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README.md

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 # README
 ## ManifoldEM: ESPER Repository
 
-This repository contains the software implementation for our [paper](https://www.biorxiv.org/content/10.1101/2021.06.18.449029v1) **Recovery of conformational continuum from single-particle cryo-EM data: Optimization of ManifoldEM informed by ground-truth studies** (Seitz, Schwander, Acosta-Reyes, Maji, Frank). It contains tools to apply the discussed method ESPER (Embedded Subspace Partitioning and Eigenfunction Realignment) to quasi-continuum models. This work was developed in the Frank research group at Columbia University in collaboration with Peter Schwander at the University of Wisconsin-Milwaukee (UWM).
+This repository contains the software implementation for our [paper](https://www.biorxiv.org/content/10.1101/2021.06.18.449029v1) **Geometric machine learning informed by ground-truth: Recovery of conformational continuum from single-particle cryo-EM data of biomolecules** (Seitz, Acosta-Reyes, Maji, Schwander*, Frank*). It contains tools to apply the discussed method ESPER (Embedded Subspace Partitioning and Eigenfunction Realignment) to quasi-continuum models. This work was developed in the Frank research group at Columbia University in collaboration with Peter Schwander at the University of Wisconsin-Milwaukee (UWM).
 
-The algorithms presented here in their current form are developed for analyzing synthetic data. Custom synthetic datasets can be generated as described in our supplementary materials. Additional information can also be found in our [previous paper](https://www.biorxiv.org/content/10.1101/864116v1): **Simulation of Cryo-EM Ensembles from Atomic Models of Molecules Exhibiting Continuous Conformations** (Seitz, Acosta-Reyes, Schwander, Frank); along with detailed code in the corresponding [repository](https://github.com/evanseitz/cryoEM_synthetic_continua).
+The algorithms presented here in their current form are developed for analyzing synthetic data. Custom synthetic datasets can be generated as described in our supplementary materials. Additional information can also be found in our [previous paper](https://www.biorxiv.org/content/10.1101/864116v1): **Simulation of Cryo-EM Ensembles from Atomic Models of Molecules Exhibiting Continuous Conformations** (Seitz, Acosta-Reyes, Schwander, Frank*); along with detailed code in the corresponding [repository](https://github.com/evanseitz/cryoEM_synthetic_continua).
 
 Please note that additional alterations to this code will be required to make this workflow fully accessible to experimentally-obtained data. Much of the code necessary for processing and organizing such data into projection directions is already available in the first half of the founding [ManifoldEM suite](https://github.com/GMashayekhi/ManifoldEM_Matlab), with a Python implementation and comprehensive GUI currently in late-stage production. The workflow presented here branches off from the current ManifoldEM framework permanently after manifolds are created via Diffusion Maps and immediately before NLSA is performed. As discussed in our paper, there also exists the possibility of combining these two techniques, with a decision made for their use based on the quality of each PD-manifold.