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 Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases 
Chapter Title:  Text Mining 
1. Abstract of Cited Paper (BIB001):  Biomedical literature is traditionally used as a way to inform scientists of the relevance of genes in relation to a research topic. However many genes, especially from poorly studied organisms, are not discussed in the literature. Moreover, a manual and comprehensive summarization of the literature attached to the genes of an organism is in general impossible due to the high number of genes and abstracts involved. We introduce the novel Genie algorithm that overcomes these problems by eval- uating the literature attached to all genes in a genome and to their orthologs according to a selected topic. Genie showed high precision (up to 100%) and the best performance in comparison to other algorithms in most of the benchmarks, especially when high sensitivity was required. Moreover, the prioritization of zebrafish genes involved in heart development, using human and mouse orthologs, showed high en- richment in differentially expressed genes from microarray experiments. The Genie web server sup- ports hundreds of species, millions of genes and offers novel functionalities. Common run times below a minute, even when analyzing the human genome with hundreds of thousands of literature records, allows the use of Genie in routine lab work. Availability: http://cbdm.mdc-berlin.de/tools/genie/. 
2. Abstract of Cited Paper (BIB002):  Background ::: Answering questions such as "Which genes are related to breast cancer?" usually requires retrieving relevant publications through the PubMed search engine, reading these publications, and creating gene lists. This process is not only time-consuming, but also prone to errors. 
3. Abstract of Cited Paper (BIB003):  PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. 
4. Abstract of Cited Paper (BIB004):  BackgroundSufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information.ResultsThe ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types.ConclusionThe use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine.

Literature Review:
Yet another way of establishing putative associations between genes, diseases and other biological entities is text mining of biomedical literature. Many gene prioritization tools utilize the results of co-occurrence based text mining, assuming that frequent colocalization of two entities in biomedical texts points to their possible interaction. More sophisticated pattern-based text-mining methods use advanced weighting schemes to assign qualities to predicted associations BIB004 , BIB001 . Other text-mining systems, e.g. ANDsystem BIB005 , apply natural language processing (NLP) algorithms allowing to differentiate between various kinds of biological entities and associations between them. Some gene prioritization tools consider text mining-inferred associations together with curated and predicted associations from other evidence sources when some of them (e.g. Génie BIB002 , GLAD4U BIB003 ) completely rely on the text-mining results. The major drawbacks of text mining methods are the high rate of false positives and the lack of accuracy in the detection of associations and determination of their types. Despite that, text mining remains the only way to absorb the whole volume of relevant scientific literature, impossible to handle manually.