Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

Abstract

Background: Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes.

RD-Connect: An Integrated Platform Connecting Databases, Registries, Biobanks and Clinical Bioinformatics for Rare Disease Research

“…For this reason, participating resources are being offered the opportunity to make use of linked data and semantic web approaches: computational standards and methods that enable them to make their data more accessible and interoperable. 28 …”

Dedalo: Looking for Clusters Explanations in a Labyrinth of Linked Data

“…Hypothesis generation is defined as "the pre-decisional process by which it is possible to formulate explanations and beliefs regarding the occurrences observed in a specific environment" [20]. Systems presented in the literature can be classified according to different dimensions: (i) manual or automatic, (ii) domain-specific or domain-independent and (iii) ontology-or Linked Data-driven.…”

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

“…Reaching beyond biomedical data integration including the scientific literature, recent visionary developments propose to expose results and findings early on as factual statements in a fixed format (“nanopublications”, “proto-ontologies”, “microparadigms”) and where any data set should have the potential to be referenced and reused electronically from any world-wide access point (digital object identifiers, DOIs for data) [11]-[14]. The representation of the data either follows data formats or requires meta-data for the correct annotation of its origins and experimental settings, but then contributes to the generation and evaluation of hypotheses [15], [16].…”