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      Mapping drug biology to disease genetics to discover drug impacts on the human phenome.

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          Abstract

          Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects.

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          Most cited references48

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

          Yoav Benjamini, Yosef Hochberg (1995)
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            A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

            Aravind Subramanian, Rajiv Narayan, Steven Corsello … (2017)
            We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.
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              The control of the false discovery rate in multiple testing under dependency

              Yoav Benjamini, Daniel Yekutieli (2001)
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Bioinform Adv
                Title: Bioinformatics advances
                Publisher: Oxford University Press (OUP)
                ISSN (Electronic): 2635-0041
                ISSN (Print): 2635-0041
                Publication date (Electronic): 2024
                Volume: 4
                Issue: 1
                Affiliations
                [1 ] Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, United States.
                [2 ] Department of Biological Science, University of Massachusetts Lowell, Lowell, MA 01854, United States.
                Article
                Publisher Item ID: vbae038
                DOI: 10.1093/bioadv/vbae038
                PMC ID: 11087821
                PubMed ID: 38736684
                SO-VID: cd6eb9c0-d37b-4e98-9ce1-ae99e4869e5a
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