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PSTAAP.py

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+
+import numpy as np
+import scipy.io
+import os
+
+
+def PSTAAP_feature(protein_sequences, test_PSTAAP=False):
+    for i in range(len(protein_sequences)):
+        protein_sequences[i] = protein_sequences[i][:24] + protein_sequences[i][25:]
+
+    if test_PSTAAP:
+        mat_contents = scipy.io.loadmat("Feature_extraction_algorithms/Fr_test.mat")
+    else:
+        mat_contents = scipy.io.loadmat("Feature_extraction_algorithms/Fr_train.mat")
+
+    Fr = mat_contents['Fr']
+    """
+    print(Fr[0*400+5*20+0,0])
+    print(Fr[5 * 400 + 0 * 20 + 16, 1])
+    print(Fr[0 * 400 + 16 * 20 + 14, 2])
+    """
+    AA = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
+    PSTAAP = np.zeros((len(protein_sequences), 46))
+    for i in range(len(protein_sequences)):
+        for j in range(len(protein_sequences[0])-2):
+            t1 = protein_sequences[i][j]
+            position1 = AA.index(t1)
+            t2 = protein_sequences[i][j+1]
+            position2 = AA.index(t2)
+            t3 = protein_sequences[i][j+2]
+            position3 = AA.index(t3)
+
+            PSTAAP[i][j] = Fr[400 * position1 + 20 * position2 + position3][j]
+
+    return PSTAAP
+
+
+if __name__ == '__main__':
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from scipy.interpolate import splrep, BSpline
+    from sklearn.preprocessing import MinMaxScaler
+    from numpy.polynomial import Polynomial
+
+
+    def plot_multiple_polynomial_fitted_functions(sample_datas, degree=3):
+        markers = ["o", "o", "^", "^", "v", "p"]
+        colors = ["b", "b", "c", "c", "m", "y"]
+        label = ["sample1(1,0,0,0)", "sample1(1,0,0,0)", "sample1(0,1,0,0)", "sample2(0,1,0,0)", "sample3(0,0,1,0)", "sample6(0,0,0,1)"]
+        plt.figure(figsize=(12, 6))
+
+        for i, sample_data in enumerate(sample_datas):
+            if i == 0 or i == 1 or i == 4 or i == 5:
+                continue
+            # 无量纲化处理
+            scaler = MinMaxScaler()
+            normalized_data = scaler.fit_transform(sample_data.reshape(-1, 1)).flatten()
+            # 拟合多项式函数
+            x = np.linspace(0, 1, len(normalized_data))
+            p = Polynomial.fit(x, normalized_data, degree)
+            y_poly = p(x)
+            # 计算极值点
+            dy_poly = p.deriv(1)(x)
+            extrema_indices = np.where(np.diff(np.sign(dy_poly)))[0]
+            extrema_x = x[extrema_indices]
+            extrema_y = y_poly[extrema_indices]
+
+            plt.plot(x, y_poly, label=f'{label[i]}', marker=markers[i], color=colors[i])
+            plt.plot(extrema_x, extrema_y, 'rx', markersize=10)  # 标记极值点
+
+        plt.xlabel('X')
+        plt.ylabel('Y')
+        plt.title('Fitted Polynomial Functions with Extrema')
+        plt.legend()
+        plt.show()
+
+    def plot_multiple_fitted_functions(sample_datas, smooth_factor=1):
+        markers = ["o", "o", "^", "^", "v", "p"]
+        colors = ["b", "b", "c", "c", "m", "y"]
+        label = ["", "", "sample1(0,1,0,0)", "sample2(0,1,0,0)", "sample3(0,0,1,0)", ""]
+        plt.figure(figsize=(12, 6))
+
+        for i, sample_data in enumerate(sample_datas):
+            if i == 0 or i == 1 or i == 4 or i == 5:
+                continue
+
+            scaler = MinMaxScaler()
+            normalized_data = scaler.fit_transform(sample_data.reshape(-1, 1)).flatten()
+
+            x = np.linspace(0, 1, len(normalized_data))
+            tck = splrep(x, normalized_data, k=3, s=smooth_factor)
+            spline = BSpline(tck[0], tck[1], tck[2])
+
+            y_spline = spline(x)
+            dy_spline = spline.derivative()
+            extrema_indices = np.where(np.diff(np.sign(dy_spline(x))))[0]
+            extrema_x = x[extrema_indices]
+            extrema_y = y_spline[extrema_indices]
+
+            plt.plot(x, y_spline, label=f'{label[i]}', marker=markers[i], color=colors[i])
+            plt.plot(extrema_x, extrema_y, 'rx', markersize=10)
+
+        plt.xlabel('X')
+        plt.ylabel('Y')
+        plt.title('Fitted B-Spline Functions with Extrema')
+        plt.legend()
+        plt.show()
+
+    protein_sequences = [
+        "TSPASVASSSSTPSSKTKDLGHNDKSSTPGLKSNTPTPRNDAPTPGTST",  # a
+        "LGGNIEQLVARSNILTLMYQCMQDKMPEVRQSSFALLGDLTKACFQHVK",  # a
+        "VDFQHASEDARKTINQWVKGQTEGKIPELLASGMVDNMTKLVLVNAIYF",  # c
+        "VEGTLKGPEVDLKGPRLDFEGPDAKLSGPSLKMPSLEISAPKVTAPDVD",  # c
+        "IDILTSREQFFSDEERKYMAINQKKAYILVTPLKSRKVIEQRCMRYNLS",  # m
+        "LAGTDGETTTQGLDGLSERCAQYKKDGADFAKWRCVLKISERTPSALAI",  # s
+    ]
+    data = PSTAAP_feature(protein_sequences, False)
+
+    # 调用绘图函数
+    plot_multiple_polynomial_fitted_functions(data)
+    plot_multiple_fitted_functions(data)

