A bio-sequence k-mer frequency counter (kFC)

Biji, C. L.; Nair, Achuthsankar S.; Madhu, Manu K; Vijayakumar, R.

doi:10.1109/cimca.2014.7057822

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The first feature extraction method is the k -mer nucleotide frequency. ,,− For a given DNA sequence, we calculated all possible k -mer nucleotides. In the experiment, the value of k is from 1 to 4.…”

Section: Methodsmentioning

confidence: 99%

Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families

Wang

Tang

et al. 2020

J. Chem. Inf. Model.

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Transcription factors (TFs) play a crucial role in controlling key cellular processes and responding to the environment. Yeast is a single-cell fungal organism that is a vital biological model organism for studying transcription and translation in basic biology. The transcriptional control process of yeast cells has been extensively calculated and studied using traditional methods and high-throughput technologies. However, the identities of transcription factors that regulate major functional categories of genes remain unknown. Due to the avalanche of biological data in the post-genomic era, it is an urgent need to develop automated computational methods to enable accurate identification of efficient transcription factor binding sites from the large number of candidates. In this paper, we analyzed high-resolution DNA-binding profiles and motifs for TFs, covering all possible contiguous 8-mers. First, we divided all 8-mer motifs into 16 various categories and selected all sorts of samples from each category by setting the threshold of E-score. Then, we employed five feature representation methods. Also, we adopted a total of four feature selection methods to filter out useless features. Finally, we used Extreme Gradient Boosting (XGBoost) as our base classifier and then utilized the one-vs-rest tactics to build 16 binary classifiers to solve this multiclassification problem. In the experiment, our method achieved the best performance with an overall accuracy of 79.72% and Mathew’s correlation coefficient of 0.77. We found the similarity relationship among each category from different TF families and obtained sequence motif schematic diagrams via multiple sequence alignment. The complexity of DNA recognition may act as an important role in the evolution of gene regulation. Source codes are available at .

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Section: Methodsmentioning

confidence: 99%

Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families

Wang

Tang

et al. 2020

J. Chem. Inf. Model.

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“…The polygonization step proposed in this study is an outstanding innovation in matching equal-length feature vectors required by classifiers, and polygon encoding provides the features of a polygon shape. By grouping PoCs, such as k-mers, in bio-sequences [48], normalization can be easily performed. The time required for the classification process can be adjusted according to these requirements.…”

Section: Steps To Create a Feature Vectormentioning

confidence: 99%

Polygonized Silhouettes and Polygon Coding Based Feature Representation for Human Action Recognition

Göçmen,

Akata

2023

IEEE Access

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The characteristics of human silhouette shape can be used for action recognition and classification. In this paper, a novel feature extraction method for the silhouette-based classification of human actions in videos is proposed. The proposed method is based on polygonization of silhouette images and coding. Since conventional silhouette generation methods do not satisfy the integrity of silhouettes, Yolact++ is modified as a silhouette generator. Our innovative approach Yolact++ masks are used as silhouettes to overcome this problem. For this purpose, a new image form called Poly Silhouette (PoS), a new Polygonization (PoG) algorithm and a new Polygon Coding (PoC) algorithm have been developed. The polygonization step is based on, but is not similar to curve and image polygonization. It is fast, adaptable, and accurate on the contour coordinates of the PoS images. PoCs were generated by projecting each edge vector generated from the corner coordinates of the PoS onto the angular areas and codes for the PoS were formed. These codes are grouped into k-mers similar to genetic algorithms and are used as features. The proposed innovative feature extraction method guarantees that feature vectors of equal length are generated from any action video. Thus, no additional action is required to overcome the dimensionality problem. By using different k-mer lengths, the classification accuracy of the method versus computation time was analyzed and depicted in figures. The method developed was tested on HMDB51 & UCF101 datasets: for SVM 20.98%, 1.63% for k-NN 4.96%, 6.83%, respectively, and significant improvements were achieved.INDEX TERMS Polygonized silhouettes, polygon coding, human action recognition.

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A bio-sequence k-mer frequency counter (kFC)

Cited by 2 publications

References 5 publications

Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families

Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families

Polygonized Silhouettes and Polygon Coding Based Feature Representation for Human Action Recognition

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