ST_Independent_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c05bcc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120476, 51) (120476,)\n",
      "Load the Prot_T5 Index datasets\n",
      "Predict and Test the independent datasets\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from numpy import sort\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import *\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, classification_report,auc\n",
    "%matplotlib inline\n",
    "import pickle\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.keras.layers import Dense, Bidirectional,Dense, LSTM, Activation, Dropout, Flatten\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from sklearn.metrics import accuracy_score\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras import regularizers\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "import os \n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score,matthews_corrcoef\n",
    "from tensorflow.keras.callbacks import Callback,EarlyStopping\n",
    "import random\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras import backend as K\n",
    "import os\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, recall_score, matthews_corrcoef, roc_curve, auc\n",
    "from tensorflow.keras import backend as K\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "import tensorflow\n",
    "import tensorflow as tf\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Reshape\n",
    "from tensorflow.keras.layers import Conv1D,Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras import utils\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.optimizers import Adam,SGD\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import copy\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from Bio import SeqIO\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Reshape, Lambda, LSTM\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D\n",
    "from tensorflow.keras.layers import Bidirectional, TimeDistributed\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,ReduceLROnPlateau\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.backend import expand_dims\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.regularizers import l1, l2\n",
    "from sklearn.metrics import roc_curve, auc, classification_report\n",
    "from tensorflow.keras.models import load_model\n",
    "import pandas as pd\n",
    "from tensorflow.keras.layers import Conv1D,Conv2D, MaxPooling2D,GlobalMaxPooling1D\n",
    "from Bio import SeqIO\n",
    "import random\n",
    "import functools\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Reshape, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv1D, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import copy\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "cut_off = int(15)\n",
    "alphabet = 'ARNDCQEGHILKMFPSTWYV-'\n",
    "\n",
    "char_to_int = dict((c, i) for i, c in enumerate(alphabet))\n",
    "int_to_char = dict((i, c) for i, c in enumerate(alphabet))\n",
    "test_x_51 = []\n",
    "test_y_51 = []\n",
    "posit_1 = 1;\n",
    "negat_0 = 0;\n",
    "\n",
    "allpos = []\n",
    "allneg = []\n",
    "\n",
    "# TRAIN DATASET -------------------------------------------------------------\n",
    "#for positive sequence\n",
    "def inner_1():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "    data = data[cut_off:-cut_off]\n",
    "    allpos.append(data)\n",
    "    for char in data:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in data]\n",
    "    test_x_51.append(integer_encoded)\n",
    "    test_y_51.append(posit_1)\n",
    "for seq_record in SeqIO.parse(\"data/DeepPSP_81_window_Test_Positive_18553 (1).fasta\", \"fasta\"):\n",
    "    inner_1()\n",
    "# for negative sequence\n",
    "def inner_2():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "    data = data[cut_off:-cut_off]\n",
    "    allneg.append(data)\n",
    "    for char in data:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in data]\n",
    "    test_x_51.append(integer_encoded)\n",
    "    test_y_51.append(negat_0)\n",
    "for seq_record in SeqIO.parse(\"data/DeepPSP_81_window_Test_Negative_101944 (2).fasta\", \"fasta\"):\n",
    "    inner_2()\n",
    "x_test_51 = array(test_x_51)\n",
    "y_test_51 = array(test_y_51)\n",
    "\n",
    "print(x_test_51.shape,y_test_51.shape)\n",
    "\n",
    "x_test_41 = x_test_51[:,[i for i in range(5,46)]]\n",
    "x_test_33 = x_test_41[:,[i for i in range(4,37)]]\n",
    "x_test_21 = x_test_33[:,[i for i in range(6,27)]]\n",
    "\n",
    "AAindex_Test_AA_Sequence = []\n",
    "for i in range(len(x_test_51)):\n",
    "    integer_of_string = str(x_test_51[i])\n",
    "    integer_of_string = integer_of_string.strip(\"[\").strip(\"]\").strip(\" \")\n",
    "    keras_job = integer_of_string.split()\n",
    "    individual_AA_peptide = []\n",
    "    for i in range(len(keras_job)):\n",
    "        index_value = int(keras_job[i])\n",
    "        individual_AA_peptide.append(int_to_char.get(index_value))\n",
    "    individual_AA_peptide = \"\".join(individual_AA_peptide)\n",
    "    AAindex_Test_AA_Sequence.append(individual_AA_peptide)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "print(\"Load the Prot_T5 Index datasets\")\n",
    "\n",
    "aminoacids='ARNDCQEGHILKMFPSTWYV-'\n",
    "\n",
    "def binary_encode(Sequence_one_hot_encoding):     \n",
    "    aa2v={x:y for x,y in zip(aminoacids,np.eye(21,21).tolist())}\n",
    "    aa2v['X']=np.zeros(21)\n",
    "    encoding=[]\n",
    "    for i in range(len(Sequence_one_hot_encoding)):\n",
    "        s=Sequence_one_hot_encoding[i]\n",
    "        encoding.append([aa2v[x] for x in s])\n",
    "    encoding=np.array(encoding)\n",
    "    return encoding\n",
    "\n",
    "Header_name = [\"PID\",\"Position\",\"Sequence\",\"Middle_Amino_Acid\"]\n",
    "\n",
    "col_of_feature = [i for i in range(1,1025)]\n",
    "\n",
    "Target = [\"Target\"]\n",
    "\n",
    "Header_name = Header_name + col_of_feature + Target\n",
    "\n",
    "df_Test_Negative = pd.read_csv(\"data/Index_DeepPSP_33_window_Test_Negative.txt\",header=None)\n",
    "df_Test_Negative.columns = Header_name\n",
    "\n",
    "df_Test_Positive = pd.read_csv(\"data/Index_DeepPSP_33_window_Test_Positive.txt\",header=None)\n",
    "df_Test_Positive.columns = Header_name\n",
    "\n",
    "frames = [df_Test_Positive, df_Test_Negative]\n",
    "\n",
    "total_mean_deeppsp_testing = pd.concat(frames,ignore_index = True)\n",
    "\n",
