Matlab蛋白质周转率建模代码详解

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资源摘要信息:"matlab换手率代码-protein_turnover_modelling:protein_turnover_modelling" 在生物医学研究和系统生物学领域,蛋白质周转率建模是理解蛋白质在细胞中合成与降解速率及其调控机制的重要工具。本资源介绍了一套在MATLAB环境下实现的蛋白质周转率建模工具集,该工具集利用了动态建模方法,对数据执行分析,并提供了相关的源代码和执行文件。 1. MATLAB编程环境: MATLAB(矩阵实验室)是一种用于数值计算、可视化以及编程的高级语言和交互式环境。在本资源中,代码已被测试在MATLAB版本2018b上运行。MATLAB提供了丰富的工具箱(Toolbox),能够帮助科研人员解决从数据处理、图像分析到高级统计、数值计算等问题。在本资源中,MATLAB被用于实现蛋白质周转率的数学建模和仿真。 2. 蛋白质周转率建模: 蛋白质周转率是指蛋白质在细胞内合成与降解的动态平衡状态,它反映了细胞内部蛋白质代谢的速率。在生物体中,蛋白质的合成和降解是生命活动的重要组成部分,对维持细胞功能和结构完整性具有重要作用。蛋白质周转率的改变可能会影响细胞功能,甚至与疾病的发生发展相关联。因此,对蛋白质周转率的研究有助于揭示细胞内的分子调控机制以及疾病病理过程。 3. 模型实现方法: 在本资源中,通过编写MATLAB代码实现蛋白质周转率模型。模型的实现基于以下步骤: - 逆行建模(anterograde_model.m): 输入拟合的mRNA数据和逻辑参数,计算合成和降解的周转率,并调用函数[anterograde_funct.m]。这里需要解析mRNA数据来模拟蛋白质表达P(t)的动态过程,并通过非线性回归方法来计算合成速率Sp和降解速率Dp。 - 逆向建模(retrograde_model.m): 类似于逆行建模,但输入的是实验蛋白表达P(t),并利用此表达数据来解析mRNA M(t)的动态过程。同样采用非线性回归方法计算合成和降解速率Sp和Dp。 4. 文件结构与功能: 资源中的文件夹结构被命名为"protein_turnover_modelling-master",暗示了该资源可能是一个包含多个文件和子文件夹的项目或库。文件结构可能包括模型的核心实现文件、辅助函数、示例数据集、测试脚本以及用户指南等。 5. 开源信息: 本资源标记为“系统开源”,表明相关的源代码以及可能的文档资料可以被研究者自由地访问、使用和修改。开源可以促进知识分享、合作开发以及软件工具的持续改进,对于科研社区来说,开源模型和工具的共享尤为重要,它有助于加快科学发现和技术创新的进程。 6. 作者与联系信息: 代码由Stefania Marcotti(Stramer实验室)于2020年编写。作者提供了联系方式,供有问题或需要进一步帮助的研究者进行联系。这显示了作者对于开源社区的开放态度以及对代码使用者的责任感。 总结而言,本资源提供了一个MATLAB环境下的蛋白质周转率建模工具集,通过详细的代码实现和说明,供研究人员在生物医学和系统生物学领域进行深入分析和研究。资源的开源属性和详细的文档指南,将有助于推广该模型的使用,并为蛋白质代谢的研究提供支持。

纠正代码:trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv') testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv') y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited'] X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited'] X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42) rfc.fit(X_train, y_train_forced_turnover_nolimited) y_pred = rfc.predict_proba(X_test) # 计算AUC值 auc = roc_auc_score(y_test_forced_turnover_nolimited, y_pred) # 输出AUC值 print('测试集AUC值为:', auc)

2023-06-03 上传

纠正这段代码:trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv') testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv') y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited'] X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited'] X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) model = Sequential() model.add(Dense(64, activation='relu', input_dim=X_train.shape[1])) model.add(Dropout(0.5)) model.add(Dense(32, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1) model_checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True, verbose=1) history = model.fit(X_train, epochs=50, batch_size=32, validation_data=(y_train_forced_turnover_nolimited), callbacks=[early_stopping, model_checkpoint]) model.load_weights('model.h5') pred = model.predict(X_test) auc = roc_auc_score(test.iloc[:, -1], pred) print('Testing AUC:', auc)

2023-05-31 上传

纠正模型:trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv') testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv') y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited'] X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited'] X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) # 构建模型 model = Sequential() model.add(Dense(64, input_dim=X_train.shape[1], activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam') # 训练模型 model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0) # 预测测试集 y_pred = model.predict(X_test) # 计算AUC auc = roc_auc_score(y_test, y_pred) print('AUC:', auc)

2023-06-01 上传