Life science knowledge is complex, large-scale and heterogeneous; causing information overload for many researchers and an interesting set of computational challenges for informatics systems designers, developers and curators. The published literature is growing exponentially providing a textual corpus consisting of over twenty million documents. Publications’ supplemental data have few enforced formatting or semantic standards that support e?ective data sharing. Finally, there are increasing numbers of biomedical informatics resources becoming available (databases, ontologies, controlled vocabularies, etc.), each serving a di?erent purpose and increasing the level of heterogeneity within the data landscape whilst also providing valuable diversity of function for users.
The overload problem is particularly important in neuroscience (and neuroinformatics) due to the subject’s interdisciplinary nature involving multiple sub?elds: molecular and cellular biology, genetics, neurophysiology, clinical imaging, neuroanatomy, computer modeling, psychology, arti?cial intelligence, etc.. We here ask how computational methods can be used across these available resources (the literature, online databases, laboratory data) to alleviate the impact of overload, leverage the available diversity of resources and ultimately facilitate discovery?
This research topic aims to tackle this challenge in neuroscience by exploring novel approaches to computational capture and utilization of emergent semantics implied by biomedical texts and data. The approaches should be explicitly motivated by facilitation of discoveries in neuroscience by means of automated or semi-automated formulation and veri?cation of scienti?c hypotheses and models. As a non-exclusive list, we expect submissions to involve (A) identi?cation of important concepts in neuroscience literature and data; (B) automated interlinking of the concepts through lexical analysis, or the use of formal semantics; (C) identi?cation of complex domain-speci?c relationships beyond mere similarities; (D) utilizing relationships between concepts to formulate machine-readable and actionable research questions, (E) automated veri?cation of questions and intuitive presentation of the results to neuroscientists, etc.
We encourage contributions especially (but not only) from the following ?elds applied to neuroscience information :
• text mining,
• question answering,
• distributional semantics,
• formal semantics,
• ontology engineering,
• biomedical knowledge modeling,
• information integration,
• graph analysis,
• analytical data processing,
• data visualisation,
• intelligent user interfaces.
Submissions may combine approaches from multiple disciplines and should demonstrate practical applicability to the formulation and veri?cation of hypothesis in neuroscience.
Life science knowledge is complex, large-scale and heterogeneous; causing information overload for many researchers and an interesting set of computational challenges for informatics systems designers, developers and curators. The published literature is growing exponentially providing a textual corpus consisting of over twenty million documents. Publications’ supplemental data have few enforced formatting or semantic standards that support e?ective data sharing. Finally, there are increasing numbers of biomedical informatics resources becoming available (databases, ontologies, controlled vocabularies, etc.), each serving a di?erent purpose and increasing the level of heterogeneity within the data landscape whilst also providing valuable diversity of function for users.
The overload problem is particularly important in neuroscience (and neuroinformatics) due to the subject’s interdisciplinary nature involving multiple sub?elds: molecular and cellular biology, genetics, neurophysiology, clinical imaging, neuroanatomy, computer modeling, psychology, arti?cial intelligence, etc.. We here ask how computational methods can be used across these available resources (the literature, online databases, laboratory data) to alleviate the impact of overload, leverage the available diversity of resources and ultimately facilitate discovery?
This research topic aims to tackle this challenge in neuroscience by exploring novel approaches to computational capture and utilization of emergent semantics implied by biomedical texts and data. The approaches should be explicitly motivated by facilitation of discoveries in neuroscience by means of automated or semi-automated formulation and veri?cation of scienti?c hypotheses and models. As a non-exclusive list, we expect submissions to involve (A) identi?cation of important concepts in neuroscience literature and data; (B) automated interlinking of the concepts through lexical analysis, or the use of formal semantics; (C) identi?cation of complex domain-speci?c relationships beyond mere similarities; (D) utilizing relationships between concepts to formulate machine-readable and actionable research questions, (E) automated veri?cation of questions and intuitive presentation of the results to neuroscientists, etc.
We encourage contributions especially (but not only) from the following ?elds applied to neuroscience information :
• text mining,
• question answering,
• distributional semantics,
• formal semantics,
• ontology engineering,
• biomedical knowledge modeling,
• information integration,
• graph analysis,
• analytical data processing,
• data visualisation,
• intelligent user interfaces.
Submissions may combine approaches from multiple disciplines and should demonstrate practical applicability to the formulation and veri?cation of hypothesis in neuroscience.
