基于KNN的Fasta序列分类预测：Python代码示例

以下是一个基于KNN的分类准确性预测Python代码示例，用于对一个Fasta文件中的38条子序列中分别带有由'A','C','G','T'组成的130个的序列进行分类预测：

from Bio import SeqIO
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 读取fasta文件
sequences = []
labels = []
for record in SeqIO.parse('sequences.fasta', 'fasta'):
    sequences.append(str(record.seq))
    labels.append(record.id)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(sequences, labels, test_size=0.2, random_state=42)

# 特征提取，将序列转化为数字特征
def seq_to_feature(seq):
    feature = []
    for base in seq:
        if base == 'A':
            feature.append(0)
        elif base == 'C':
            feature.append(1)
        elif base == 'G':
            feature.append(2)
        elif base == 'T':
            feature.append(3)
    return feature

X_train = [seq_to_feature(seq) for seq in X_train]
X_test = [seq_to_feature(seq) for seq in X_test]

# 训练KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

在这个示例中，我们首先使用Biopython库中的SeqIO模块读取Fasta文件，然后将序列和标签分别存储在两个列表中。接着，我们使用train_test_split函数将数据集划分为训练集和测试集。然后，我们定义了一个seq_to_feature函数，用于将序列转化为数字特征。最后，我们使用sklearn库中的KNeighborsClassifier类训练KNN分类器，并在测试集上进行预测以计算准确性。