# Deep learning model to predict mRNA Degradation

**This article was published as a part of the Data Science Blogathon**

Designing a deep learning model that will predict degradation rates at each base of an RNA molecule using the Eterna dataset comprising over 3000 RNA molecules.

### Introduction

## Project Objectives

In this project, we are going to use explore our dataset and then preprocessed sequence, structure, and predicted loop type features so that they can be used to train our deep learning GRU model. Finally predicting degradation records on public and test datasets.

## Code

### Getting Ready

We will be using **TensorFlow** as our main library to build and train our model and JSON/Pandas to ingest the data. For visualization, we are going to use Plotly and for data manipulation Numpy.

# Dataframe import json import pandas as pd import numpy as np # Visualization import plotly.express as px # Deeplearning import tensorflow.keras.layers as L import tensorflow as tf # Sklearn from sklearn.model_selection import train_test_split #Setting seeds tf.random.set_seed(2021) np.random.seed(2021)

#### Training Parameters

- Target columns: reactivity, deg_Mg_pH10, deg_Mg_50C, deg_pH10, deg_50C
- Model_Train: True if you want to Train a model which takes 1 hour to train.

# This will tell us the columns we are predicting target_cols = ['reactivity', 'deg_Mg_pH10', 'deg_Mg_50C', 'deg_pH10', 'deg_50C'] Model_Train = True # True if you want to Train model which take 1 hour to train.

## Metric

Our model performance metric is **MCRMSE **(Mean column-wise root mean squared error), which takes root mean square error of ground truth of all target columns.

where is the number of scored ground truth target columns, and are the actual and predicted values, respectively?

def MCRMSE(y_true, y_pred):## Monte Carlo root mean squared errors colwise_mse = tf.reduce_mean(tf.square(y_true - y_pred), axis=1) return tf.reduce_mean(tf.sqrt(colwise_mse), axis=1)

#### Loading Data

The mRNA degradation data is available on Kaggle.

### Columns detail explanation

**id**– Unique identifier sample.**seq_scored**– This should match the length of reactivity, deg_*, andcolumns.*error***seq_length**– The length of the sequence.**sequence**– Describes the RNA sequence, a combination of A, G, U, and C for each sample.**structure**– An array of (, ), and. characters donate to the base is to be paired or unpaired.**reactivity**– These numbers are reactivity values for the first 68 bases used to determine the likely secondary structure of the RNA sample.**deg_pH10**– The likelihood of degradation after incubating without magnesium on pH10 at base or linkage.**deg_Mg_pH10**– The likelihood of degradation after incubating with magnesium on pH 10 at base or linkage.**deg_50C**– The likelihood of degradation after incubating without magnesium at 50 degrees Celsius at base or linkage.**deg_Mg_50C**– The likelihood of degradation after incubating with magnesium at 50 degrees Celsius at base or linkage.– calculated errors in experimental values obtained in reactivity, and deg_* columns.***_error_*****predicted_loop_type**– Loop types assigned by bpRNA from Vienna which suggests:- S: paired Stem
- M: Multiloop
- I: Internal loop
- B: Bulge
- H: Hairpin loop
- E: dangling End
- X: external loop

#### Observing Data

data_dir = "stanford-covid-vaccine/" train = pd.read_json(data_dir + "train.json", lines=True) test = pd.read_json(data_dir + "test.json", lines=True) sample_df = pd.read_csv(data_dir + "sample_submission.csv")

we have sequence, structure, and predicted loop types that are in text formats. We will be converting them into numerical tokens so that they can be used to train deep learning models. Then we have arrays within columns from reactivity_error to deg_50C that we will be using as targets.

train.head(2)

print('Train shapes: ', train.shape) print('Test shapes: ', test.shape)

Train shapes: (2400, 19) Test shapes: (3634, 7)

## Signal to noise distribution

We can see the signal-to-noise distribution is between 0 to 15 and the majority of samples lie between 0-6. We have also negative values that we need to get rid of.

fig = px.histogram( train, "signal_to_noise", nbins=25, title='signal_to_noise distribution', width=800, height=400 ) fig.show()