    "df_Test_array = total_mean_deeppsp_testing.drop([\"PID\",\"Position\",\"Sequence\",\"Middle_Amino_Acid\",\"Target\"],axis=1)\n",
    "df_Test_array = np.array(df_Test_array)\n",
    "\n",
    "df_Test_Sequence = np.array(total_mean_deeppsp_testing[\"Sequence\"])\n",
    "\n",
    "dictionary_Test_Set = dict(zip(df_Test_Sequence,df_Test_array))\n",
    "\n",
    "Test_array = []\n",
    "for i in range(len(AAindex_Test_AA_Sequence)):\n",
    "    AAindex_Test_AA = AAindex_Test_AA_Sequence[i][9:-9]\n",
    "    sequences_of_test_sequence = AAindex_Test_AA.strip(\"-\")\n",
    "    value_of_the_sequence =dictionary_Test_Set.get(sequences_of_test_sequence)\n",
    "    Test_array.append(value_of_the_sequence)\n",
    "\n",
    "Prot_T5_Test_array = np.array(Test_array)\n",
    "\n",
    "binaryCoding_Independent_Test = binary_encode(AAindex_Test_AA_Sequence)\n",
    "\n",
    "y_test = y_test_51\n",
    "print(\"Predict and Test the independent datasets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0cc59f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "EM_Prot_model = keras.models.load_model('models/Again_job_was_performed_stacked_generilization_model_5112305.h5')\n",
    "\n",
    "Y_pred1_EM_Prot = EM_Prot_model.predict([x_test_51, Prot_T5_Test_array])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97e4a747",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matthews Correlation :  0.39059338038647223\n",
      "Confusion Matrix : \n",
      " [[82275 19650]\n",
      " [ 6099 12452]]\n",
      "Test accuracy: 0.7862727846210034\n",
      "Specificity =  0.8072111846946284  Sensitivity =  0.6712306614198695\n"
     ]
    }
   ],
   "source": [
    "yhat = argmax(Y_pred1_EM_Prot, axis=1)\n",
    "r_test_y = y_test\n",
    "print(\"Matthews Correlation : \",matthews_corrcoef(r_test_y, yhat))\n",
    "print(\"Confusion Matrix : \\n\",confusion_matrix(r_test_y, yhat))\n",
    "print('Test accuracy:', accuracy_score( r_test_y, yhat))\n",
    "sp_1, sn_1 = confusion_matrix(r_test_y, yhat)\n",
    "sp_2 = sp_1[0]/(sp_1[0]+sp_1[1])\n",
    "sn_2 = sn_1[1]/(sn_1[0]+sn_1[1])\n",
    "print(\"Specificity = \",sp_2, \" Sensitivity = \",sn_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e87782b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(EM_Prot_model,show_shapes = True, to_file='Embdding+ProtT5.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a63bdc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_135\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "embedding_layer_input (InputLay [(None, 51)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_embedding_layer (Emb (None, 51, 21)       483         embedding_layer_input[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_Conv1D_1 (Conv1D)    (None, 49, 128)      8192        ensemble_1_embedding_layer[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_drop_out_1 (Dropout) (None, 49, 128)      0           ensemble_1_Conv1D_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_max_pooling1d_146 (M (None, 24, 128)      0           ensemble_1_drop_out_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_Conv1D_3 (Conv1D)    (None, 22, 64)       24640       ensemble_1_max_pooling1d_146[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_drop_out_3 (Dropout) (None, 22, 64)       0           ensemble_1_Conv1D_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "input_78 (InputLayer)           [(None, 1024)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_max_pooling1d_147 (M (None, 11, 64)       0           ensemble_1_drop_out_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_Dense_1 (Dense)      (None, 128)          131200      input_78[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_flatten_73 (Flatten) (None, 704)          0           ensemble_1_max_pooling1d_147[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_dropout_154 (Dropout (None, 128)          0           ensemble_2_Dense_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_dense_penultimate_la (None, 256)          180480      ensemble_1_flatten_73[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_Dense_2 (Dense)      (None, 32)           4128        ensemble_2_dropout_154[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_drop_out_bilstm_3 (D (None, 256)          0           ensemble_1_dense_penultimate_laye\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_dropout_155 (Dropout (None, 32)           0           ensemble_2_Dense_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_classification_layer (None, 1)            257         ensemble_1_drop_out_bilstm_3[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_dense_77 (Dense)     (None, 1)            33          ensemble_2_dropout_155[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_67 (Concatenate)    (None, 2)            0           ensemble_1_classification_layer_l\n",
      "                                                                 ensemble_2_dense_77[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dense_134 (Dense)               (None, 2)            6           concatenate_67[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_135 (Dense)               (None, 2)            6           dense_134[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 349,425\n",
      "Trainable params: 12\n",
      "Non-trainable params: 349,413\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "EM_Prot_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "300a6ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "CM = confusion_matrix(r_test_y, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "009651a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[82275, 19650],\n",
       "       [ 6099, 12452]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "712de9fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = CM[1][1]\n",
    "TN = CM[0][0]\n",
    "FN = CM[1][0]\n",
    "FP = CM[0][1]\n",
    "Recall=  (TP/(TP+FN))*100\n",
    "FSCORE = 2*((Precision*Recall)/(Precision+Recall))\n",
    "Precision=  TP/(TP+FP)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d837466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.78886050713351"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afeb7af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.12306614198695"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e33d8f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.165893431780944"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FSCORE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc4ed62",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}