You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions 
Chapter Title:  A review on HIV-1-human PPI prediction techniques 
1. Abstract of Cited Paper (BIB001):  Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them. 
2. Abstract of Cited Paper (BIB002):  Protein-protein interactions (PPIs) play a critical role in many cellular functions. A number of experimental techniques have been applied to discover PPIs; however, these techniques are expensive in terms of time, money, and expertise. There are also large discrepancies between the PPI data collected by the same or different techniques in the same organism. We therefore turn to computational techniques for the prediction of PPIs. Computational techniques have been applied to the collection, indexing, validation, analysis, and extrapolation of PPI data. This chapter will focus on computational prediction of PPI, reviewing a number of techniques including PIPE, developed in our own laboratory. For comparison, the conventional large-scale approaches to predict PPIs are also briefly discussed. The chapter concludes with a discussion of the limitations of both experimental and computational methods of determining PPIs. 
3. Abstract of Cited Paper (BIB003):  A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area.

Literature Review:
PPIs are regarded as the main biochemical reactions in cells, which determine different biological processes, organized in the cell. There exit several literatures that assess different methodologies used for predicting PPIs BIB003 BIB004 BIB002 . Most of the approaches predominantly focus on the prediction of PPIs in a single organism such as yeast or human. Here we discuss several computational methods that concentrate on the HIV-1-human PPI prediction. We also evaluate the applicability of different methods and perform different analysis to compare those methods at the system level. Computational methods for prediction of possible viral-host interactions are one of the major tasks in PPI research for antiviral drug discovery and treatment optimization. Predicting PPIs between viral and host proteins has contributed substantial knowledge to the drug design area. Recently, PPI prediction has been regarded as a promising alternative to the traditional approach to drug design BIB001 . Having knowledge about the interaction pattern provides a great opportunity to understand pathogenesis mechanisms, and thus supports the development of drugs. There are different approaches available in the literature that exploit different methodologies for predicting PPIs between HIV-1 and human proteins. Here, we have classified these approaches into three categories depending on the methodologies, which have been used in those literatures. In the first category, we collect the works that use classification-based approaches for detection of PPIs. In the second category, we describe a work that mainly depends on the structural similarities of HIV-1 and human proteins. Here we also describe a work that uses the short eukaryotic linear motifs (ELMs)  on HIV-1 proteins and human protein counter domains (CDs) information for predicting interactions between HIV-1 and human proteins. Recently association rule mining techniques are gaining popularity in this domain and are established to be a useful alternative in the field of PPIs prediction. We mark these works as the third category. The classification of these approaches is shown in Table 2 .