#### Removing negative values

train = train.query("signal_to_noise >= 1")

#### Sequence Test length

After looking at sequence length distribution we know that we have two distinctive sequence lengths, one at 107 and another at 130.

fig = px.histogram( test, "seq_length", nbins=25, title='sequence_length distribution', width=800, height=400 ) fig.show()

#### Splitting Test into Public and Private DataFrame

Let’s split our test dataset based on sequence length. Doing this will improve the overall performance of our GRU model.

public_df = test.query("seq_length == 107")

private_df = test.query("seq_length == 130")

#### Pre-processing Data

token2int = {x: i for i, x in enumerate("().ACGUBEHIMSX")}

token2int

{'(': 0, ')': 1, '.': 2, 'A': 3, 'C': 4, 'G': 5, 'U': 6, 'B': 7, 'E': 8, 'H': 9, 'I': 10, 'M': 11, 'S': 12, 'X': 13}

#### Converting DataFrame to 3D Array

The function below takes a Pandas data frame and converts it into a 3D NumPy array. We will be using it to convert both training features and targets.

def dataframe_to_array(df): return np.transpose(np.array(df.values.tolist()), (0, 2, 1))

#### Tokenization of Sequence

The function below uses a string to integer dictionary that we had created early to convert training features into arrays containing integers. Then we will be using dataframe_to_array to convert our dataset into a 3D NumPy array.

def dataframe_label_encoding( df, token2int, cols=["sequence", "structure", "predicted_loop_type"] ): return dataframe_to_array( df[cols].applymap(lambda seq: [token2int[x] for x in seq]) ) ## tokenization of Sequence, Structure, Predicted loop

#### Preprocessing Features and Labels

- Using label endorsing function on our training features.
- Converting target data frame into a 3D array.

train_inputs = dataframe_label_encoding(train, token2int) ## Label encoding train_labels = dataframe_to_array(train[target_cols]) ## dataframe to 3D array to

#### Train & Validation split

Splitting our training data into train and validation sets. We are using signal to noise filter to equally distribute our dataset.

x_train, x_val, y_train, y_val = train_test_split( train_inputs, train_labels, test_size=0.1, random_state=34, stratify=train.SN_filter )

#### Preprocessing Public and Private Dataframe

Earlier we have split our test dataset into public and private based on sequence length now we are going to use dataframe_label_encoding to tokenized and reshape it into NumPy array as we have done the same with the training dataset.

public_inputs = dataframe_label_encoding(public_df, token2int) private_inputs = dataframe_label_encoding(private_df, token2int)

## Training / Evaluating Model

#### Build Model

Before jumping directly into the deep learning model, we have tested other gradient boosts such as Light GBM and CatBoost. Then as we were dealing with the sequence, I thought to experiment around BiLSTM model, but they all performed worst compared to the triple GRU model with linear activation.

This model is influenced by xhlulu initial models. I amazed me how simple the GRU layer can produce the best results possible without using data augmentation or feature engineering.

To learn more about RNNs, LSTM and GRU, please see this blog post.

def build_model( embed_size, # Length of unique tokens seq_len=107, # public dataset seq_len pred_len=68, # pred_len for public data dropout=0.5, # trying best dropout (general) sp_dropout=0.2, # Spatial Dropout embed_dim=200, # embedding dimension hidden_dim=256, # hidden layer units ): inputs = L.Input(shape=(seq_len, 3)) embed = L.Embedding(input_dim=embed_size, output_dim=embed_dim)(inputs) reshaped = tf.reshape( embed, shape=(-1, embed.shape[1], embed.shape[2] * embed.shape[3]) ) hidden = L.SpatialDropout1D(sp_dropout)(reshaped) # 3X BiGRU layers hidden = L.Bidirectional( L.GRU( hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer="orthogonal", ) )(hidden) hidden = L.Bidirectional( L.GRU( hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer="orthogonal", ) )(hidden) hidden = L.Bidirectional( L.GRU( hidden_dim, dropout=dropout, return_sequences=True, kernel_initializer="orthogonal", ) )(hidden) # Since we are only making predictions on the first part of each sequence, # we have to truncate it truncated = hidden[:, :pred_len] out = L.Dense(5, activation="linear")(truncated) model = tf.keras.Model(inputs=inputs, outputs=out) model.compile(optimizer="Adam", loss=MCRMSE) # loss function as of Eval Metric return model

## Building model

building our model by adding embed size (14) and we are going to use default values for other parameters.

