DeepPep: Deep proteome inference from peptide profiles

DeepPep: Deep proteome inference from peptide profiles

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in pro...

From Deep Inference to Proof Nets

This paper shows how derivations in (a variation of) SKS can be translated into proof nets. Since an SKS derivation contains more information about a proof than the corresponding proof net, we observe a loss of information which can be understood as “eliminating bureaucracy”. Technically this is achieved by cut reduction on proof nets. As an intermediate step between the two extremes, SKS deriv...

Deep Inference

Learning Deep Inference Machines

Introduction. The traditional approach to structured prediction problems is to craft a graphical model structure, learn parameters for the model, and perform inference using an efficient– and usually approximate– inference approach, including, e.g., graph cut methods, belief propagation, and variational methods. Unfortunately, while remarkably powerful methods for inference have been developed ...

Deep Active Inference

This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies as efficient large scale, black box optimisation technique, to introduce the “deep active inference” agent. This agent minimises a variational free energy bound on the average surprise...