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Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction

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Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope Database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.

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At the Speed of Sound: Efficient Audio Scene Classification

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At the Speed of Sound: Efficient Audio Scene Classification Efficient audio scene classification is essential for smart sensing platforms such as robots, medical monitoring, surveillance, or autonomous vehicles. We propose a retrieval-based scene classification architecture that combines recurrent neural networks and attention to compute embeddings for short audio segments. We train our framework using a custom audio loss function that captures both the relevance of audio segments within a scene and that of sound events within a segment. Using experiments on real audio scenes, we show that we can discriminate audio scenes with high accuracy after listening in for less than a second. This preserves 93% of the detection accuracy obtained after hearing the entire scene.

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