A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed: A Special Focus on Glycosylation, Acetylation, Methylation, Hydroxylation, and Ubiquitination.

Posttranslational modifications (PTMs) of proteins impart a significant role in human cellular functions ranging from localization to signal transduction. Hundreds of PTMs act in a human cell. Among them, only the selected PTMs are well established and documented. PubMed includes thousands of papers on the selected PTMs, and it is a challenge for the biomedical researchers to assimilate useful information manually. Alternatively, text mining approaches and machine learning algorithm automatically extract the relevant information from PubMed. Protein phosphorylation is a well-established PTM and several research works are under way. Many existing systems are there for protein phosphorylation information extraction. A recent approach uses a hybrid approach using text mining and machine learning to extract protein phosphorylation information from PubMed. Some of the other common PTMs that exhibit similar features in terms of entities that are involved in PTM process, that is, the substrate, the enzymes, and the amino acid residues, are glycosylation, acetylation, methylation, hydroxylation, and ubiquitination. This has motivated us to repurpose and extend the text mining protocol and machine learning information extraction methodology developed for protein phosphorylation to these PTMs. In this chapter, the chemistry behind each of the PTMs is briefly outlined and the text mining protocol and machine learning algorithm adaption is explained for the same.

Extracting Significant Comorbid Diseases from MeSH Index of PubMed.

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.

Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.

Genes and proteins form the basis of all cellular processes and ensure a smooth functioning of the human system. The diseases caused in humans can be either genetic in nature or may be caused due to external factors. Genetic diseases are mainly the result of any anomaly in gene/protein structure or function. This disruption interferes with the normal expression of cellular components. Against external factors, even though the immunogenicity of every individual protects them to a certain extent from infections, they are still susceptible to other disease-causing agents. Understanding the biological pathway/entities that could be targeted by specific drugs is an essential component of drug discovery. The traditional drug target discovery process is time-consuming and practically not feasible. A computational approach could provide speed and efficiency to the method. With the presence of vast biomedical literature, text mining also seems to be an obvious choice which could efficiently aid with other computational methods in identifying drug-gene targets. These could aid in initial stages of reviewing the disease components or can even aid parallel in extracting drug-disease-gene/protein relationships from literature. The present chapter aims at finding drug-gene interactions and how the information could be explored for drug interaction.