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Solution to Kaggle competition - Pfam seed random split

The detailed python notebook can be found here.

Problem Setup

Classify sample of protein sequence to a domain family_accession label. Each sample is a protein sequence which has different length from about 20 to about 1900. There are nearly 18000 domain family_accession labels.So it is a multi-classification problem with large dataset and big feature space.

Data Structure

There are three sets of data: train, dev, and test.

  • The train set contains 1,086,741 samples.

  • The dev set contains 126,171 samples.

  • The test set contains 126,171 samples. I will not use it during the whole process.

Each sample contains information of:


Description of fields:

  • sequence: These are usually the input features to your model. Amino acid sequence for this domain. There are 20 very common amino acids (frequency > 1,000,000), and 4 amino acids that are quite uncommon: X, U, B, O, Z.

  • family_accession: These are usually the labels for your model. Accession number in form PFxxxxx.y (Pfam), where xxxxx is the family accession, and y is the version number. Some values of y are greater than ten, and so 'y' has two digits.

  • family_id: One word name for family.

  • sequence_name: Sequence name, in the form "uniprot_accession_id/start_index-end_index".

  • aligned_sequence: Contains a single sequence from the multiple sequence alignment (with the rest of the members of the family in seed, with gaps retained.

Input : The input can be either sequence data or aligned_sequence data.

Output: The output label is family_accession

Data Analysis

Label (refer to Python notebook section 2.1)

Check how many labels do train/dev/test set have.

  • Check the label in one train fileThe number of unique family id or unique family_accession are both 6176 in just one file. It means there are a big number of labels. unique family_id has the same number as family_accession. So I will only look at family_accession.

  • family_accession has two parts x and y. Check the difference.PFxxxx.yThere are 6176 unique value for the front part x and 33 unique values for the back part y.It means only the front part contributes to the labels. The back part probably mean something else.

  • Double check for all files to make sure my analysis is consistent in all files.Confirmed

Check the number of samples in each family_accession. (Python notebook section2.1.1)

  • The sample numbers in each family_accession label are different. It could be either 1 or over 3500.

  • There are only 13071 labels in the dev set and 17929 labels in the train set. So there are a lot samples we do not need. But we cannot guarantee that the labels will not show up in the test set. So we can remove the ones with less training sample.

  • After examination, I found out that every label which showed up in dev set has more than 7 training samples in the train set. So I remove the ones which have less than 8 training samples within the label.

The train set filter process (Python notebook section 2.1.2)

  • I kept 1,072,097 samples in the train set with 13912 labels.

Check on the back part of the label to see any difference (Python notebook section 2.1.3)

  • TODO

Sequence (Python notebook section 2.2)

Check the number of uncommon amino acids in all samples

  • There are only 747 sequence containing uncommon amino acids.

Check the length of sequence (Number of amino acids)

  • The length ranges from about 20 up to 1972.

  • The unique amino acids are

  • By comparing to published unique amino acids list. I used this numeric encoding dictionary str_to_int (in Python notebook section 2.3.

Check whether the number of unique amino acids in each sample has correlations with each other.

  • Z, U, B, X, O are almost indepandent with the others. It fits the discription that they are uncommon.

  • Z is strongly correlated with B, But due to the small occurrence, it does not mean anything.

Check if they have any relationship with the front part of family_accession

  • Nothing observed

Check if they have any relationship with the back part of family_accession

  • It looks like the back part of family_accession is related to the number of unique amino acid in each sample.

  • TODO - cluster samples based on the back part of family_accession. Run classification in each cluster

Aligned sequence (Python notebook section 2.3)

Encode aligned sequence and check how they are related in each family

  • Notice that the length of aligned sequence are the same within each family_accession

  • Notice that some families have the same length of aligned sequence.

  • Notice that the aligned sequence in the same family have similar pattern.

Classification strategy

  • In the samples with the same aligned_sequence length, calculate the centroid for each acid position in one family. whichever family centroid is closer to the new sample will be the family label.

Model development 1

Use aligned_sequence as input (Python notebook section 3.1)

  • Find the length of inputs.

  • Each length contains 1 to many families.

  • Save the length with corresponding families

Develop model (Python notebook section 4.1)

  • For one specific length, Find all the training set and corresponding families.

  • Calculate the centroid of the family samples.

  • Get a new sample for dev set with the same input length, calculate the norm between the new sample and each family centroid.

  • Assign the closest centroid as the predicted family label

  • Result proven to be 100% accurate every time.

  • I did not explore the whole dataset. From the random 20 length I checked, the accuracy remains the same.

Model development 2

Use sequence as input (Python notebook section 3.2)

Since protein sequence cannot always be aligned nicely and manually checked, It is worth to work with sequence as input. There are two feature extraction methods I am testing. One is general Autoencoder (Python notebook section 3.2.1). Another one is UniRep (Python notebook section 3.2.2). Either of these methods were probably tuned and tested. But I hope you get an idea of what I want to do.

Both methods requires zero padding for the input as they have very different length and need to be fed to a model. Since some samples have only about 20 acids and some have over 1900 acids, they cannot all be padded to the same length. Hence, I am applying gridded padding to the input.

For instance, If I have samples as:

input = [[9,2,4,2], [4,5], [6,7,8,3,66,4,3]]

I want to pad them in length with steps of 5. It means I will pad them to either 5 or 10 or 15 and etc.

So the padded input becomes

input = [[9,2,4,2,0], [4,5,0,0,0], [6,7,8,3,66,4,3,0,0,0]]

The Input padding algorithm are written in Python notebook section 3.2.1. A similar method is embedded in online UniRep JAX package but might be slightly different. It is not shown in the Python notebook section 3.2.2 but used for the feature extraction there.

Feature analysis (Python notebook section 3.2.3)

Analysis for Autoencoder feature:

  • PC1 has range from 0 to 300 while PC2 is 10000 times smaller and PC3 is even worse. Potentially the PCA need to be whitened.

  • The PCs forms connecting straight lines ==>

  • The colour does not change smoothly for both front of the family and back of the family.

Next step

  • try DBscan to cluster samples --> Proved that the clustering is not good in the later analysis.

  • Try decision tree.

Analysis for UniRep feature:

  • The PCA feature does not show obvious clustering from the observation ==> no unsupervised clustering.

  • It does not suit few-shot learning unless it is expanded to higher dimension with the help of blessing of dimensionality. But I do not recommand that as 1900 is already a big dimension.

Next step:

  • try some simple fast classifier

Develop model (Python notebook section 4.2)

Define metric (Python notebook section 4.2.1)

If I am using regression to solve this classification problem, I can use Mean Squared Error as the metrics.

Tested some conventional ML method using extracted features to classify train set as a whole (Python notebook section

  • Neural network

  • Decision Tree

Tested some conventional ML methods using extracted features to cluster data first (Python notebook section

  • DBscan

  • Spectral cluster

Tested few-shot learning using extracted features to run binary classification (Python notebook section

  • The confusion matrix show that it performs all right but not 100% accurate.

Improvement (TODO)

  • Few shot learning but with clustering using the back of family_accession as shown in the end of Python notebook section 2.2

  • Improve on the feature extraction. The current feature extraction is not good at all.

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