|
--- |
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tags: |
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- dna_bert |
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--- |
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``` |
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NUM_CLASSES = number of the classes in your data |
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|
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from transformers import ( |
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AutoTokenizer, |
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AutoModelForSequenceClassification, |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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zhihan1996/DNA_bert_6, do_lower_case=False |
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) |
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|
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model = AutoModelForSequenceClassification.from_pretrained( |
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zhihan1996/DNA_bert_6, num_labels=NUM_CLASSES |
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) |
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|
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def return_kmer(seq, K=6): |
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""" |
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This function outputs the K-mers of a sequence |
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Parameters |
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---------- |
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seq : str |
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A single sequence to be split into K-mers |
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K : int, optional |
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The length of the K-mers, by default 6 |
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Returns |
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------- |
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kmer_seq : str |
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A string of K-mers separated by spaces |
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""" |
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|
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kmer_list = [] |
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for x in range(len(seq) - K + 1): |
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kmer_list.append(seq[x : x + K]) |
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|
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kmer_seq = " ".join(kmer_list) |
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return kmer_seq |
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|
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sequence = your DNA sequences |
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|
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train_kmers = [return_kmer(seq) for seq in sequence] |
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|
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train_encodings = tokenizer.batch_encode_plus( |
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train_kmers, |
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max_length=512, # max len of BERT |
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padding=True, |
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truncation=True, |
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return_attention_mask=True, |
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return_tensors="pt", |
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) |
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``` |
