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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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MOLIERE: Automatic Biomedical Hypothesis Generation System.

Justin Sybrandt1, Michael Shtutman2, Ilya Safro1

  • 1Clemson University, School of Computing, Clemson SC, USA.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|February 13, 2018
PubMed
Summary
This summary is machine-generated.

MOLIERE aids biomedical researchers by generating hypotheses from over 24.5 million documents, uncovering implicit connections between genes, diseases, and diagnoses using a novel network approach.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Scientific Discovery

Background:

  • Hypothesis generation accelerates biomedical research by identifying hidden links between concepts.
  • Existing systems often rely on limited, domain-specific data.
  • There is a need for comprehensive, large-scale data analysis for hypothesis generation.

Purpose of the Study:

  • To introduce MOLIERE, a novel system for hypothesis generation.
  • To leverage a vast corpus of over 24.5 million documents for biomedical insights.
  • To demonstrate the effectiveness of a multi-modal, multi-relational network approach.

Main Methods:

  • Constructed a multi-modal and multi-relational network of biomedical entities (papers, genes, proteins, diseases, diagnoses) from heterogeneous National Center for Biotechnology Information (NCBI) datasets.
  • Utilized Latent Dirichlet Allocation on abstracts near shortest paths within the network to model hypotheses.
  • Applied MOLIERE to historical data for hypothesis generation.

Main Results:

  • Successfully generated hypotheses by analyzing connections within a large-scale biomedical network.
  • Demonstrated the effectiveness of the MOLIERE approach on historical biomedical data.
  • The MOLIERE network, implementation, and data are publicly available.

Conclusions:

  • MOLIERE offers a powerful, scalable solution for biomedical hypothesis generation.
  • The system's foundation on a comprehensive network enables discovery of implicit relationships.
  • Public availability of MOLIERE promotes broader scientific collaboration and advancement.