Add links to app dirs

Author: Fairly

tutorials/compound_property_prediction_tutorial.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Compound representation learning and property prediction\n",
     "\n",
-    "In this tuorial, we will go through how to run a Graph Neural Network (GNN) model for compound property prediction. In particular, we will demonstrate how to pretrain and finetune the model in the downstream tasks.\n",
+    "In this tuorial, we will go through how to run a Graph Neural Network (GNN) model for compound property prediction. In particular, we will demonstrate how to pretrain and finetune the model in the downstream tasks. If you are intersted in more details, please refer to the README for \"[info graph](https://github.com/PaddlePaddle/PaddleHelix/apps/pretrained_compound/info_graph)\" and \"[pretrained GNN](https://github.com/PaddlePaddle/PaddleHelix/apps/pretrained_compound/pretrain_gnns)\".\n",
     "\n",
     "#  Part I: Pretraining\n",
     "\n",

tutorials/compound_property_prediction_tutorial_cn.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# 化合物表示学习和性质预测\n",
     "\n",
-    "在这篇教程中，我们将介绍如何运用图神经网络（GNN）模型来预测化合物的性质。具体来说，我们将演示如何对其进行预训练（pretrain），如何针对下游任务进行模型微调（finetune），并利用最终的模型进行推断（inference）。\n",
+    "在这篇教程中，我们将介绍如何运用图神经网络（GNN）模型来预测化合物的性质。具体来说，我们将演示如何对其进行预训练（pretrain），如何针对下游任务进行模型微调（finetune），并利用最终的模型进行推断（inference）。如果你想了解更多细节，请查阅 \"[info graph](https://github.com/PaddlePaddle/PaddleHelix/apps/pretrained_compound/info_graph/README_cn.md)\" 和 \"[pretrained GNN](https://github.com/PaddlePaddle/PaddleHelix/apps/pretrained_compound/pretrain_gnns/README_cn.md)\" 的详细解释.\n",
     "\n",
     "# 第一部分：预训练\n",
     "\n",

tutorials/linearrna_tutorial_cn.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "LinearRNA 包括一系列的线性时间 RNA 二级结构分析算法: **LinearFold** 和 **LinearPartition**。"
+    "LinearRNA 包括一系列的线性时间 RNA 二级结构分析算法: **LinearFold** 和 **LinearPartition**。关于这个主题的更多信息请查阅[这里](https://github.com/PaddlePaddle/PaddleHelix/c/pahelix/toolkit/linear_rna/README_cn.md)。"
    ]
   },
   {

tutorials/protein_pretrain_and_property_prediction_tutorial.ipynb

@@ -11,7 +11,7 @@
    "source": [
     "# Protein pretraining and property prediction\n",
     "\n",
-    "In this tuorial, we will go through how to run a sequence model for protein property prediction. In particular, we will demonstrate how to pretrain it and how to finetune in the downstream tasks.\n",
+    "In this tuorial, we will go through how to build a simply sequence model for protein property prediction. In particular, we will demonstrate how to pretrain it and how to finetune it in the downstream tasks. More details of this topic can be found [here](https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/pretrained_protein/tape).\n",
     "\n",
     "In recent years, with sequencing technology development, the protein sequence database scale has significantly increased. However, the cost of obtaining labeled protein sequences is still very high, as it requires biological experiments. Besides, due to the inadequate number of labeled samples, the model has a high probability of overfitting the data. Borrowing the ideas from natural language processing (NLP), we can pre-train numerous unlabeled sequences by self-supervised learning. In this way, we can extract useful biological information from proteins and transfer them to other tagged tasks to make these tasks training faster and more stable convergence. These instructions refer to the work of paper TAPE, providing the model implementation of Transformer, LSTM, and ResNet.\n",
     "\n",
@@ -345,4 +345,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

tutorials/protein_pretrain_and_property_prediction_tutorial_cn.ipynb

@@ -11,7 +11,7 @@
    "source": [
     "# 蛋白质预训练和性质预测\n",
     "\n",
-    "在这份教程中，我们将介绍如何构建一个序列模型来进行蛋白质性质预测。具体来说，我们将展示如何对模型进行预训练并针对下游任务进行微调。\n",
+    "在这份教程中，我们将介绍如何构建一个序列模型来进行蛋白质性质预测。具体来说，我们将展示如何对模型进行预训练并针对下游任务进行微调。关于这个主题的更多详细介绍请查阅[这里](https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/pretrained_protein/tape/README_cn.md)。\n",
     "\n",
     "近年来，随着测序技术的发展，蛋白质序列数据库的规模显著扩大。然而，必须通过湿实验才能够获得的有标注蛋白序列的成本仍然很高。此外，由于标记样本数量不足，模型有很高的概率过拟合数据。借鉴自然语言处理（NLP）的思想，通过自监督学习可以在大量无标注的蛋白序列上进行预训练。这样，我们就可以从蛋白质序列中提取有用的生物信息，并将其迁移到其他有标注的任务中，使这些任务的训练速度更快和更稳定地收敛。本教程的内容参考了 TAPE 的工作，提供了 Transformer、LSTM 和 ResNet 的模型实现。\n",
     "\n",
@@ -345,4 +345,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}