The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren

被引:15
作者
Foksinska, Aleksandra [1 ]
Crowder, Camerron M. [1 ,2 ]
Crouse, Andrew B. [1 ]
Henrikson, Jeff [3 ]
Byrd, William E. [1 ]
Rosenblatt, Gregory [1 ]
Patton, Michael J. [1 ]
He, Kaiwen [1 ]
Tran-Nguyen, Thi K. [1 ]
Zheng, Marissa [4 ]
Ramsey, Stephen A. [5 ]
Amin, Nada [6 ]
Osborne, John [7 ]
Might, Matthew [1 ]
机构
[1] Univ Alabama Birmingham, Hugh Kaul Precis Med Inst, Birmingham, AL 35294 USA
[2] Univ Alabama Birmingham, Dept Neurobiol, Birmingham, AL USA
[3] Groovescale, Seattle, WA USA
[4] Harvard Univ, Dept Mol & Cellular Biol, Cambridge, MA USA
[5] Oregon State Univ, Sch Elect Engn & Comp Sci, Corvallis, OR USA
[6] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA USA
[7] Univ Alabama Birmingham, Informat Inst, Dept Med, Birmingham, AL USA
来源
FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2022年 / 5卷
基金
美国国家卫生研究院;
关键词
rare disease; precision medicine; drug repurposing; artificial intelligence; biomedical reasoning; PROTEIN;
D O I
10.3389/frai.2022.910216
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
There are over 6,000 different rare diseases estimated to impact 300 million people worldwide. As genetic testing becomes more common practice in the clinical setting, the number of rare disease diagnoses will continue to increase, resulting in the need for novel treatment options. Identifying treatments for these disorders is challenging due to a limited understanding of disease mechanisms, small cohort sizes, interindividual symptom variability, and little commercial incentive to develop new treatments. A promising avenue for treatment is drug repurposing, where FDA-approved drugs are repositioned as novel treatments. However, linking disease mechanisms to drug action can be extraordinarily difficult and requires a depth of knowledge across multiple fields, which is complicated by the rapid pace of biomedical knowledge discovery. To address these challenges, The Hugh Kaul Precision Medicine Institute developed an artificial intelligence tool, mediKanren, that leverages the mechanistic insight of genetic disorders to identify therapeutic options. Using knowledge graphs, mediKanren enables an efficient way to link all relevant literature and databases. This tool has allowed for a scalable process that has been used to help over 500 rare disease families. Here, we provide a description of our process, the advantages of mediKanren, and its impact on rare disease patients.
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页数:13
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