- sequence length: 107
- prediction length: 68
- dropout: 0.5
- spatial dropout: 0.2
- embedded dimensions: 200
- hidden layers dimensions: 256

model = build_model( embed_size=len(token2int) ## embed_size = 14 ) ## uniquie token in sequence, structure, predicted_loop_type model.summary()

Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 107, 3)] 0 _________________________________________________________________ embedding (Embedding) (None, 107, 3, 200) 2800 _________________________________________________________________ tf.reshape (TFOpLambda) (None, 107, 600) 0 _________________________________________________________________ spatial_dropout1d (SpatialDr (None, 107, 600) 0 _________________________________________________________________ bidirectional (Bidirectional (None, 107, 512) 1317888 _________________________________________________________________ bidirectional_1 (Bidirection (None, 107, 512) 1182720 _________________________________________________________________ bidirectional_2 (Bidirection (None, 107, 512) 1182720 _________________________________________________________________ tf.__operators__.getitem (Sl (None, 68, 512) 0 _________________________________________________________________ dense (Dense) (None, 68, 5) 2565 ================================================================= Total params: 3,688,693 Trainable params: 3,688,693 Non-trainable params: 0 _________________________________________________________________

## Training Model

We are going to train our model for 40 epochs and save the model checkpoint in the Model folder. We have experimented with batch sizes from 16, 32, 64 and by far 64 batch sizes produced better results and fast convergence.

As we can observe both training and validation loss (MCRMSE) is reducing with every iteration until 20 epochs and from there they start to diverge. For the next experimentation, we will be keeping the number of epochs limited to twenty to get fast and better results.

if Model_Train: history = model.fit( x_train, y_train, validation_data=(x_val, y_val), batch_size=64, epochs=40, verbose=2, callbacks=[ tf.keras.callbacks.ReduceLROnPlateau(patience=5), tf.keras.callbacks.ModelCheckpoint("Model/model.h5"), ], )

