gan学到的是什么

一个介绍 (An Introduction)

Generative Networks like GANs are unique to other deep learning models in that they generate a sample instead of optimizing for an output. This allows for a measure of creativity; scientists can analyze the output of a generative model to understand a biological system.

诸如GAN之类的生成网络对于其他深度学习模型而言是独特的，因为它们生成样本而不是针对输出进行优化。 这可以衡量创造力； 科学家可以分析生成模型的输出以了解生物系统。

The purpose of this article is to detail the potential applications of GANs to scientific research, so I will assume a preliminary understanding of GANs. Basically, GANs contains a generator which learns the distribution of a dataset with the help of a discriminator, resulting in a model capable of outputting new samples. The architecture is as follows:

本文的目的是详细介绍GAN在科学研究中的潜在应用，因此，我将对GAN进行初步的了解。 基本上，GANs包含一个生成器，该生成器借助鉴别器来学习数据集的分布，从而生成能够输出新样本的模型。 架构如下：

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I divide the contribution of GANs to scientific research into 3 major categories: preparation, direction, and modeling.

我将GAN对科学研究的贡献分为三个主要类别：准备，指导和建模。

制备 (Preparation)

Preparing samples to study is probably one of the most unattractive parts of biological research. GANs are useful when we have some data, and we want to convert that into an image (when we input data, we use conditional GANs, or cGANS). Thus, we need to analyze where we might need to produce an image in the preparation phase.

准备样本进行研究可能是生物学研究中最缺乏吸引力的部分之一。 当我们有一些数据并且想要将其转换为图像时(当我们输入数据时，我们使用条件GAN或cGANS)，GAN很有用。 因此，我们需要分析在准备阶段可能需要在何处产生图像。

One paper [1] has used cGANs as a method for normalizing stained tissue cells for computational analysis. Stain normalization is done to reduce inconsistencies in stained tissues (e.g. some samples may be more darkly stained than others) and prepare those tissues for computer-aided detection systems.

一篇论文[1]已将cGANs用作归一化染色组织细胞以进行计算分析的方法。 进行染色归一化以减少染色组织中的不一致(例如，某些样品可能比其他样品更暗)，并为计算机辅助检测系统准备这些组织。

However, performing standard techniques of stain normalization is often distorts subtleties in tissue structure. The authors utilized cGAN to address this problem, since the cGANs better learn the underlying structure of the tissue samples, thereby preserving its structure. cGANs can thus be used as a preprocessing step in a tissue analysis pipeline.

但是，执行标准的污渍归一化技术通常会扭曲组织结构中的细微差别。 作者利用cGAN解决了这个问题，因为cGAN可以更好地了解组织样本的基础结构，从而保留其结构。 因此，cGAN可用作组织分析流程中的预处理步骤。

[source][来源]

方向 (Direction)

GANs is particularly useful for establishing potential directions in scientific study: we can generate molecules or try out potential protein structures using GANs.

GAN对于建立科学研究的潜在方向特别有用：我们可以使用GAN生成分子或尝试潜在的蛋白质结构。

Molecules that GANs output are rarely stable or potentially useful, but we can subsequently use other deep learning models to screen the few promising molecules in a dataset. This will advance drug discovery by outputting far more viable drugs than we can produce with standard techniques (standard techniques for drug discovery means just relying on the imagination of senior chemists) [2].

GANs输出的分子很少稳定或可能有用，但是我们可以随后使用其他深度学习模型来筛选数据集中的一些有前途的分子。 这将通过输出比我们用标准技术(标准的药物发现技术仅依赖于高级化学家的想象力)生产的可行性更高的可行药物来促进药物开发[2]。

[source][来源]

The process of drug discovery is more than just discovering possible drugs, though. Testing is rigorous and the probability that a drug passes these standards is extremely low. For a drug to be useful, it must react with the intended protein or pathway to produce the intended effect while simultaneously not reacting to our countless other bodily systems. Such a feat is necessarily difficult; GANs just allows us to fail faster.

但是，药物发现的过程不仅仅是发现可能的药物。 测试非常严格，药物通过这些标准的可能性极低。 为了使一种药物有用，它必须与预期的蛋白质或途径发生React以产生预期的效果，同时又不与我们无数的其他身体系统发生React。 这样的壮举一定是困难的。 GAN只是让我们更快地失败。

GANs can also suggest new scientific directions by generating potential protein designs. Outputting new proteins, however, is a much harder task than generating small molecules by virtue of the countless interactions contributing to the protein’s complex structure. Therefore, using GANs to design new proteins is not as developed as for generating smaller molecules. However, because new proteins are used for industry (e.g. laundry detergent uses enzymes), the task of protein design is not limited by the downstream effects of molecules in a biological system.

GAN还可以通过产生潜在的蛋白质设计来提出新的科学方向。 但是，由于产生了许多复杂的蛋白质，因此与产生小分子相比，输出新蛋白质要困难得多。 因此，使用GAN设计新蛋白的能力还不如生成较小的分子。 但是，由于新的蛋白质被用于工业(例如洗衣粉使用酶)，因此蛋白质设计的任务不受生物系统中分子的下游作用的限制。

造型 (Modeling)

A GAN is able to output a new image because it learns the distribution of a particular kind of imaging. For instance, if the GAN trains on a dataset of cats, it learns the distribution of cat images, and hence is able to output images (which look like cats) based on that distribution. We can use the same process to model biological systems.

GAN能够输出新图像，因为它可以学习特定种类的成像的分布。 例如，如果GAN在猫的数据集上训练，它会学习猫的图像分布，因此能够基于该分布输出图像(看起来像猫)。 我们可以使用相同的过程对生物系统进行建模。

If one protein affects the structure of the entire cell, then we can modify the feature vector representing that protein to generate an image of the cell’s structure. Using this GANs, we can study changes of the cell structure by taking samples at discrete phases in its development. Then, we can interpolate the feature vectors of the input protein, resulting in a continuous model of the cell’s development [4].

如果一种蛋白质影响整个细胞的结构，那么我们可以修改代表该蛋白质的特征向量，以生成细胞结构的图像。 使用这种GAN，我们可以通过在发育过程中的不同阶段取样来研究细胞结构的变化。 然后，我们可以内插输入蛋白质的特征向量，从而形成细胞发育的连续模型[4]。

[source][来源]

We can apply the same process to a plethora of systems, such as modeling tissue development [5]. Subsequently, when we further study the system, we can derive additional variables affecting the system. We can retrain our GAN with the addition of that new discovered variable, then the GAN will be an even more accurate model of the biological system. Then, when we discover all significant variables, the GAN will practically be a perfect representation of the biological system: we input some variables, then it generates the exact conditions of the biological system.

我们可以将相同的过程应用于多种系统，例如对组织发育进行建模[5]。 随后，当我们进一步研究系统时，我们可以得出影响系统的其他变量。 我们可以通过添加新发现的变量来重新训练GAN，然后GAN将成为生物系统的更准确模型。 然后，当我们发现所有重要变量时，GAN实际上将是生物系统的完美代表：我们输入一些变量，然后它会生成生物系统的确切条件。

We are far from achieving this ideal given the complexity of practically any biological system, GANs can potentially represent perfect mathematical models of biological systems as we investigate further into them.

考虑到几乎任何生物系统的复杂性，我们都远未达到这一理想，随着我们对它们的进一步研究，GAN可以潜在地代表生物系统的完美数学模型。

翻译自: https://medium.com/@adam.mehdi23/gans-for-driving-biological-research-d1c2d678036c

gan学到的是什么

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