Benjamin J. Blundell, Christian Sieben, Suliana Manley, myself, QueeLim Ch’ng, and Susan Cox published a new paper in Frontiers in Bioinformatics (open access).
Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Here we present a deep learning solution for reconstructing the protein complexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained. Our convolutional neural network coupled with a differentiable renderer predicts pose and derives a single structure. After training, the network is discarded, with the output of this method being a structural model which fits the data-set. We demonstrate the performance of our system on two protein complexes: CEP152 (which comprises part of the proximal toroid of the centriole) and centrioles.
Massimiliano Patacchiola, Patrick Fox-Roberts and myself published a new paper in Pattern Recognition Letters (PDF here).
This work presents a new way of training auto encoders to allow separation of style and content which gives GAN like performance with the ease of training of auto encoders.
In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs)and Variational Autoencoders (VAEs). However, standard Autoencoders (AEs) and closely related structures have re-mained popular because they are easy to train and adapt to different tasks. An interesting question is if we can achieve state-of-the-art performance with AEs while retaining their good properties. We propose an answer to this question by introducing a new model called Y-Autoencoder (Y-AE). The structure and training procedure of a Y-AE enclose a representation into an implicit and an explicit part. The implicit part is similar to the output of an auto-encoder and the explicit part is strongly correlated with labels in the training set. The two parts are separated in the latent space by split-ting the output of the encoder into two paths (forming a Y shape) before decoding and re-encoding. We then impose a number of losses, such as reconstruction loss, and a loss on dependence between the implicit and explicit parts. Additionally, the projection in the explicit manifold is monitored by a predictor, that is embedded in the encoder and trained end-to-end with no adversarial losses. We provide significant experimental results on various domains, such as separation of style and content, image-to-image translation, and inverse graphics.