diff --git a/IoT/IoT talk/README.md b/IoT/IoT talk/README.md index 986802d..0ee1506 100644 --- a/IoT/IoT talk/README.md +++ b/IoT/IoT talk/README.md @@ -3,10 +3,14 @@ IoT talk : sensor,device and model ### What is IoT talk system ? The IoT talk system is create by Jason Yi-Bing Lin[1]. -It is a useful IoT connection system that can easily been used. +It is a useful IoT connection system that can used easily. Just by using the website GUI and some python code, we can built a system that contains sensing,visualization, prediction and spraying the water due to -prediction result.And the codes we used are shown above. +prediction result.And the codes we used are shown above.
+#### Note!!! +##### The IoT talk system from Yi-Bing Lin's lab is not release yet. +##### So it doesn`t allow other peoples to use. +##### To see our result , you can watch the video in our wiki [IoT demo](http://2017.igem.org/Team:NCTU_Formosa/Demonstrate) Sensor&spraying -------- @@ -14,7 +18,7 @@ The `iot_sensor.ino` is a part of our IoT talk system that can sensing the weath such as temperature,humidity,pressure and rainfall. After that,the device will send these data to the IoT talk server. -These libraries are requested of running this code. +These libraries are requested to running this code. ``` diff --git a/IoT/README.md b/IoT/README.md index 88b5448..8b4c2f4 100644 --- a/IoT/README.md +++ b/IoT/README.md @@ -2,13 +2,14 @@ IoT software ======== This is a collection of IoT prediction model and sensor code -It contain three files , they are data,disease_model and IoT talk. +It contains three files: data, disease_model and IoTtalk. -------- The description of files is as follows. 1 disease_model : the disease model software for training an Artificial neural network -2 data : the collection of row disease data and weather data we used +2 data : the collection of raw disease data , weather data and training +and testing data after processing. 3 IoT talk : the collection of our IoT talk system code. diff --git a/IoT/disease_model/README.md b/IoT/disease_model/README.md index 605c7f8..e21ec9a 100644 --- a/IoT/disease_model/README.md +++ b/IoT/disease_model/README.md @@ -1,11 +1,11 @@ -Disease Predictin Model +Disease Prediction Model ======== -This is a the CNN base model that can use for disease prediction +This is the CNN based model that can be used for disease prediction How to use ? -------- -The following requirement are needed for using this model. +The following requirements are needed for using this model. `python >= 3.6` with packages : `optparse`,`numpy`,`tensorflow`>=1.2,`tensorlayer` @@ -20,7 +20,7 @@ Training model -------- Run `CNN.py` to starting training -You need to input the training file , testing file ,and savename to run the code +You need to input the `training file` , `testing file` ,and `savename` to run the code Example : ``` @@ -40,4 +40,5 @@ they are some options to modify the network hyper parameters `-g [--graph]` using tensorboard or not -After training , model output a `.npz` format file ,it can be easy analyze by `tl.files.load_npz` function \ No newline at end of file +After training , model output a `.npz` format file ,it can be analyzed easily +by the function of `tl.files.load_npz`. \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..9cecc1d --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + {one line to give the program's name and a brief idea of what it does.} + Copyright (C) {year} {name of author} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 2e312d8..f220f95 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,23 @@ NCTU_Formosa2017 --------- -This is a software code about NCTU_Formosa2017.This year ,we built a predict model of +This is a software code programmed by NCTU_Formosa, 2017.This year ,we build a predict model of peptide function. We introduce our model to the antifungal peptide prediction and get -a good performance for antifungal peptide prediction. At the end ,we also conduct wet lab +a good performance for antifungal peptide prediction. In the end ,we also conduct wet lab experiment to validate our model.Finally we proof that our model