Epoch 1/40 30/30 - 69s - loss: 0.4536 - val_loss: 0.3796 Epoch 2/40 30/30 - 57s - loss: 0.3856 - val_loss: 0.3601 Epoch 3/40 30/30 - 57s - loss: 0.3637 - val_loss: 0.3410 Epoch 4/40 30/30 - 57s - loss: 0.3488 - val_loss: 0.3255 Epoch 5/40 30/30 - 57s - loss: 0.3357 - val_loss: 0.3188 Epoch 6/40 30/30 - 57s - loss: 0.3295 - val_loss: 0.3163 Epoch 7/40 30/30 - 57s - loss: 0.3200 - val_loss: 0.3098 Epoch 8/40 30/30 - 57s - loss: 0.3117 - val_loss: 0.2997 Epoch 9/40 30/30 - 57s - loss: 0.3046 - val_loss: 0.2899 Epoch 10/40 30/30 - 57s - loss: 0.2993 - val_loss: 0.2875 Epoch 11/40 30/30 - 57s - loss: 0.2919 - val_loss: 0.2786 Epoch 12/40 30/30 - 57s - loss: 0.2830 - val_loss: 0.2711 Epoch 13/40 30/30 - 57s - loss: 0.2777 - val_loss: 0.2710 Epoch 14/40 30/30 - 57s - loss: 0.2712 - val_loss: 0.2584 Epoch 15/40 30/30 - 57s - loss: 0.2640 - val_loss: 0.2580 Epoch 16/40 30/30 - 57s - loss: 0.2592 - val_loss: 0.2518 Epoch 17/40 30/30 - 57s - loss: 0.2540 - val_loss: 0.2512 Epoch 18/40 30/30 - 57s - loss: 0.2514 - val_loss: 0.2461 Epoch 19/40 30/30 - 57s - loss: 0.2485 - val_loss: 0.2492 Epoch 20/40 30/30 - 57s - loss: 0.2453 - val_loss: 0.2434 Epoch 21/40 30/30 - 57s - loss: 0.2424 - val_loss: 0.2411 Epoch 22/40 30/30 - 57s - loss: 0.2397 - val_loss: 0.2391 Epoch 23/40 30/30 - 57s - loss: 0.2380 - val_loss: 0.2412 Epoch 24/40 30/30 - 57s - loss: 0.2357 - val_loss: 0.2432 Epoch 25/40 30/30 - 57s - loss: 0.2330 - val_loss: 0.2384 Epoch 26/40 30/30 - 57s - loss: 0.2316 - val_loss: 0.2364 Epoch 27/40 30/30 - 57s - loss: 0.2306 - val_loss: 0.2397 Epoch 28/40 30/30 - 57s - loss: 0.2282 - val_loss: 0.2343 Epoch 29/40 30/30 - 57s - loss: 0.2242 - val_loss: 0.2392 Epoch 30/40 30/30 - 57s - loss: 0.2232 - val_loss: 0.2326 Epoch 31/40 30/30 - 57s - loss: 0.2207 - val_loss: 0.2318 Epoch 32/40 30/30 - 57s - loss: 0.2192 - val_loss: 0.2339 Epoch 33/40 30/30 - 57s - loss: 0.2175 - val_loss: 0.2287 Epoch 34/40 30/30 - 57s - loss: 0.2160 - val_loss: 0.2310 Epoch 35/40 30/30 - 57s - loss: 0.2137 - val_loss: 0.2299 Epoch 36/40 30/30 - 57s - loss: 0.2119 - val_loss: 0.2288 Epoch 37/40 30/30 - 57s - loss: 0.2101 - val_loss: 0.2271 Epoch 38/40 30/30 - 57s - loss: 0.2088 - val_loss: 0.2274 Epoch 39/40 30/30 - 57s - loss: 0.2082 - val_loss: 0.2265 Epoch 40/40 30/30 - 57s - loss: 0.2064 - val_loss: 0.2276

## Evaluate training history

Both validation and training loss were reduced until 20 epochs. The validation loss became flat after 35 so in my opinion, we should test results on both 20 and 35 epochs.

if Model_Train: fig = px.line( history.history, y=["loss", "val_loss"], labels={"index": "epoch", "value": "MCRMSE"}, title="History", ) fig.show()

## Loading models and making predictions

The test dataset was divided into public and private sets that have different sequence lengths, so in order to predict degradation on different lengths, we need to build 2 different models and load our saved checkpoints. This is possible because RNN models can accept sequences of varying lengths as inputs. Artificial Intelligence(https://ai.stackexchange.com/questions/2008/how-can-neural-networks-deal-with-varying-input-sizes)

#### Loading weights

model_public = build_model(seq_len=107, pred_len=107, embed_size=len(token2int)) model_private = build_model(seq_len=130, pred_len=130, embed_size=len(token2int)) model_public.load_weights("Model/model.h5") model_private.load_weights("Model/model.h5")

#### Prediction

We have successfully predicted for both public and private data sets. In the next step, we will be combining them using test id.

public_preds = model_public.predict(public_inputs) private_preds = model_private.predict(private_inputs)

private_preds.shape

(3005, 130, 5)

## Post-processing and submit

**Converting 3D NumPy array into Data frame:**

- combining both private (
**df,prediction )**and**public(df,prediction)**data frame. - Adding series of integers in front of id based on a sequence of single prediction for example
`[id_00073f8be_0,id_00073f8be_1,id_00073f8be_2 ..]`

- Concatenating all of the data into Pandas Dataframe and preparing for submission.

