and labeling is performed based on the concatenation between two protein sequences. Then, a substitution score which represents the structural alignment is learned using k-Nearest Neighbor (k-NN). Based on the learned substitution score and alignment score, the protein homology is detected using Smith-Waterman algorithm and Support Vector Machine (SVM). By selecting the most important sequences, the accuracy of PRHD is improved and the computational complexity for PRHD is reduced by using structural alignment along with the local alignment.Results: The performance of the proposed SOFM-SMSW algorithm is tested with SCOP database and it has been compared with various existing algorithms such as SVM Top-N-gram, SVM pairwise, GPkernal, Long Short-Term Memory (LSTM), SOFM Top-N-gram and SOFM-SW. Conclusion: The experimental results illustrate that the proposed SOFM-SMSW algorithm has better accuracy, precision, recall, ROC and ROC 50 for PRHD than the other existing algorithms.

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