During the past decade, with the significant progress of computational power
as well as ever-rising data availability, deep learning techniques became
increasingly popular due to their excellent performance on computer vision
problems. The size of the Protein Data Bank has increased more than 15 fold
since 1999, which enabled the expansion of models that aim at predicting
enzymatic function via their amino acid composition. Amino acid sequence
however is less conserved in nature than protein structure and therefore
considered a less reliable predictor of protein function. This paper presents
EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the
Enzyme Commission number of enzymes based only on their voxel-based spatial
structure. The spatial distribution of biochemical properties was also examined
as complementary information. The 2-layer architecture was investigated on a
large dataset of 63,558 enzymes from the Protein Data Bank and achieved an
accuracy of 78.4% by exploiting only the binary representation of the protein
shape. Code and datasets are available at this https URL

本文介绍了 EnzyNet，这是一个新颖的 3D 卷积神经网络分类器，可以仅基于酶的体素空间结构来预测酶的 EC 号码，并探讨了生化属性的空间分布作为补充信息。

EnzyNet: 一种基于 3D 卷积神经网络在空间表示上进行的酶分类方法

EnzyNet: enzyme classification using 3D convolutional neural networks on  spatial representation

In this work, we present a novel 3D-Convolutional Neural Network (CNN)
architecture called I2I-3D that predicts boundary location in volumetric data.
Our fine-to-fine, deeply supervised framework addresses three critical issues
to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label
training and prediction (2) precise voxel-level prediction to capture fine
scale structures prevalent in medical data and (3) directed multi-scale,
multi-level feature learning. We evaluate our approach on a dataset consisting
of 93 medical image volumes with a wide variety of anatomical regions and
vascular structures. In the process, we also introduce HED-3D, a 3D extension
of the state-of-the-art 2D edge detector (HED). We show that our deep learning
approach out-performs, the current state-of-the-art in 3D vascular boundary
detection (structured forests 3D), by a large margin, as well as HED applied to
slices, and HED-3D while successfully localizing fine structures. With our
approach, boundary detection takes about one minute on a typical 512x512x512
volume.

介绍了一种叫做 I2I-3D 的 3D 卷积神经网络体系结构，用于在体积数据中预测边界位置，并且经过实验证明，该深度学习方法优于结构森林 3D 和 HED 等方法，适用于医疗数据的边界检测，同时能够捕捉到细微的结构。