6 research outputs found
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Parapred: antibody paratope prediction using convolutional and recurrent neural networks.
MOTIVATION: Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). RESULTS: In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. AVAILABILITY AND IMPLEMENTATION: The Parapred method is freely available as a webserver at http://www-mvsoftware.ch.cam.ac.uk/and for download at https://github.com/eliberis/parapred. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
We analyse multimodal time-series data corresponding to weight, sleep and
steps measurements. We focus on predicting whether a user will successfully
achieve his/her weight objective. For this, we design several deep long
short-term memory (LSTM) architectures, including a novel cross-modal LSTM
(X-LSTM), and demonstrate their superiority over baseline approaches. The
X-LSTM improves parameter efficiency by processing each modality separately and
allowing for information flow between them by way of recurrent
cross-connections. We present a general hyperparameter optimisation technique
for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
state-of-the-art cross-modal approach, using a comparable number of parameters.
Finally, we visualise the model's predictions, revealing implications about
latent variables in this task.Comment: To appear in NIPS ML4H 2017 and NIPS TSW 201