Dataset Information

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.

ABSTRACT: Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

SUBMITTER: Saqi M

PROVIDER: S-EPMC6556902 | biostudies-other | 2019 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.

Saqi Mansoor M Lysenko Artem A Guo Yi-Ke YK Tsunoda Tatsuhiko T Auffray Charles C

Briefings in bioinformatics 20190301 2

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of mo ...[more]

PMID: 29684165

Similar Datasets

Project description:BackgroundA number of compelling candidate Alzheimer's biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully.MethodsThe methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer's disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level.ResultsThree interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these 'emerging' potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail.ConclusionsSystematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.

Project description:Through selective attention, decision-makers can learn to ignore behaviorally irrelevant stimulus dimensions. This can improve learning and increase the perceptual discriminability of relevant stimulus information. Across cognitive models of categorization, this is typically accomplished through the inclusion of attentional parameters, which provide information about the importance assigned to each stimulus dimension by each participant. The effect of these parameters on psychological representation is often described geometrically, such that perceptual differences over relevant psychological dimensions are accentuated (or stretched), and differences over irrelevant dimensions are down-weighted (or compressed). In sensory and association cortex, representations of stimulus features are known to covary with their behavioral relevance. Although this implies that neural representational space might closely resemble that hypothesized by formal categorization theory, to date, attentional effects in the brain have been demonstrated through powerful experimental manipulations (e.g., contrasts between relevant and irrelevant features). This approach sidesteps the role of idiosyncratic conceptual knowledge in guiding attention to useful information sources. To bridge this divide, we used formal categorization models, which were fit to behavioral data, to make inferences about the concepts and strategies used by individual participants during decision-making. We found that when greater attentional weight was devoted to a particular visual feature (e.g., "color"), its value (e.g., "red") was more accurately decoded from occipitotemporal cortex. We also found that this effect was sufficiently sensitive to reflect individual differences in conceptual knowledge, indicating that occipitotemporal stimulus representations are embedded within a space closely resembling that formalized by classic categorization theory. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Dataset Information

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.

Publications

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets