# Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1
## 4 Discussion  
Transcription factors have a major role in the identification of the TFBSs. The identification of TFBS should lead to the understanding of transcriptional gene regulation. This is considered to be one of the challenges in computational biology as not all of the TFBS have been identified yet.  
In this paper, the prediction of transcription factor binding sites of SP1 on Human Chromosome1 is carried out using eleven different classification models, in addition to a proposed voting technique and a boosting technique. The performance of the different classification techniques was measured by calculating their accuracy, specificity, sensitivity and AUC of precision-recall. The KNN, SVM, Extra Trees and Random Forest produced the best results. Their accuracies were 92%, 86.2%, 86.8% and 86.8%, respectively, while their AUCs were 97%, 95%, 94% and 94%, respectively.  
The proposed suggested model ensembles two classification models, namely SVM and MLP, into a voting algorithm. Voting was implemented using soft voting (i.e., the average probability of their results was taken as the ensembled model's final prediction). The final accuracy and AUC achieved for the proposed model were 88.1% and 95%, respectively. The results show that the KNN classification algorithm solely outperformed the ensemble algorithm. The problem with KNN is the dependence of performance accuracy and the quality of data, and with large datasets, such as transcription factor binding site data, the performance decreases and requires high memory. Moreover, KNN requires data to be scaled with all features being relevant to the prediction process. The proposed voting technique achieves a comparable accuracy to KNN and is more suitable for high dimensional data, with lots of possible noise as the case with the transcription factor binding sites data. It is known that bioinformatics data are inherently noisy, and machine learning algorithms are sensitive to this noise. Noise in the data arises from the laboratory techniques used, which are prone to human errors [27]. This noise can be overcome by multiple attempts of the laboratory experiments to ensure that the observations obtained are correct.  
Moreover, the highest accuracy recorded for KNN is 92%, while it is 88.1% for the proposed voting algorithm. These results outperform the work in the literature which reported the accuracy. However, although the work done is performed on different datasets, they all target the TFBS problem. In [18], SVM was used and obtained 81.84% and 82.27% on two datasets. Moreover, neural networks achieved an accuracy of 84.4% in [19], while deep learning reached only an accuracy of 80% in [21]. This shows that the proposed work achieves promising results in the classification of TFBS, specifically, SP1 on human chromosome 1.  
Furthermore, the dataset used in this study contained labeled sequences all of the same length, and all sequences were encoded. However, in the case of having missing labels or different lengths sequences, preprocessing methods can be employed. Regarding the issue of different lengths, padding of sequences could be done to unify the lengths before performing classification, where a dummy nucleotide value is used to append  
Figure 1: Precision-Recall Curve for all Classifiers.  
shorter sequences, then the sequences can go through the encoding stage. For the missing labels, sequences could be either deleted, or the missing values could be imputed.  
This work can be further experimented upon using a larger dataset, as well as other ensemble algorithms. Moreover, the running time performance of the algorithms could be tested against different datasets.