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:  HDF enrichment in the four predicted interaction sets 
1. Abstract of Cited Paper (BIB001):  Human Immunodeficiency Viruses (HIV-1 and HIV-2) rely upon host-encoded proteins to facilitate their replication. Here, we combined genome-wide siRNA analyses with interrogation of human interactome databases to assemble a host-pathogen biochemical network containing 213 confirmed host cellular factors and 11 HIV-1-encoded proteins. Protein complexes that regulate ubiquitin conjugation, proteolysis, DNA-damage response, and RNA splicing were identified as important modulators of early-stage HIV-1 infection. Additionally, over 40 new factors were shown to specifically influence the initiation and/or kinetics of HIV-1 DNA synthesis, including cytoskeletal regulatory proteins, modulators of posttranslational modification, and nucleic acid-binding proteins. Finally, 15 proteins with diverse functional roles, including nuclear transport, prostaglandin synthesis, ubiquitination, and transcription, were found to influence nuclear import or viral DNA integration. Taken together, the multiscale approach described here has uncovered multiprotein virus-host interactions that likely act in concert to facilitate the early steps of HIV-1 infection. 
2. Abstract of Cited Paper (BIB002):  Human immunodeficiency virus (HIV)-1 depends on the host cell machinery to support its replication. To discover cellular factors associated with HIV-1 replication, we conducted a genome-scale siRNA screen, revealing more than 311 host factors, including 267 that were not previously linked to HIV. Surprisingly, there was little overlap between these genes and the HIV dependency factors described recently. However, an analysis of the genes identified in both screens revealed overlaps in several of the associated pathways or protein complexes, including the SP1/mediator complex and the NF-kappaB signaling pathway. cDNAs for a subset of the identified genes were used to rescue HIV replication following knockdown of the cellular mRNA providing strong evidence that the following six genes are previously uncharacterized host factors for HIV: AKT1, PRKAA1, CD97, NEIL3, BMP2K, and SERPINB6. This study highlights both the power and shortcomings of large scale loss-of-function screens in discovering host-pathogen interactions. 
3. Abstract of Cited Paper (BIB003):  Motivation: Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. ::: ::: Results: The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. ::: ::: Availability: A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org ::: ::: Contact: [email protected] 
4. Abstract of Cited Paper (BIB004):  Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set of interacting human and viral proteins would greatly contribute to our understanding of the mechanisms of infection and subsequently to the design of new therapeutic approaches. This work is the first attempt to predict the global set of interactions between HIV-1 and human host cellular proteins. We propose a supervised learning framework, where multiple information data sources are utilized, including cooccurrence of functional motifs and their interaction domains and protein classes, gene ontology annotations, posttranslational modifications, tissue distributions and gene expression profiles, topological properties of the human protein in the interaction network and the similarity of HIV-1 proteins to human proteins’ known binding partners. We trained and tested a Random Forest (RF) classifier with this extensive feature set. The model’s predictions achieved an average Mean Average Precision (MAP) score of 23%. Among the predicted interactions was for example the pair, HIV-1 protein tat and human vitamin D receptor. This interaction had recently been independently validated experimentally. The rank-ordered lists of predicted interacting pairs are a rich source for generating biological hypotheses. Amongst the novel predictions, transcription regulator activity, immune system process and macromolecular complex were the top most significant molecular function, process and cellular compartments, respectively. Supplementary material is available at URL www.cs.cmu.edu/~oznur/hiv/hivPPI.html 
5. Abstract of Cited Paper (BIB005):  Identifying protein-protein interactions (PPI's) is critical for understanding virtually all cellular molecular mechanisms. Previously, predicting PPI's was treated as a binary classification task and has commonly been solved in a supervised setting which requires a positive labeled set of known PPI's and a negative labeled set of non-interacting protein pairs. In those methods, the learner provides the likelihood of the predicted interaction, but without a confidence level associated with each prediction. Here, we apply a conformal prediction framework to make predictions and estimate confidence of the predictions. The conformal predictor uses a function measuring relative 'strangeness' interacting pairs to check whether prediction of a new example added to the sequence of already known PPI's would conform to the 'exchangeability' assumption: distribution of interacting pairs is invariant with any permutations of the pairs. In fact, this is the only assumption we make about the data. Another advantage is that the user can control a number of errors by providing a desirable confidence level. This feature of CP is very useful for a ranking list of possible interactive pairs. In this paper, the conformal method has been developed to deal with just one class - class interactive proteins - while there is not clearly defined of 'non-interactive'pairs. The confidence level helps the biologist in the interpretation of the results, and better assists the choices of pairs for experimental validation. We apply the proposed conformal framework to improve the identification of interacting pairs between HIV-1 and human proteins.

Literature Review:
We have also studied these four works in the context of enrichment of HDFs in their predicted sets. For this we gathered 275 HDFs from the study done by Brass et al. BIB001 , 296 HDFs from the study done by Konig et al. BIB002 and 375 HDFs from the study done by Zhou et al. BIB003 . There were 908 unique HDFs in the union of these sets. We measure the proportion of overlaps between the predicted set of Tastan et al., Doolitle et al., Dyer et al. and Mukhopadhyay et al. with these HDFs set. In Figure 16 the overlaps between the predicted interaction sets for each of these four works and the three HDF sets are shown. For this, we intersect the human proteins in the predicted interaction set with three HDF sets individually and measure the proportion of the human proteins that belong to these HDF sets. We find that a large proportion of human proteins in the predicted set of Doolittle et al. belongs to each of the experimentally predicted HDF sets. Figure 17 also shows the proportion of overlapped human proteins in the interaction sets that belong to the union of all three HDF sets. Overall assessment of all predicted sets using conformal approach As the predicted interaction sets are dependent on the corresponding methodologies used for prediction, it is somewhat unfair to draw conclusion regarding the superiority of any method based on simply comparing them. Still to get an overview of the quality of the predicted sets, we have made an attempt to compare the predicted sets. For the purpose of a comprehensive comparison, we take the conformal prediction approach as proposed in BIB006 . It uses 35 features collected from Tastan et al. BIB005 and assigns a confidence level to each predicted pair. We use this rich feature set because it integrates most of the physical properties of the proteins. Moreover, for all possible interaction pairs the values of corresponding features are available in BIB006 . Usually conformal prediction is more concerned with two or more classes. However, in BIB006 conformal prediction is used to deal with one defined class, which consists of experimentally validated interaction pairs. Here the Non-Conformity Measure deals with the feature sets and a P-value is assigned to individual predicted interaction to validate how likely it is with respect to the previously defined interactions. More is the P-value of an interaction, more it is likely to occur. Here we find P-values of all predicted interactions of each of the four studies and plot the proportion of interactions against the P-values. Figure 18 shows column plots of predicted interactions with P-values for each of the four studies, separately. From this figure it is clear that the prediction sets of Tastan et al. BIB004 .05 and 56.50%, respectively). Note that this is expected because the 35 features used in the conformal prediction were collected from Tastan et al. BIB005 . For lack of better alternative, we use this approach for comparing the predictions.