Physicochemical.py

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+import pandas as pd
+import numpy as np
+
+
+data_dict = {
+    "A": [0.108555943, 0.405506884, 0.955223881, 0.896118721, 0, 1, 1, 1, 0, 0, 1, 0],
+    "R": [0.767557104, 1, 0.686567164, 0.801369863, 1.191, 1, 1, 1, 1, 1, 0, 0],
+    "N": [0.441656988, 0.330413016, 0.462686567, 0, 0, 1, 1, 1, 0, 1, 0, 0],
+    "D": [0.449322493, 0, 0.253731343, 0.865296804, 0.374, 1, 1, 1, -1, 1, 1, 0],
+    "C": [0.356871854, 0.285356696, 0, 1, 0.793, 1, 1, 0, 0, 1, 0, 1],
+    "Q": [0.55029036, 0.360450563, 0.686567164, 0.811643836, 0, 1, 1, 1, 0, 1, 0, 0],
+    "E": [0.557955865, 0.056320401, 0.71641791, 0.873287671, 0.403, 1, 1, 1, -1, 1, 1, 0],
+    "G": [0, 0.400500626, 0.955223881, 0.885844749, 0, 0, 1, 1, 0, 1, 1, 0],
+    "H": [0.62013163, 0.603254068, 0.164179104, 0.816210046, 0.569, 1, 1, 1, 1, 1, 1, 0.8],
+    "I": [0.43437863, 0.406758448, 0.910447761, 0.883561644, 0, 1, 0, 1, 0, 0, 0, 0],
+    "L": [0.43437863, 0.40175219, 0.970149254, 0.865296804, 0, 1, 0, 1, 0, 0, 0, 0],
+    "K": [0.550677507, 0.872340426, 0.701492537, 0.79109589, 1, 1, 1, 1, 1, 1, 0, 0],
+    "M": [0.574061169, 0.371714643, 0.850746269, 0.820776256, 0, 1, 0, 1, 0, 0, 0.1, 0],
+    "F": [0.697793264, 0.339173967, 0.179104478, 0.811643836, 0, 1, 0, 1, 0, 0, 0.1, 0.9],
+    "P": [0.310181959, 0.441802253, 0.417910448, 0.979452055, 0, 1, 1, 1, 0, 0.5, 1, 0],
+    "S": [0.232442896, 0.364205257, 0.746268657, 0.813926941, 0, 1, 1, 1, 0, 1, 1, 0],
+    "T": [0.341076268, 0.42428035, 0.567164179, 0.808219178, 0, 1, 1, 1, 0, 1, 0, 0],
+    "W": [1, 0.39048811, 1, 0.841324201, 0, 1, 0, 1, 0, 0, 0, 0.8],
+    "Y": [0.821680217, 0.361702128, 0.731343284, 0.809360731, 0.961, 1, 0, 1, 0, 0.5, 1, 0.8],
+    "V": [0.325822687, 0.399249061, 0.865671642, 0.878995434, 0, 1, 0, 1, 0, 0, 0, 0]
+}
+
+
+data_new_dict = {
+    "G": [0.0, 0.5363372093023252, -1],
+    "A": [0.07347447047353533, 0.5534883720930233, 1],
+    "T": [0.2366637964906612, 0.48837209302325573, -1],
+    "S": [0.15120012868167378, 0.372093023255814, -1],
+    "P": [0.21957275509630982, 0.7209302325581398, -1],
+    "V": [0.22993840656583635, 0.5363372093023252, 1],
+    "L": [0.31981981981981985, 0.5416666666666666, 1],
+    "I": [0.31981981981981985, 0.5552325581395349, 1],
+    "M": [0.4250240432219403, 0.4011627906976744, 1],
+    "F": [0.5205540765087008, 0.2558139534883721, 1],
+    "Y": [0.6151618372354518, 0.36046511627906974, 1],
+    "W": [0.740630755511022, 0.4976744186046512, 1],
+    "D": [0.33248277702242695, 0.0, -1],
+    "E": [0.409931793152727, 0.06976744186046511, -1],
+    "N": [0.32651104755672825, 0.2674418604651163, -1],
+    "Q": [0.40479484311833796, 0.34883720930232553, -1],
+    "K": [0.40452036659863644, 1.0, -1],
+    "R": [0.5447355923482843, 1.2325581395348837, -1],
+    "H": [0.47177217934844545, 0.8488372093023256, 1],
+    "C": [0.24070355568601118, 0.20348837209302326, 1]
+}
+
+
+def PC_feature(seq):
+    feature = []
+    for i in range(len(seq)):
+        f = [data_new_dict[aa] for aa in seq[i]]
+        feature.append(f)
+    return np.array(feature)
+
+
+"""
+seq = ["AAC", 'CRN']
+output = PC_feature(seq)
+print(output)
+print(output.shape)
+"""
+
+