python计算蛋白质序列的KNN得分

前面已经给出了计算 KNN 得分的 Python 实现，这里再给出一个完整的示例，包括读取蛋白质序列数据和计算 KNN 得分：

import numpy as np

def knn_encode(protein_seq, k=3):
    """
    K-nearest neighbor coding for protein sequences.
    Args:
        protein_seq: str, the protein sequence.
        k: int, the parameter k for KNN encoding.
    Returns:
        A numpy array with length 20*k.
    """
    amino_acids = 'ACDEFGHIKLMNPQRSTVWY'
    aa_map = {aa: i for i, aa in enumerate(amino_acids)}
    n = len(protein_seq)
    features = np.zeros((n, 20))
    for i, aa in enumerate(protein_seq):
        if aa in aa_map:
            features[i, aa_map[aa]] = 1
    encoded = np.zeros(20*k)
    for i in range(n):
        if i >= k:
            knn_indices = np.argsort(-np.sum(features[i-k:i, :], axis=0))[:k]
        elif i < k:
            knn_indices = np.argsort(-np.sum(features[:i, :], axis=0))[:k]
        else:  # i < k and i >= n - k
            knn_indices = np.argsort(-np.sum(features[i-k:i, :], axis=0))[:k]
        encoded[knn_indices + i*20] = 1
    return encoded

def knn_score(seq1, seq2, k=3):
    """
    Calculate the KNN score between two protein sequences.
    Args:
        seq1: str, the first protein sequence.
        seq2: str, the second protein sequence.
        k: int, the parameter k for KNN encoding.
    Returns:
        A float value representing the KNN score.
    """
    encoded1 = knn_encode(seq1, k=k)
    encoded2 = knn_encode(seq2, k=k)
    distance = np.sum(np.abs(encoded1 - encoded2))
    return distance

# 读取蛋白质序列数据
with open('protein_sequences.txt') as f:
    sequences = f.read().splitlines()

# 计算任意两个序列之间的 KNN 得分
n = len(sequences)
knn_scores = np.zeros((n, n))
for i in range(n):
    for j in range(i+1, n):
        score = knn_score(sequences[i], sequences[j])
        knn_scores[i, j] = score
        knn_scores[j, i] = score

# 输出 KNN 得分矩阵
print(knn_scores)

其中，`protein_sequences.txt` 是包含多个蛋白质序列的文本文件，每行一个序列。通过将每个序列与其他序列计算 KNN 得分，可以得到一个 KNN 得分矩阵，其中第 i 行第 j 列的值为第 i 个序列和第 j 个序列之间的 KNN 得分。