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美國Applied Proteomics Inc. (API)
Applied Proteomics Inc. 正致力于將蛋白質(zhì)組學(xué)應(yīng)用到診斷學(xué)、伴隨診斷學(xué)和個性化醫(yī)療領(lǐng)域的發(fā)展中，以幫助作出更好的醫(yī)療決策和提供更優(yōu)質(zhì)的醫(yī)療護(hù)理服務(wù)。 API 已開發(fā)出蛋白質(zhì)組學(xué)平臺解決方案，它將使蛋白質(zhì)生物標(biāo)記物開發(fā)成為一項(xiàng)隨時解決健康診斷問題的可復(fù)制的工業(yè)化應(yīng)用。 API 的平臺集結(jié)了先進(jìn)的儀器設(shè)備、更快的計(jì)算功能、廣泛的基因組注釋和嚴(yán)密的標(biāo)準(zhǔn)操作程序、全面的質(zhì)量控制、有據(jù)可循的工藝流程，涵蓋了從樣品收集到信息化處理的數(shù)百個步驟。 API 目前正在進(jìn)行多個內(nèi)部診斷開發(fā)項(xiàng)目。 API 總部位于加州圣地亞哥。


Applied Proteomics Inc. (API) is advancing the application of proteomics to the development of diagnostics, companion diagnostics, and personalized medicine applications to power better medical decisions and care. API has developed a proteomics platform solution to make protein-based biomarker discovery possible as a replicable, industrial application ready to be applied to health diagnostic problems. API's platform combines improved instrumentation, faster computing and extensive genome annotations with regimented standard operating procedures, extensive quality controls, and documented processes spanning hundreds of steps from sample collection to processing through informatics. API is currently advancing several internal diagnostic development programs. API is headquartered in San Diego, CA.
API was co-founded by Dr. Danny Hillis, a leader in computer science and systems engineering and Dr. David Agus, a leading oncologist and trailblazer in molecular medicine applications. Dr. Agus saw protein measurement as the best diagnostic tool, but protein-based diagnostics were failing to fulfill the potential.

Drawing on his own extensive experience in systems engineering and particularly in designing complex, data-intensive processes, Dr. Hillis recognized the short-comings of proteomics were not necessarily the individual components but the highly complicated, highly integrated process itself. Given its inherent complexity, proteomics demanded a novel discovery approach.

To pioneer the best proteomics platform, API adopted an industrial approach to combine improved instrumentation, faster computing and extensive genome annotations with regimented standard operating procedures, extensive quality controls, and documented processes spanning hundreds of steps from sample collection to processing through informatics.