Cancer classification system

(beta version)

This Cancer classification system was generated from our recent study "Noncoding RNAs and deep learning neural network discriminate multi-cancer types"
by Anyou Wang, Rong Hai,Paul J Rider and Qianchuan He

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. Here, we develop a comprehensive detection system to classify all common cancer types. By integrating artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data, our system can accurately detect cancer vs healthy object with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve). Intriguinely, with no more than 6 biomarkers, our approach can easily discriminate any individual cancer type vs normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify all common cancers with a stable 78% of accuracy at heterological cancerous tissues and conditions. This provides a valuable framework for large scale cancer screening.

binary classifier

  • def myModel(train_x):
  •         mymodel = Sequential([ 
              Dense(30, input_dim=len(train_x.columns), activation='relu'),
              Dropout(0.1),
              Dense(60, activation='relu'),
              Dropout(0.1),
              Dense(1, activation='sigmoid')
               ])
              mymodel.compile(optimizer='adam',loss='binary_crossentropy',
                      metrics=['accuracy'])
            return mymodel
          

    multiple classifier

  • def myModel(train_x,train_y):
  •        
       mymodel=Sequential([
          Dense(units=240,input_dim=len(train_x.columns),activation='relu'),
          Dropout(0.1),
          Dense(units=240,activation='relu'),
          Dropout(0.1),
          Dense(units=240, activation='relu'),
          Dropout(0.1),
          Dense(units=240, activation='relu'),
          Dropout(0.1),
          Dense(units=240, activation='relu'),
          Dropout(0.1),
          Dense(units=240, activation='relu'),
          Dropout(0.1),
          Dense(units=240, activation='relu'),
          Dropout(0.1),
          Dense(units=len(set(train_y)), activation='softmax')
          ])
       mymodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
       return mymodel
    
           
           
           
                 

    To search biomarker database, please enter a gene ID or symbol based on GRCh38.p2.v22

    For example,ENSG00000213700.3 or RPL17P50