财富杂志 AI如何让老药新用?(3)（在线收听）

At the dawn of modern genetic research, almost no one anticipated the enormous complexity in the biology of disease.

在现代基因研究的初期，几乎没有人预见到疾病生物学的巨大复杂性。

Many researchers thought the genome would be a kind of instruction manual for the body.

很多研究人员认为，基因组应该是人体的一种指导手册。

Pioneers such as Celera Genomics' Craig Venter and Francis Collins of the National Institutes of Health were celebrated as "gene hunters,"

像塞雷拉基因组公司的克雷格·温特以及国立卫生研究院的弗朗西斯·柯林斯这样的先驱被誉为“基因猎手”，

a term that evoked crusaders scouring the globe for that one "silver bullet" gene that would explain—and facilitate a cure for—a given disease.

这个词让人联想到十字军战士们在全球寻找一种“银弹”基因，这种基因能够解释——并帮助治愈——某种特定的疾病。

To some extent, these researchers found real treasure. Geneticist Nancy Wexler, for example,

在某种程度上，这些研究人员发现了真正的宝藏。例如，遗传学者Nancy Wexler

spent years in Venezuela compiling family trees of those affected by Huntington's disease, a rare, inherited condition.

花了多年时间在委内瑞拉汇编亨廷顿氏舞蹈病患者的族谱，这是一种罕见的遗传病。

Her work led to the discovery of the mutation in a single gene that predicts whether an individual will contract the condition.

她的研究发现了一种单基因的突变，这种突变可以预测一个人是否会患上这种病。

But scientists soon realized that genetic maps were less like an instruction manual and more like the parts catalog you get with Ikea furniture.

但科学家们很快意识到，基因图谱不太像一本说明书，而更像是宜家家具的零件目录。

What's more, researchers discovered other catalogs that added complex variables to the relationships between genes and disease—

更重要的是，研究人员还发现了其他目录，这些目录为基因和疾病之间的关系增加了复杂的变量——

for example, the proteome, the proteins encoded by DNA, and the transcriptome, all the nucleic acids that convert DNA into proteins.

例如，蛋白质组，DNA编码的蛋白质，以及转录组，所有将DNA转换成蛋白质的核酸。

The morning-after disappointment has proved wrenching, as researchers learned that complex diseases,

失望是痛苦的，因为研究人员发现，复杂的疾病，

such as cancer and Alzheimer's, didn't yield to a single gene. (Even Huntington's, its gene identified, has remained untreatable.)

如癌症和阿尔茨海默症，不会产生单个基因。（即使亨廷顿氏舞蹈症的基因已被确认，它仍然无法治愈。）

Today, Cohen and others see a link between the obsession with simplicity and a decline in drug discovery.

如今，科恩和其他人看到了对简单的痴迷和对药物发现的减少之间的联系。

That decline shows itself in the 1-in-10 success rate for FDA approval of new therapies; in spiraling costs for drug development

这种减少表现在：FDA批准新疗法的成功率为十分之一；药物研发成本不断攀升

(what a Tufts study recently identified as "the $2.6 billion pill"); and in the soaring prices of the few treatments that break new ground,

（塔夫斯大学最近的一项研究将其认定为“价值26亿美元的药丸”）；为数不多的几种有突破的治疗方法价格飞涨，

such as the $475,000 cost of a course of treatment with Novartis's leukemia drug Kymriah.

如诺华的抗白血病药物Kymriah一个疗程的费用为47.5万美元。

More recently, researchers have begun to grapple with biological complexity with the help of the science of networks.

最近，研究人员开始在网络科学的帮助下设法解决生物学的复杂性。

That science's chief evangelist is Albert-Laszlo Barabasi,

这一科学的首席布道师是艾伯特-拉斯洛·巴拉巴西，

a professor at Northeastern University whose 2014 book Linked popularized the notion that network theory can explain numerous fields,

美国东北大学的一名教授，他在2014年出版的《Linked》一书中普及了这样一种概念，即网络理论可以解释各种领域，

from fashion trends to sexual relations to disease.

如流行趋势、性关系以及疾病。

Barabasi and others realized that disease is like a bad signal that moves through a network of connections from genes to proteins to cells to tissues,

巴拉巴西和其他人意识到，疾病就像一个不良信号，通过一个连接网络，在基因、蛋白质、细胞和组织之间移动

until all these "perturbations" manifest as the familiar symptoms of disease.

直到所有这些“混乱”显现出常见的疾病症状。

Complicated diseases are confluences of numerous effects, because pleiotropy means that any given protein can act at different points in the body.

复杂的疾病是多种影响的汇合，因为基因多效性意味着任何给定蛋白质都可以在身体的不同部位起作用。

Startups like Pharnext assume that drugs can also be pleiotropic,

像Pharnext的初创公司认为，药物也可以是多效性的，

acting on more than one protein and more than one interaction in the body at the same time.

在同一时间作用于不止一种蛋白质，在体内不止一种相互作用。

To find a drug combination capable of tackling complexity, the enormous power of machine learning,

为了寻找能够解决复杂性的组合药物，机器学习的巨大能力

with its ability to spot patterns in data, must be wedded to a sense of the structure by which disease operates.

以及其发现数据模式的能力，必须与对疾病运行结构的感知结合起来。

This, in turn, has required an evolution in the relationship between computer scientists and biologists.

这反过来要求计算机科学家和生物学家之间的关系发生进化。

Newer machine-learning approaches ingest vastly more data and can assemble hierarchies of information that let them go beyond correlation.

更新的机器学习方法可以大量吸收更多的数据，并且能够将信息进行层次组合，使其超越相关性。

Still, harnessing these "deep learning" neural networks into a structure that has any predictive power requires some elegant algorithm-building.

不过，要将这些“深度学习”神经网络转化为具有预测能力的结构，需要一些简炼的算法构建。

Colin Hill, CEO and founder of GNS Healthcare, is one of the builders.

GNS Healthcare的CEO兼创始人Colin Hill就是构建者之一。

His company, based in Cambridge, Mass., has spent 18 years developing a computer system called REFS,

他的公司位于马萨诸塞州剑桥市，该公司花了18年研发一种电脑系统——REFS，

which stands for "reverse engineering, forward simulation."

全称为“逆向工程，正演模拟。”