works well and discover -some new antifungal peptide that never been found before.Besides , we also built an antifungal - database that link host , pathogens and antifungal peptide all together, provide user who aims to -solve plant disease to search all available information at once. +some new antifungal peptides that have never been found before.Besides , we also built an antifungal + database that links up the relationship of hosts, pathogens and antifungal peptides all together, provide user who aims to +solve plant diseases and to search all available information at once. Our database website is available at [Parabase](http://web.it.nctu.edu.tw/~nctu_formosa/Parabase/) -#### To learn more about our project , you can check our igem wiki. +#### To learn more about our project , you can check our iGEM wiki. [NCTU_Formosa2017_wiki](http://2017.igem.org/Team:NCTU_Formosa/Description) -#It contain three files , they are predict model,database,and IoT. The description of files is as follows. +#It contains three files , they are predict model,database,and IoT. The description of files is as follows. 1 predict model : the SCM code and the visualize code. - 2 database : the code we use for database website. + 2 database : the code we used for database website. 3 IoT : the code we use for the prediction model and IoT talk system. diff --git a/predict model/README.md b/predict model/README.md index 4909039..8ff8fb4 100644 --- a/predict model/README.md +++ b/predict model/README.md @@ -1,6 +1,6 @@ Prediction software ======== -This is a code about our antifungal prediction model. +These are codes about our antifungal prediction model. ## It contains two parts , SCM model and the SCM_color. diff --git a/predict model/SCM/README.md b/predict model/SCM/README.md index d19c005..b89cfa7 100644 --- a/predict model/SCM/README.md +++ b/predict model/SCM/README.md @@ -4,15 +4,16 @@ The following requirements are needed for using the scoring card predicting mode `python>=3.6` with packages :`optparse`,`math`,`multiprocessing` -You can use `pip` to install these packages +These packages are all in python standard library. +If you do not have these packages you can use `pip` to install these packages. ``` pip install optparse pip install math pip install multiprocessing ``` ->Scoring Card Data Format ->---------- +Scoring Card Data Format +---------- ACCCTTTTYYYYMMMMMACAC 1 @@ -30,7 +31,7 @@ ACCAAMMMMTTTRRSSSSSSSS 0 Optimization --------- -Use `optimize_max.py` to make and optimize the scoring card +Use `optimize_max.py` to build up and optimize the scoring card model. ``` python optimize_max.py -f [datafile] ``` @@ -85,3 +86,5 @@ Test_acc = (The testing accuracy with theshold) Sensitivity = (The testing sensitivity with theshold) Specitivity = (The testing specificity with theshold) ``` + +This code is from [Shinn-Ying Ho's Lab website](http://iclab.life.nctu.edu.tw/iclab_webtools/SCMBYK/download.php) with some modification. \ No newline at end of file diff --git a/predict model/SCM/optimize.py b/predict model/SCM/optimize.py new file mode 100644 index 0000000..139bad6 --- /dev/null +++ b/predict model/SCM/optimize.py @@ -0,0 +1,385 @@ + +import genetic +import random +import math +from optparse import OptionParser #markliou +from multiprocessing import Pool +#============== Initial Population ====================# +# usr option method markliou +######size=60###### +cmdparser = OptionParser() +cmdparser.add_option("-f", "--file", action="store", dest="filename", default='example/istr', help="input a training data filename") +cmdparser.add_option("-g", "--gap", action="store", dest="gap", default=0, help="gap number, default is 0") +cmdparser.add_option("-v", "--cvfold", action="store", dest="nfold", default=10, help="cross validation fold, default is 10") +cmdparser.add_option("-G", "--Generation", action="store", dest="ngen", default=20, help="number of Generation, default is 20") +cmdparser.add_option("-P", "--Population", action="store", dest="population", default=60, help="number of Population, default is 40") +cmdparser.add_option("-V", "--Weight1", action="store", dest="w1", default=0.9, help="Weight 