Systematic drug repurposing through text mining.

Drug development remains a time-consuming and highly expensive process with high attrition rates at each stage. Given the safety hurdles drugs must pass due to increased regulatory scrutiny, it is essential for pharmaceutical companies to maximize their return on investment by effectively extending drug life cycles. There have been many effective techniques, such as phenotypic screening and compound profiling, which identify new indications for existing drugs, often referred to as drug repurposing or drug repositioning.

Mining gene-centric relationships from literature: the roles of gene mutation and gene expression in supporting drug discovery.

Identifying drug target candidates is an important task for early development throughout the drug discovery process. This process is supported by the development of new high-throughput technologies that enable better understanding of disease mechanism. It becomes critical to facilitate effective analysis of the large amount of biological data.

Identifying novel drug indications through automated reasoning.

Background: With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.

A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions.

Motivation: Genetic factors determine differences in pharmacokinetics, drug efficacy, and drug responses between individuals and sub-populations. Wrong dosages of drugs can lead to severe adverse drug reactions in individuals whose drug metabolism drastically differs from the "assumed average". Databases such as PharmGKB are excellent sources of pharmacogenetic information on enzymes, genetic variants, and drug response affected by changes in enzymatic activity.

Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.

Motivation: Identifying drug-drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs.

Efficient extraction of protein-protein interactions from full-text articles.

Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant papers, evidence for physical interactions, and proper identifiers for each protein involved.

Synthesis of pharmacokinetic pathways through knowledge acquisition and automated reasoning.

Biological pathways are seen as highly critical in our understanding of the mechanism of biological functions. To collect information about pathways, manual curation has been the most popular method. However, pathway annotation is regarded as heavily time-consuming, as it requires expert curators to identify and collect information from different sources.

Querying parse tree database of Medline text to synthesize user-specific biomolecular networks.

Curated biological knowledge of interactions and pathways is largely available from various databases, and network synthesis is a popular method to gain insight into the data. However, such data from curated databases presents a single view of the knowledge to the biologists, and it may not be suitable to researchers' specific needs. On the other hand, Medline abstracts are publicly accessible and encode the necessary information to synthesize different kinds of biological networks.

Fuzzy c-means clustering with prior biological knowledge.

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes.

Mining gene-disease relationships from biomedical literature: weighting protein-protein interactions and connectivity measures.

Motivation: The promises of the post-genome era disease-related discoveries and advances have yet to be fully realized, with many opportunities for discovery hiding in the millions of biomedical papers published since. Public databases give access to data extracted from the literature by teams of experts, but their coverage is often limited and lags behind recent discoveries. We present a computational method that combines data extracted from the literature with data from curated sources in order to uncover possible gene-disease relationships that are not directly stated or were missed by the initial mining.