The Role Of Biology In Deep Learning

计算生物学中的深度学习：进展、挑战与未来展望

Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook

该综述梳理了计算生物学中深度学习的发展历程、优势与挑战，聚焦其在DNA序列分析、蛋白质结构预测等领域的应用进展，并展望了该领域突破现有瓶颈、深化生物系统研究的前景。

Nothing makes sense in deep learning, except in the light of evolution

Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the success of DL must look at the population of all algorithms in the field and how they have evolved over time. We argue that cultural evolution is a useful framework to explain the success of DL. In analogy to biology, we use `development' to mean the process converting the pseudocode or text description of an algorithm into a fully trained model. This includes writi…

整合深度学习与合成生物学：一种通过N端编码序列增强基因表达的协同设计方法

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-terminal Coding Sequences

本文提出深度学习与合成生物学协同设计的少样本NCS优化方法，结合k近邻编码、word2vec、注意力机制和时间序列网络，仅经六轮迭代即获得使GFP表达提高5.41倍的NCS（MLD62），并验证其可提升N-乙酰神经氨酸产量。

Deep Learning and Artificial General Intelligence: Still a Long Way to Go

In recent years, deep learning using neural network architecture, i.e. deep neural networks, has been on the frontier of computer science research. It has even lead to superhuman performance in some problems, e.g., in computer vision, games and biology, and as a result the term deep learning revolution was coined. The undisputed success and rapid growth of deep learning suggests that, in future, it might become an enabler for Artificial General Intelligence (AGI). In this article, we approach this statement critically showing five major reasons of why deep neural networks, as of the current st…

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.

交替任务下可塑性的无量纲控制：从进化生物学到持续学习

Dimensionless Controls of Plasticity Under Alternating Tasks: From Evolutionary Biology to Continual Learning

本研究借鉴进化生物学思路，将交替任务下的深度学习可塑性简化为任务分歧度$r$和可达性$ηT$两个无量纲控制量，推导了可塑性边界并通过实验验证了主导因素，得到最优可达性的逆幂律启发式规则。