Causal inference using deep neural networks

no code implementations • 25 Nov 2020 • Ye Yuan, Xueying Ding, Ziv Bar-Joseph

Causal inference from observation data is a core problem in many scientific fields.

Causal Inference

Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas Program

no code implementations • 18 Mar 2019 • Michael P Snyder, Shin Lin, Amanda Posgai, Mark Atkinson, Aviv Regev, Jennifer Rood, Orit Rosen, Leslie Gaffney, Anna Hupalowska, Rahul Satija, Nils Gehlenborg, Jay Shendure, Julia Laskin, Pehr Harbury, Nicholas A Nystrom, Ziv Bar-Joseph, Kun Zhang, Katy Börner, Yiing Lin, Richard Conroy, Dena Procaccini, Ananda L Roy, Ajay Pillai, Marishka Brown, Zorina S Galis

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution.

Distributed Gradient Descent in Bacterial Food Search

no code implementations • 11 Apr 2016 • Shashank Singh, Sabrina Rashid, Zhicheng Long, Saket Navlakha, Hanna Salman, Zoltan N. Oltvai, Ziv Bar-Joseph

Communication and coordination play a major role in the ability of bacterial cells to adapt to ever changing environments and conditions.

Quantitative Methods

Inferring Interaction Networks using the IBP applied to microRNA Target Prediction

no code implementations • NeurIPS 2011 • Hai-Son P. Le, Ziv Bar-Joseph

Determining interactions between entities and the overall organization and clustering of nodes in networks is a major challenge when analyzing biological and social network data.

Clustering

Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings

no code implementations • NeurIPS 2010 • Ziv Bar-Joseph, Hai-Son P. Le

Here we develop a new method that can utilize soft matches (given as priors) to infer both, unique and similar expression patterns across species and a matching for the genes in both species.