diff --git a/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb b/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb
index bf16e38..dae0b2f 100644
--- a/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb
+++ b/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb
@@ -15,7 +15,7 @@
       "id": "e0825580",
       "metadata": {},
       "source": [
-        "In this tutorial, we will use a [**M**ulti-**O**mics **G**raph c**O**nvolutional **NET**works (MOGONET) by **Wang et al. (Nature Communication, 2021)**](https://www.nature.com/articles/s41467-021-23774-w) [1] pipeline implemented in `PyKale` [2] to integrate **patient multiomics data** for **cancer classification**.\n",
+        "In this tutorial, we will use a [**M**ulti-**O**mics **G**raph c**O**nvolutional **NET**works (MOGONET) by **Wang et al. (Nature Communication, 2021)**](https://www.nature.com/articles/s41467-021-23774-w) [1] pipeline implemented in [`PyKale`](https://github.com/pykale/pykale) [2] to integrate **patient multiomics data** for **cancer classification**.\n",
         "\n",
         "We will work with multiomics data from [**BRCA** of TCGA](https://www.cancerimagingarchive.net/collection/tcga-brca/) [3], which has five subtypes as the labels of classification. Three omics modalities will be used: mRNA expression, DNA methylation, and miRNA expression.\n",
         "\n",
@@ -93,8 +93,8 @@
         "    \"pykale[example]@git+https://github.com/pykale/pykale@main\" \\\n",
         "    gdown==5.2.0 torch-geometric==2.6.0 torch_sparse torch_scatter \\\n",
         "    -f https://data.pyg.org/whl/torch-2.6.0+cu124.html \\\n",
-        "    && echo \"pykale, gdown, nilearn, and yacs installed successfully ✅\" \\\n",
-        "    || echo \"Failed to install pykale, gdown, nilearn, and yacs ❌\""
+        "    && echo \"pykale and its requirements installed successfully ✅\" \\\n",
+        "    || echo \"Failed to install pykale and its requirements ❌\""
       ]
     },
     {
@@ -311,7 +311,7 @@
       "id": "007e4533",
       "metadata": {},
       "source": [
-        "If users are interested in more details regarding the model, please refer to the [Helper Function & Model Definition](https://pykale.github.io/mmai-tutorials/tutorials/multiomics-cancer-classification/extend-reading/helper-functions.html) of the tutorial.\n",
+        "If users are interested in more details regarding the model, please refer to the [Helper Function and Model Definition](https://pykale.github.io/mmai-tutorials/tutorials/multiomics-cancer-classification/extend-reading/helper-functions.html) of the tutorial.\n",
         "\n",
         "To initialize the model, we firstly call `MogonetModel` from [`model.py`](https://github.com/pykale/mmai-tutorials/blob/main/tutorials/multiomics-cancer-classification/model.py)."
       ]
@@ -481,7 +481,7 @@
       "metadata": {},
       "source": [
         "## Step 5: Interpretation Study\n",
-        "We use `kale.interpret` to perform interpretation, where a function that systematically masks input features and observes the effect on performance—highlighting which features are most important for classification is provided. Please refer to [Interpret Study page](https://pykale.github.io/mmai-tutorials/tutorials/multiomics-cancer-classification/extend-reading/interpretation-study.html) for more details.\n",
+        "We use `kale.interpret` to perform interpretation, where a function that systematically masks input features and observes the effect on performance—highlighting which features are most important for classification is provided. Please refer to [Interpretation Study page](https://pykale.github.io/mmai-tutorials/tutorials/multiomics-cancer-classification/extend-reading/interpretation-study.html) for more details.\n",
         "\n",
         "Because the interpretation study needs us to mask one feature and observe the performance drop, we firstly define the trainer for the interpretation experiments.\n",
         "\n",