Transcription factors (TFs), as the key regulatory elements of gene transcription, can activate or suppress the transcription by binding to specific sets of DNA sequences. In the past, the introduction of ChIP-seq sequencing technologies provides immense opportunities for precise categorization of TF binding sites. In this talk, we will introduce several novel computational models for integrative analysis of the accumulated ChIP-seq data. Firstly, due to cost, time or sample material availability, it is not always possible for researchers to obtain ChIP-seq data for every TF in every sample of interest, which considerably limits the power of integrative studies. To tackle this problem, we propose Local Sensitive Unified Embedding (LSUE) for imputing new ChIP-seq datasets. Secondly, we construct gene regulatory networks in 13 human tissues by integrating large-scale transcription factor (TF)-gene regulations with gene and protein expression data. By comparing these regulatory networks, it was found that many tissue-specific regulations that are important for tissue identity. In particular, the tissue-specific TFs are found to regulate more genes than those expressed in multiple tissues, and the processes regulated by these tissue-specific TFs are closely related to tissue functions. Therefore, recognizing tissue specific regulatory networks can help better understand the molecular mechanisms underlying diseases and identify new disease genes.