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:  Category-3: association rule mining-based approaches 
1. Abstract of Cited Paper (BIB001):  Association rule mining and bi-clustering are data mining tasks that have become very popular in many application domains, particularly in bioinformatics. However, to our knowledge, no algorithm was introduced for performing these two tasks in one process. We propose a new approach called FIST for extracting bases of extended association rules and conceptual bi-clusters conjointly. This approach is based on the frequent closed itemsets framework and requires a unique scan of the database. It uses a new suffix tree based data structure to reduce memory usage and improve the extraction efficiency, allowing parallel processing of the tree branches. Experiments conducted to assess its applicability to very large datasets show that FIST memory requirements and execution times are in most cases equivalent to frequent closed itemsets based algorithms and lower than frequent itemsets based algorithms.

Literature Review:
In Mukhopadhyay et al.  , an attempt was made to predict interactions based on association rule mining technique. Here the well-known Apriori  algorithm is used for generating association rules between human proteins interacting with HIV-1 proteins. Some novel interactions are then predicted using those rules. Besides the prediction task, the discovered rules describe an association between different human proteins in the context of HIV-1 interaction. The main advantage of using the association rule mining BIB002 technique for prediction purpose, is that it does not need any negative samples for prediction unlike classification-based approaches, where predictions are highly BIB001 Protein structure information is integrated for prediction of PPIs. The main idea behind this technique is that given a set of proteins with defined structures and associated interactions, proteins with similar structures or substructures with them will tend to share the same interaction partners. An association rule mining technique based on biclustering is used for predicting PPIs. Predictions are defined from the association rules that are generated using a novel biclustering-based association rule mining technique.