preds_ls = [] for df, preds in [(public_df, public_preds), (private_df, private_preds)]: for i, uid in enumerate(df.id): single_pred = preds[i] single_df = pd.DataFrame(single_pred, columns=target_cols) single_df["id_seqpos"] = [f"{uid}_{x}" for x in range(single_df.shape[0])] preds_ls.append(single_df) preds_df = pd.concat(preds_ls) preds_df.head()

reactivity deg_Mg_pH10 deg_Mg_50C deg_pH10 deg_50C id_seqpos0 0.685760 0.703746 0.585288 1.857178 0.808561 id_00073f8be_0 1 2.158555 3.243329 3.443042 4.394709 3.012130 id_00073f8be_1 2 1.432280 0.674404 0.672512 0.662341 0.718279 id_00073f8be_2 3 1.296234 1.306208 1.898748 1.324560 1.827133 id_00073f8be_3 4 0.851104 0.670810 0.971952 0.573919 0.962205 id_00073f8be_4

## Submission

Merging sample data frame with predicted on `id_seqpos`

to avoid repetition and make sure it follows submission format. Finally, save our data frame into .csv file.

submission = sample_df[["id_seqpos"]].merge(preds_df, on=["id_seqpos"]) submission.to_csv("Submission/submission.csv", index=False)

submission.head()

id_seqpos reactivity deg_Mg_pH10 deg_Mg_50C deg_pH10 deg_50C0 id_00073f8be_0 0.685760 0.703746 0.585288 1.857178 0.808561 1 id_00073f8be_1 2.158555 3.243329 3.443042 4.394709 3.012130 2 id_00073f8be_2 1.432280 0.674404 0.672512 0.662341 0.718279 3 id_00073f8be_3 1.296234 1.306208 1.898748 1.324560 1.827133 4 id_00073f8be_4 0.851104 0.670810 0.971952 0.573919 0.962205

## Private Score

After submission into OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction | Kaggle we got a private score of 0.2723 which is quite better. This result was produced on **50** epochs as I was trying different parameters to improve my score.

## Conclusion

This was a unique experience for me as I was dealing with JSON files with multiple arrays within single samples. After figuring out how to use that data the challenge became quite simple and the Kaggle community have a bigger part in helping me solve this problem. This article was purely model-based and apart from it I have explored the dataset and used data analysis to make sense of some of the common patterns. I wanted to include my experiments with other gradient boosting and LSTM models, but I wanted to present the best possible model.

We have used JSON files and converted them into tokenized 3D Numpy arrays and then used the 3X GRU model. Finally, we have used saved weights to create distinct models for varying lengths of the RNA sequence. I will suggest you use my code and play around to check whether you can beat my score on the leader board.

## Source Code

You can find the source code on dagshub and Deepnote Project.

## Additional Data

**How RNA vaccines work, and their issues: **https://www.pbs.org/wgbh/nova/video/rna-coronavirus-vaccine/

**Launch of the OpenVaccine challenge:** https://scopeblog.stanford.edu/2020/05/20/stanford-biochemist-works-with-gamers-to-develop-covid-19-vaccine/

**The impossibility of mass immunization: **https://www.wsj.com/articles/from-freezer-farms-to-jets-logistics-operators-prepare-for-a-covid-19-vaccine-11598639012

**Eterna, the crowdsourcing platform for RNA design:** https://eternagame.org

### References

- Image 1 – https://news.harvard.edu/gazette/story/newsplus/harvard-establishes-mrna-immunotherapy-research-collaboration-with-moderna/
- Image 2 -https://news.harvard.edu/gazette/story/newsplus/harvard-establishes-mrna-immunotherapy-research-collaboration-with-moderna/
- Data – https://www.kaggle.com/c/stanford-covid-vaccine/data

### Author

**Abid Ali Awan**

I am a certified data scientist professional, who loves building machine learning models and research on the latest AI technologies. I am currently testing AI Products at PEC-PITC, which later get approved for human trials for example Breast Cancer Classifier.

**You can follow me on LinkedIn, Twitter, and Polywork where I post my article on weekly basis.**

**The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.**