DNA-Sequence文件夹为跨物种的DNA序列，识别不同物种DNA序列，建立跨物种预测模型，对这些序列进行预测识别的源代码

建立DNA序列跨物种预测模型的源代码大致如下：

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split

# 读取数据集
def read_dataset():
    data = []
    labels = []
    # 读取DNA序列数据集
    for filename in os.listdir("./DNA-Sequence"):
        with open(os.path.join("./DNA-Sequence", filename), "r") as f:
            seq = f.read().strip()
            data.append(seq)
            # 解析标签，例如：文件名为 "human_1.txt"，标签为 "human"
            label = filename.split("_")[0]
            labels.append(label)
    # 将标签转换为数字编码
    label_encoder = LabelEncoder()
    labels = label_encoder.fit_transform(labels)
    # 将数据集分为训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test

# 将DNA序列编码成数字序列
def encode_dna_sequences(sequences):
    # 将每个碱基映射到一个数字编码
    base_to_index = {"A": 0, "C": 1, "G": 2, "T": 3}
    # 将DNA序列编码成数字序列
    encoded_sequences = np.zeros((len(sequences), len(sequences[0])))
    for i, sequence in enumerate(sequences):
        for j, base in enumerate(sequence):
            encoded_sequences[i][j] = base_to_index[base]
    return encoded_sequences

# 建立CNN模型
def build_cnn_model(input_shape, num_classes):
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv1D(64, 3, activation="relu", input_shape=input_shape),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Conv1D(128, 3, activation="relu"),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Conv1D(256, 3, activation="relu"),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling1D(2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation="relu"),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation="softmax")
    ])
    return model

# 建立LSTM模型
def build_lstm_model(input_shape, num_classes):
    model = tf.keras.models.Sequential([
        tf.keras.layers.LSTM(64, input_shape=input_shape),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation="softmax")
    ])
    return model

if __name__ == "__main__":
    # 读取数据集
    X_train, X_test, y_train, y_test = read_dataset()
    # 编码DNA序列
    X_train_encoded = encode_dna_sequences(X_train)
    X_test_encoded = encode_dna_sequences(X_test)
    # 建立模型
    input_shape = (len(X_train_encoded[0]), 1)
    num_classes = len(np.unique(y_train))
    model = build_cnn_model(input_shape, num_classes)
    # 训练模型
    model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
    model.fit(X_train_encoded, y_train, epochs=10, batch_size=64, validation_data=(X_test_encoded, y_test))

在这个示例代码中，我们使用了Python中的TensorFlow和Scikit-learn库。具体而言，我们首先使用`read_dataset()`函数读取DNA序列数据集，然后使用`encode_dna_sequences()`函数将DNA序列编码成数字序列。接下来，我们使用`build_cnn_model()`和`build_lstm_model()`函数建立CNN和LSTM模型。最后，我们使用`model.fit()`函数训练模型，并在测试集上进行测试。请注意，这只是一个简单的示例代码，您可能需要根据您的具体需求进行修改。