🏈 指点迷津 | Brief 🎯要点
🎯人体疾病模块网络结构位置与病理生物学关系 | 🎯药物与药物靶点相互作用 | 🎯细胞和蛋白质之间的作用分层 | 🎯疾病和症状之间的联系 | 🎯药物与副作用之间的联系 | 🎯生物学分析
📜图分析用例
📜Python鲁汶意外莱顿复杂图拓扑分解算法
🍪语言内容分比
Python药物副作用数学矩阵降维算法
预测药物效应在药物研发中非常重要，而药物研发是制药科学的主要目标。关于已批准药物的安全性、有效性和耐受性，有大量信息可用，这可以最大限度地减少预测新药效应所需的费用和时间。
药物数据准备
在本研究中，观察到的标签矩阵用Y ∈ R m × n Y \in R ^{m \times n} Y ∈ R m × n m m m n n n y i j ∈ { − 1 , 0 , + 1 } y_{i j} \in \{-1,0,+1\} y ij ∈ { − 1 , 0 , + 1 } U ∈ R m × k 和 V ∈ R n × k U \in R ^{m \times k} 和 V \in R ^{n \times k} U ∈ R m × k 和 V ∈ R n × k
arg min U , V ∥ R ∘ ( Y − U V T ) ∥ F 2 \underset{ U , V }{\arg \min }\left\| R ^{\circ}\left( Y - U V ^T\right)\right\|_F^2 U , V arg min R ∘ ( Y − U V T ) F 2 其中∥ ⋅ ∥ F \|\cdot\|_F ∥ ⋅ ∥ F ∘ { }^{\circ} ∘ R ∈ R m × n R \in R ^{m \times n} R ∈ R m × n y i j = 1 y_{i j}=1 y ij = 1 r i j = p w r_{i j}=p w r ij = pw y i j = − 1 y_{i j}=-1 y ij = − 1 r i j = n w r_{i j}=n w r ij = n w p w p_w p w n w n w n w
药物邻域信息采用邻接矩阵𝐀表示，其元素定义如下：
A i , μ = { S i , μ d if d μ ∈ N ( d i ) 0 否则 A _{i, \mu}=\left\{\begin{array}{cc} S_{i, \mu}^d & \text { if } d _\mu \in N\left(d_{ i }\right) \\ 0 & \text { 否则 } \end{array}\right. A i , μ = { S i , μ d 0 if d μ ∈ N ( d i ) 否则 其中 N ( d i ) N\left(d_{ i }\right) N ( d i ) K 1 K_1 K 1 d i d _{ i } d i
B j , v = { S j , v t if d v ∈ N ( t j ) 0 否则 B _{j, v}=\left\{\begin{array}{cc} S_{j, v}^t & \text { if } d _v \in N\left( t _{ j }\right) \\ 0 & \text { 否则 } \end{array}\right. B j , v = { S j , v t 0 if d v ∈ N ( t j ) 否则 最小化潜在空间中 d i d _{ i } d i N ( d i ) N\left(d_{ i }\right) N ( d i )
α 2 ∑ i = 1 m ∑ μ = 1 m A i , μ ∥ u i − u μ ∥ F 2 = α 2 tr ( U T L d U ) \frac{\alpha}{2} \sum_{i=1}^m \sum_{\mu=1}^m A _{i, \mu}\left\| u _i- u _\mu\right\|_F^2=\frac{\alpha}{2} \operatorname{tr}\left( U ^T L ^d U \right) 2 α i = 1 ∑ m μ = 1 ∑ m A i , μ ∥ u i − u μ ∥ F 2 = 2 α tr ( U T L d U ) Python伪码算法实践：
Copy import pandas as pd
import numpy as np
from collections import defaultdict
from tqdm import tqdm
import sys
from _utils import performance_compare as pc
Copy df = pd.read_csv(base_dir + '/Data/table.csv',index_col=0)
drugMat = pd.read_csv(base_dir + '/Data/sim.csv',index_col=0)
diseaseMat = pd.read_csv(base_dir + '/Data/sim.csv',index_col=0)
ind_bi = pd.read_csv(base_dir + '/Data/binary.csv',index_col=0)
se_bi = pd.read_csv(base_dir + '/Data/binary.csv',index_col=0)
use_ind = ind_bi[df.columns.tolist()].loc[df.index.tolist()]
use_se = se_bi[df.columns.tolist()].loc[df.index.tolist()]
drug2name = pd.read_pickle(base_dir + '/Data/name.pkl')
list(drug2name.keys())[0]
name2pt = pd.read_pickle(base_dir + '/Data/cui.pkl')
drugs = df.index.tolist()
diseases = df.columns.tolist()
ann_ind = [drug2name.get(k) for k in drugs]
ann_col = [pt2name.get(k) for k in diseases]
