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researcher

J. Pfaendtner

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • physics.bio-ph1
  • physics.comp-ph1

identity via Semantic Scholar / OpenAlex

activity
20182020
collaborators

4 papers

physics.comp-ph2020

Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data

Maxim Ziatdinov, Shuai Zhang, Orion Dollar +7

Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the cl…

physics.bio-ph2019

Relaxing the aquaporin crystal structure in a membrane with surface vibrational spectroscopy

L. Schmüser, M. Trefz, S. J. Roeters +7

High-resolution structural information on membrane proteins is essential for understanding cell biology and for structure-based design of new medical drugs and drug delivery strate…

cs.LG2018

IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks

Khushmeen Sakloth, Wesley Beckner, Jim Pfaendtner +1

Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successfu…

cs.LG2018

Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction

Garrett B. Goh, Khushmeen Sakloth, Charles Siegel +2

Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. H…

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