Discovering Higher-Order Interactions Through Neural Information Decomposition

Discovering Domains Mediating Protein Interactions

Background: Protein-protein interactions do not provide any direct information re‌garding the domains within the proteins that mediate the interactions. The majority of proteins are multi domain proteins and the interaction between them is often defined by the pairs of their domains. Most of the former studies focus only on interacting do‌main pairs. However they do not consider the in...

Higher Order Dynamic Mode Decomposition

Simultaneous silence organizes structured higher-order interactions in neural populations

Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain un...

Neural computing in discovering RNA interactions.

High-order RNA structures are involved in regulating many biological processes; various algorithms have been designed to predict them. Experimental methods to probe such structures and to decipher the results are tedious. Artificial intelligence and the neural network approach can support the process of discovering RNA structures. Secondary structures of RNA molecules are probed by autoradiogra...

Higher Order Recurrent Neural Networks

In this paper, we study novel neural network structures to better model long term dependency in sequential data. We propose to use more memory units to keep track of more preceding states in recurrent neural networks (RNNs), which are all recurrently fed to the hidden layers as feedback through different weighted paths. By extending the popular recurrent structure in RNNs, we provide the models...