MotifVoter: a novel ensemble method for fine-grained integration of generic motif finders.

MOTIVATION: Locating transcription factor binding sites (motifs) is a key step in understanding gene regulation. Based on Tompa's benchmark study, the performance of current de novo motif finders is far from satisfactory (with sensitivity

Towards ontology-driven navigation of the lipid bibliosphere.

BACKGROUND: The indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge both in enterprise and academic domains. The emergence of semantic web technologies and their fusion with artificial intelligence techniques has provided a new toolkit with which to address these data integration challenges. In the emerging field of lipidomics such navigation challenges are barriers to the translation of scientific results into actionable knowledge, critical to the treatment of diseases such as Alzheimer's syndrome, Mycobacterium infections and cancer. RESULTS: We present a literature-driven workflow involving document delivery and natural language processing steps generating tagged sentences containing lipid, protein and disease names, which are instantiated to custom designed lipid ontology. We describe the design challenges in capturing lipid nomenclature, the mandate of the ontology and its role as query model in the navigation of the lipid bibliosphere. We illustrate the extent of the description logic-based A-box query capability provided by the instantiated ontology using a graphical query composer to query sentences describing lipid-protein and lipid-disease correlations. CONCLUSION: As scientists accept the need to readjust the manner in which we search for information and derive knowledge we illustrate a system that can constrain the literature explosion and knowledge navigation problems. Specifically we have focussed on solving this challenge for lipidomics researchers who have to deal with the lack of standardized vocabulary, differing classification schemes, and a wide array of synonyms before being able to derive scientific insights. The use of the OWL-DL variant of the Web Ontology Language (OWL) and description logic reasoning is pivotal in this regard, providing the lipid scientist with advanced query access to the results of text mining algorithms instantiated into the ontology. The visual query paradigm assists in the adoption of this technology.

Ontology-centric integration and navigation of the dengue literature.

Uninhibited access to the unstructured information distributed across the web and in scientific literature databases continues to be beyond the reach of scientists and health professionals. To address this challenge we have developed a literature driven, ontology-centric navigation infrastructure consisting of a content acquisition engine, a domain-specific ontology (in OWL-DL) and an ontology instantiation pipeline delivering sentences derived by domain-specific text mining. A visual query tool for reasoning over A-box instances in the populated ontology is presented and used to build conceptual queries that can be issued to the knowledgebase. We have deployed this generic infrastructure to facilitate data integration and knowledge sharing in the domain of dengue, which is one of the most prevalent viral diseases that continue to infect millions of people in the tropical and subtropical regions annually. Using our unique methodology we illustrate simplified search and discovery on dengue information derived from distributed resources and aggregated according to dengue ontology. Furthermore we apply data mining to the instantiated ontology to elucidate trends in the mentions of dengue serotypes in scientific abstracts since 1974.

A workflow for mutation extraction and structure annotation.

Rich information on point mutation studies is scattered across heterogeneous data sources. This paper presents an automated workflow for mining mutation annotations from full-text biomedical literature using natural language processing (NLP) techniques as well as for their subsequent reuse in protein structure annotation and visualization. This system, called mSTRAP (Mutation extraction and STRucture Annotation Pipeline), is designed for both information aggregation and subsequent brokerage of the mutation annotations. It facilitates the coordination of semantically related information from a series of text mining and sequence analysis steps into a formal OWL-DL ontology. The ontology is designed to support application-specific data management of sequence, structure, and literature annotations that are populated as instances of object and data type properties. mSTRAPviz is a subsystem that facilitates the brokerage of structure information and the associated mutations for visualization. For mutated sequences without any corresponding structure available in the Protein Data Bank (PDB), an automated pipeline for homology modeling is developed to generate the theoretical model. With mSTRAP, we demonstrate a workable system that can facilitate automation of the workflow for the retrieval, extraction, processing, and visualization of mutation annotations -- tasks which are well known to be tedious, time-consuming, complex, and error-prone. The ontology and visualization tool are available at (http://datam.i2r.a-star.edu.sg/mstrap).

Diabetes of the brain: computational approaches and interventional strategies.

Diabetes mellitus (DM) is characterized by hyperglycemia either due to deficient insulin production (Type 1 Diabetes mellitus) or peripheral insulin resistance of the cells (Type 2 Diabetes mellitus). Both Type 1 Diabetes mellitus and Type 2 Diabetes mellitus are more prevalent and efforts are directed to actively control these metabolic syndromes. Currently, Alzheimer's disease (AD), is gaining popularity as 'Type 3 diabetes' or 'Diabetes of the brain' and it is now evident that this neurodegenerative disease has multiple shared pathology with DM. Alarming is the fact that the incidence of AD might double within the next two decades, and this is certain to cause devastating effects not only to the afflicted individual or the family, but also to the global economy. Methods to either delay the onset or inhibit the progression of AD are therefore necessary. Progressive dementia, increased deposition of amyloid- ß protein, neurofibrillary tangles and neuritic plaques in the brain are some of the hallmarks of AD. More understanding of the disease at the cellular and molecular level will enable identifying the possible targets for intervention and pave way for either development of novel or modification of the existing therapeutic options. In this work we have performed semantic data mining analysis on a large collection of most recently published data and identified an updated list of common genes expressed in DM and AD. Functional analysis of these genes revealed both existing and missing links involved in a bigger network associated with both disease conditions. Thus we argue that computational analysis methods help not only in understanding the mechanistic links but also in narrowing down precise targets (genes, proteins, metabolites and signalling pathways) and provide the base for both disease intervention and development of therapeutic options.

Mining to find the lipid interaction networks involved in Ovarian Cancers.

The role of lipids in cancer during the genesis, progression and subsequent metastasis stages is increasingly discussed in the scientific literature. This information is discussed in a wide range of journals making it difficult for researchers to track the latest developments. A comprehensive assessment and translation of the lipidome of ovarian cancer, originating from literature, has yet to be made. We illustrate the deployment of semantic technologies; lipid ontology and text mining, in the aggregation and coordination of lipid literature. We provide the first report on the roles and types of lipids involved in ovarian cancer based on the mining of literature and identify key lipid-protein interactions that may point to potential drug discovery targets.