def hierarchical(to_predict, tissue_predict, tissue_label_encoder, scaler, average, genes_list):
    for_age_prediction = pd.Series(tissue_predict.T, name='tissue_id')
    data = pd.concat([for_age_prediction, to_predict], axis=1)
    print(data)
    j=-1
    predicted_age=[]
    for i in tissue_predict:
        j+=1
        if i == 'blood':
            Blood_tissue = data.loc[j:j]
            blood_model, blood_dfs_genes = load_network('AgebyTissue/Blood/')
            blood_age_data = preprocess_data(Blood_tissue[Blood_tissue.columns[1:]], scaler, genes_list, average, blood_dfs_genes)
            age_predict = nn_predict(blood_model, blood_age_data)
            age_predict.flatten()

        elif i == 'brain':
            Brain_tissue = data.loc[j:j]
            brain_model, brain_dfs_genes = load_network('AgebyTissue/Brain/')
            brain_age_data = preprocess_data(Brain_tissue[Brain_tissue.columns[1:]], scaler, genes_list, average, brain_dfs_genes)
            age_predict = nn_predict(brain_model, brain_age_data)
            age_predict.flatten()

        elif i == 'liver':
            Liver_tissue = data.loc[j:j]
            liver_model, liver_dfs_genes = load_network('AgebyTissue/Liver/')
            liver_age_data = preprocess_data(Liver_tissue[Liver_tissue.columns[1:]], scaler, genes_list, average, liver_dfs_genes)
            age_predict = nn_predict(liver_model, liver_age_data)
            age_predict.flatten()

        elif i == 'peripheral blood mononuclear cell':
            Blood_cell_tissue = data.loc[j:j]
            blood_cell_model, blood_cell_dfs_genes = load_network('AgebyTissue/Blood_cell/')
            blood_cell_age_data = preprocess_data(Blood_cell_tissue[Blood_cell_tissue.columns[1:]], scaler, genes_list, average, blood_cell_dfs_genes)
            age_predict = nn_predict(blood_cell_model, blood_cell_age_data)
            age_predict.flatten()

        else:
            age_predict='nan'
            #return "There is no such tissue for age prediction"
        predicted_age.append(age_predict.astype(int).tolist())
    return predicted_age