# Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1
## 1 Introduction
### Related Work  
There are several approaches in the literature targeting the prediction of TF binding sites. In [17], a tool was developed named \"DRAF\" for the prediction of transcription factor binding sites (TFBSs). The tool improves prediction accuracy of previous models that were based on position weight matrices (PWMs). It combines features from Transcription Factor Binding Sites (TFBS) sequences and physicochemical properties of TF DNA binding into classification algorithms of machine learning. Specifically, it uses Random Forest to make predictions. The authors tested their tools against other classification algorithms, namely neural network, Support Vector Machine (SVM) and Gaussian mixture regression; however, Random Forest provided the best accuracy results. In [18], a method was developed to predict TFBS using three features: nucleotide composition, nucleotides distribution and the transition between nucleotides. This method was implemented using two SVM classification models; each one was tested on a different dataset. The accuracies of the model were 81.84% and 82.27%. Moreover, a back propagation neural network was trained to classify the SP1 TFBS on human chromosome1 [19]. The proposed neural network consisted of two hidden layers. The input layer consisted of twenty-eight neurons and the output layer consisted of two neurons to predict whether the input is a binding site or not. The authors compared the results of their trained neural network with those of other classification models, namely the SVM, Linear Discriminant Analysis (LDA) and K-Nearest Neighbors (KNN) on the same dataset. It was shown that the neural network outperformed the other classification models with an accuracy of 84.4%. The work in [20] also presents a fragment-based prediction method, which splits a binding sequence into overlapping pentamers (5 base pairs) to calculate interaction energy. Their algorithm shows improved efficiency and accuracy, especially for long binding sites. In [21], a deep learning model combines both a Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM). They also proposed a new encoding scheme to represent nucleotide positions. The method presented was tested on several datasets, and the accuracy reached 80%. [PERSON] et al. use CNNs in [22] to present a multi-task learning framework. Their method is mainly concerned with unlabeled transcription factor binding sites data. The performance of the proposed method shows a 5.7% performance improvement over other compared methods in terms of the Area Under Curve (AUC). PhyloReg was presented in [23]. PhyloReg is a semi-supervised learning method used to better train earlierdeep learning models. The approach presents increases in prediction accuracy. In [24], the attention mechanism in deep learning is employed to develop DeepGRN. The results obtained show that DeepGRN achieves higher unified scores in 6 out of 13 targets than the compared methods. In addition, the importance of histone modifications and chromatin accessibility in the identification of TFBS along with DNA sequence was studied in [25]. A Convolutional Neural Network (CNN) model was developed to combine these new features for cell-specific TFBSs prediction. The developed model was compared with other classification techniques: Logistic Regression, KNN, Random Forest and gradient boosted regression trees. Combining the three features along with CNN produced the best results. In this paper, multiple machine learning classification algorithms were used to enhance the accurate prediction of TF binding sites of SP1 on human chromosome 1. In addition, voting and boosting algorithms were proposed to enhance the prediction performance. The results obtained in this paper show an enhanced performance for predicting TF binding sites, with an AUC equals 0.97 using KNN and 0.95 using the proposed voting technique. Moreover, the accuracy obtained by both KNN and the proposed voting technique are comparable, with 92% using KNN, and 88.1% using voting.