12,161 research outputs found
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
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 https://github.com/shervinea/enzynet.Comment: 11 pages, 6 figure
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
- …