df.index = ann_ind
df.columns = ann_col
drugMat.index = ann_ind
drugMat.columns = ann_ind
diseaseMat.index = ann_col
diseaseMat.columns = ann_col
use_ind.index = ann_ind
use_ind.columns = ann_col
use_se.index = ann_ind
use_se.columns = ann_col
Copy cv_data = defaultdict(list)
idx = df.columns.tolist().index(r)
intMat = np.array(df.T)
num_drugs,num_disease = intMat.T.shape
test_data = np.array([[k,j] for k in [idx] for j in range(num_drugs)],dtype=np.int32)
x,y = test_data[:,0], test_data[:,1]
test_label = intMat[x,y]
W = np.ones(intMat.shape)
W[x,y] = 0
cv_data[ann_col[idx]].append((W,test_data,test_label))
for W, test_data, test_label in cv_data[r]:
model = nrbdmf.NRBdMF(K1=5,K2=5,num_factors=50,theta=1.0,lambda_d=0.625,lambda_t=0.625,alpha=0.1,beta=0.1, max_iter=5000,tolx=1e-5,positive_weight=0.1,negative_weight=0.1,missing_base=0,indicator=True,half_mask=False,verbose=False)
model.fix_model(W, np.array(df).T, diseaseMat, drugMat, seed=123)
posi_val,nega_val = model.ex_evaluation(test_data, test_label)
print(posi_val,nega_val)
# processing output
pred_res = model.pred_res
pred_res.index = df.index.tolist()
se_label = use_se[r].tolist() # side effect binary label (before merging)
pred_res['se_label'] = se_label
ind_label = use_ind[r].tolist()
pred_res['ind_label'] = ind_label
pred_res['name'] = pred_res.index.tolist()
pred_res = pred_res.sort_values('scores',ascending=False)
auroc, aupr = nrbdmf.calc_auc_aupr(y_true=pred_res['se_label'].tolist(), y_score=pred_res['scores'].tolist(),title=r)
Copy cv_data = defaultdict(list)
idx = df.index.tolist().index(r)
intMat = np.array(df)
num_drugs,num_disease = intMat.T.shape
test_data = np.array([[k,j] for k in [idx] for j in range(num_drugs)],dtype=np.int32)
x,y = test_data[:,0], test_data[:,1]
test_label = intMat[x,y]
W = np.ones(intMat.shape)
W[x,y] = 0
cv_data[ann_ind[idx]].append((W,test_data,test_label))
for W, test_data, test_label in cv_data[r]:
model = nrbdmf.NRBdMF(K1=5,K2=5,num_factors=50,theta=1.0,lambda_d=0.625,lambda_t=0.625,alpha=0.1,beta=0.1, max_iter=5000,tolx=1e-5,positive_weight=0.1,negative_weight=0.1,missing_base=0,indicator=True,half_mask=False,verbose=False)
model.fix_model(W, np.array(df), drugMat, diseaseMat, seed=123)
posi_val,nega_val = model.ex_evaluation(test_data, test_label)
print(posi_val,nega_val)
pred_res = model.pred_res pred_res.index = df.columns.tolist()
se_label = use_se.loc[r].tolist()
pred_res['se_label'] = se_label
ind_label = use_ind.loc[r].tolist()
pred_res['ind_label'] = ind_label
pred_res['name'] = pred_res.index.tolist()
pred_res = pred_res.sort_values('scores',ascending=False)
auroc, aupr = nrbdmf.calc_auc_aupr(y_true=pred_res['se_label'].tolist(), y_score=pred_res['scores'].tolist(),title=r) 