address comments

holgerroth · Feb 25, 2025 · 2822980 · 2822980
1 parent bc75435
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diff --git a/examples/advanced/bionemo/task_fitting/src/bionemo_mlp_job.py b/examples/advanced/bionemo/task_fitting/src/bionemo_mlp_job.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

diff --git a/examples/advanced/bionemo/task_fitting/src/bionemo_mlp_learner.py b/examples/advanced/bionemo/task_fitting/src/bionemo_mlp_learner.py
@@ -34,7 +34,7 @@
 from nvflare.app_common.utils.fl_model_utils import FLModelUtils
 
 
-class BioNeMoMLPLearner(ModelLearner):  # does not support CIFAR10ScaffoldLearner
+class BioNeMoMLPLearner(ModelLearner):
     def __init__(
         self,
         data_path: str,

diff --git a/examples/advanced/bionemo/task_fitting/task_fitting.ipynb b/examples/advanced/bionemo/task_fitting/task_fitting.ipynb
@@ -66,7 +66,7 @@
     "\n",
     "Here we are interested in training a neural network to predict subcellular location from an embedding.\n",
     "\n",
-    "The data we will be using comes from the paper [Light attention predicts protein location from the language of life](https://academic.oup.com/bioinformaticsadvances/article/1/1/vbab035/6432029) by Stärk et al. In this paper, the authors developed a machine learning algorithm to predict the subcellular location of proteins from sequence through protein langage models that are similar to those hosted by BioNeMo. Protein subcellular location refers to where the protein localizes in the cell, for example a protein my be expressed in the Nucleus or in the Cytoplasm. Knowing where proteins localize can provide insights into the underlying mechanisms of cellular processes and help identify potential targets for drug development. The following image includes a few examples of subcellular locations in an animal cell:\n",
+    "The data we will be using comes from the paper [Light attention predicts protein location from the language of life](https://academic.oup.com/bioinformaticsadvances/article/1/1/vbab035/6432029) by Stärk et al. In this paper, the authors developed a machine learning algorithm to predict the subcellular location of proteins from sequence through protein langage models that are similar to those hosted by BioNeMo. Protein subcellular location refers to where the protein localizes in the cell, for example a protein may be expressed in the Nucleus or in the Cytoplasm. Knowing where proteins localize can provide insights into the underlying mechanisms of cellular processes and help identify potential targets for drug development. The following image includes a few examples of subcellular locations in an animal cell:\n",
     "\n",
     "\n",
     "(Image freely available at https://pixabay.com/images/id-48542)\n",