Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new modes of investigation in which LLMs can be used to reframe classic neuroscience questions to deliver fresh answers.
View Article and Find Full Text PDFHere, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) → intermediate event(s) → clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.
View Article and Find Full Text PDFThe conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery.
View Article and Find Full Text PDFEthnopharmacological Relevance: Parkinson's disease (PD) is a neurodegenerative disorder characterized by a loss of dopaminergic neurons in the substantia nigra pars compacta and the presence in surviving neurons of Lewy body inclusions enriched with aggregated forms of the presynaptic protein α-synuclein (aSyn). Although current therapies provide temporary symptomatic relief, they do not slow the underlying neurodegeneration in the midbrain. In this study, we analyzed contemporary herbal medicinal practices used by members of the Lumbee tribe to treat PD-related symptoms, in an effort to identify safe and effective herbal medicines to treat PD.
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