Human-level Protein Localization with Convolutional Neural Networks
Authors: Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the largest comparison of CNN architectures including Gap Net-PL for protein localization in HTI images of human cells. Gap Net-PL outperforms all other competing methods and reaches close to perfect localization in all 13 tasks with an average AUC of 98% and F1 score of 78%. On a separate test set the performance of Gap Net-PL was compared with three human experts and 25 scholars. |
| Researcher Affiliation | Academia | 1 LIT AI Lab, Johannes Kepler University Linz 2 Institute for Machine Learning, Johannes Kepler University Linz {rumetshofer,hofmarcher,hochreit,klambauer}@ml.jku.at 3Department of Medical Chemistry, Center for Pathobiochemistry and Genetics, Medical University of Vienna clemens.roehrl@meduniwien.ac.at |
| Pseudocode | No | The paper presents architectural diagrams (e.g., Figure 3) but does not include any pseudocode or algorithm blocks describing the methodology. |
| Open Source Code | Yes | Code and dataset are available at: https://github.com/ml-jku/gapnet-pl |
| Open Datasets | Yes | All experiments were conducted on a dataset released for the Cyto Challenge 2017 by the Congress of the International Society for Advancement of Cytometry (ISAC). The main challenge dataset contains 20,000 samples taken from the Cell Atlas (Thul et al., 2017) which is part of the Human Protein Atlas. |
| Dataset Splits | Yes | The pre-processed samples were then randomly split into a training (80%), a validation (10%) and a test (10%) set. |
| Hardware Specification | Yes | The batch size for the models depends on their memory consumption and is chosen as large as possible to fit on a typical GPU with 11GB memory (NVIDIA GTX 1080 Ti). |
| Software Dependencies | No | The paper mentions activation functions (SELU) and optimizers (SGD) but does not provide specific software library names with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, scikit-learn 0.x.x). |
| Experiment Setup | Yes | For our experiments we use an initial learning rate of 0.01, dropout in the fully connected layers of 30% and a batch size of 40. |