1 AUC, default is 0.9") +cmdparser.add_option("-W", "--Weight2", action="store", dest="w2", default=0.1, help="Weight 2 Correlation, default is 0.1") +cmdparser.add_option("-s", "--scorecard", action="store", dest="scorecard", default='0', help="input scorecard name") +cmdparser.add_option("-p", "--pools", action="store", dest="pools", default=10, help="number of pools") +(options, args) = cmdparser.parse_args() +if(options.filename == 0): + print (" the input filename is not specified!! Please use -f or --file option...") + exit(" For more information, use -h or --help") +trainname = options.filename +train_file = open(trainname) +gap = int(options.gap) +nfold = int(options.nfold) +ngen = int(options.ngen) +W1 = float(options.w1) +W2 = float(options.w2) +scorecard = options.scorecard +Population = int(options.population) +pools = int(options.pools) + +#set score max and min +boundMAX = 1000 +boundMIN = 0 +#set some const value +aalist= ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'] +dipep = [x1+x2 for x1 in aalist for x2 in aalist] +samplelist= [n.strip('\n').split('\t') for n in train_file.readlines()] +#read training sample +train_file.close() + +###small function we used +#all key to zero +def ini_dic(dic=None,key_list=None): + if key_list: + return {k:0 for k in key_list } + elif dic: + return {k:0 for k in dic.keys() } + else: + return None +#count the aa score +def count_aa_score(scorecard): + avg_aa_score = [0]*20 + k=0; + for i in range(400): + if (k == 20): + k=0 + avg_aa_score[k] += float(scorecard[i]) + avg_aa_score[int(i/20)] += float(scorecard[i]) + k = k+1 + for i in range(20): + avg_aa_score[i] = avg_aa_score[i]/40 + return avg_aa_score +#read and make a scoring card +def make_sc(scorecard): + f3 = open(scorecard) + count = 0 + tmpscore = [] + for line in f3.readlines(): + if count==3: + tmpscore1 = line.split('[') + tmpscore2 = tmpscore1[1].strip(']'),split(',') + for tmpval in tmpscore2: + tmpscore.append(float(tmpval)) + count = count + 1 + f3.close() + return tmpscore +#calculate average +def average(x): + assert len(x) > 0 + return float(sum(x)) / len(x) +#calculate Pearson coefficient +def pearson_def(x, y): + assert len(x) == len(y) + n = len(x) + assert n > 0 + avg_x = average(x) + avg_y = average(y) + diffprod = 0 + xdiff2 = 0 + ydiff2 = 0 + for idx in range(n): + xdiff = x[idx] - avg_x + ydiff = y[idx] - avg_y + diffprod += xdiff * ydiff + xdiff2 += xdiff * xdiff + ydiff2 += ydiff * ydiff + return diffprod / math.sqrt(xdiff2 * ydiff2) + +#initialize dipeptide frequence dic +dipep_0= ini_dic(key_list=dipep) +dipep_1= ini_dic(key_list=dipep) + +# counting the positive dipeptide and negative dipeptide +pos_count,neg_count = 0,0 +for line in samplelist: + seq= line[0] + clas = int(line[1]) + #divide positive data and negative data + for aa in range(0, len(seq)-(gap+1)): + tempaa=seq[aa]+seq[aa+(gap+1)] + if clas == 0: + dipep_0[tempaa] += 1 + neg_count += 1 + elif clas == 1: + dipep_1[tempaa] += 1 + pos_count += 1 + +scoring = [] +for x5 in dipep: + # times 1000 to make score bigger + eachscore = float(((float(dipep_1[x5])/pos_count)-(float(dipep_0[x5])/neg_count))*1000) + scoring.append(eachscore) +#nomolize them +nol_scoring= [] +for i in scoring: + n= (i-min(scoring))/(max(scoring)-min(scoring))*1000 + nol_scoring.append(round(n)) + +#ini_scorecard is the first scoring card +ini_scorecard = nol_scoring +avg_aa_score = count_aa_score(ini_scorecard) + +def p_evaluate(chromosome): + + sco= [] + x= 0.0 + ts_file= [] + index = 1 + indexl = [] + #use index the divide k-fold data,indexl store all index + for line in samplelist: + dipep_test= ini_dic(key_list=dipep) + seq= line[0] + clas = line[1] + #count dipeptide frequence + for aa in range(0, len(seq)-(gap+1)): + tempaa= seq[aa:aa+(gap+2)] + dipep_test[tempaa] += 1 + #caculate score of each dipeptide + gg=0.0 + j= 0 + for x6 in dipep: + gg+= float((chromosome[j])*dipep_test[x6]) + j+=1 + #append [score,clas] and index + ts_file.append([gg/(len(seq)-(gap+1)),clas]) + indexl.append(index % nfold) + index = index + 1 + + auc_score = 0 + kfoldacc = 0 + for i in range(nfold): + test = [] + train = [] + # make test set and train set + for count in range(len(ts_file)): + if indexl[count] == i: + test.append(ts_file[count]) + else: + train.append(ts_file[count]) + + # Train to find Best Score , train is a list of score [[gg/(len(seq)-(gap+1)),real]......] + for sample in train: + sco.append(sample[0]) + TPRl=[] + FPRl=[] + x= min(sco) #x is the min + maxacc = -1 + + #sco is a list of score [predict,predict,predict] sorted,from small to large + while x<= max(sco): + thre= x + #initialize true positive,false positive,true negative,false negative + TP,FP,TN,FN,TPR,FPR=[0.0]*6 + + for everysample in test: + ss= everysample[0] #predict score + clas= everysample[1] #real clas + assert type(clas) == str + if ss> thre: + clas2= "1" + else: + clas2= "0" + if clas== "1" and clas2== "1": + TP+= 1 + elif clas== "1" and clas2== "0": + FN+= 1 + elif clas== "0" and clas2== "1": + FP+= 1 + elif clas== "0" and clas2== "0": + TN+= 1 + TPR= TP/(TP+FN) + FPR= FP/(FP+TN) + TPRl.append(TPR) + FPRl.append(FPR) + acc = (TP+TN)/(TP+FP+TN+FN) + if (acc > maxacc): + maxacc = acc + x= x+1 + + #calculate auc + ARC,area,high,bottom=[0.0]*4 + + for pp in range(0, len(TPRl)-2): + high= float(FPRl[pp])-float(FPRl[pp+1]) + bottom= float(TPRl[pp])+float(TPRl[pp+1]) + area= float(bottom*high)/2.0 + ARC+= area + auc_score += ARC + kfoldacc += maxacc + auc_score = (auc_score/nfold) #average + kfoldacc = (kfoldacc/nfold) #average + #caculate aa score for each aa + cur_aa_score = count_aa_score(chromosome) + corr_score = pearson_def(avg_aa_score, cur_aa_score) + score = (W1*auc_score + W2*corr_score)*100 + #conpute score w1=0.9 w2=0.1 ，bigger better + return kfoldacc,score + +class Environment(genetic.Environment): + #evaluate all chromosome + def evaluate_pop(self): + chrom_list=[n.chromosome for n in self.population] + acc_score = pool.map(p_evaluate,chrom_list) + num=0 + print('===== acc_score =====') + print(acc_score) + for ind in self.population: + ind.kfoldacc = acc_score[num][0] + ind.score = acc_score[num][1] + num += 1 + +class OneMax(genetic.Individual): + boundMAX = 1000 + boundMIN = 0 + length = 400 + #optimization = genetic.MINIMIZE + def evaluate(self, optimum=None): + self.kfoldacc,self.score = p_evaluate(self.chromosome) + def crossover(self, other): + #chrom_list contain chromosome in this crossover + chromosomesList = []; + chromosomesList.append(self) + chromosomesList.append(other) + papa = self.chromosome[:] + mama = other.chromosome[:] + #copy the class + child1 = self.__class__(papa) + child2 = self.__class__(papa) + ###Find Different + indif = [Temp for Temp in range(1,self.length) if papa[Temp] != mama[Temp]] + ndif = len(indif) + # if all same quit function + if ndif == 0: + return self, other + ###OA + oaarray= [] + level=2 + factor= ndif + temp= level-1 + #number of exp setting + J=1 + while (((level**J)-1)/temp)< factor: + J+=1 + exp= int(level**J) + #initialize the array ; oaarray factor*exp + oaarray= [[0 for j in range(factor)] for i in range(exp)] + #IGA : making OA array + remaind= exp + pow_level_k= 1 + for k in range(0, J): + j= int((pow_level_k-1)/temp ) + remaind/= level + for i in range(0, exp): + oaarray[i][j]= (int(i/int(remaind)))%level + pow_level_k*= level + pow_level_k= level + for k in range(1, J): + j= int((pow_level_k-1)/temp ) + for s in range(0, j): + for t in range(1, temp+1): + if (t+j+s*temp) 0.0: + child1.chromosome[indif[pp]]= mama[indif[pp]] + child2.chromosome[indif[pp]]= mama[indif[pp]] + else: + child1.chromosome[indif[pp]]= papa[indif[pp]] + child2.chromosome[indif[pp]]= mama[indif[pp]] + if abs_MED_list[pp]== min(abs_MED_list): + if child2.chromosome[indif[pp]]== papa[indif[pp]]: + child2.chromosome[indif[pp]]= mama[indif[pp]] + elif child2.chromosome[indif[pp]]== mama[indif[pp]]: + child2.chromosome[indif[pp]]= papa[indif[pp]] + + child1.evaluate() + child2.evaluate() + chromosomesList.append(child1) + chromosomesList.append(child2) + #sorting the chromosome + chromosomesList.sort(key=lambda x :(x.score) ,reverse=True) + new_child1,new_child2=chromosomesList[:2] + return new_child1, new_child2 + #change the mutate method + def mutate(self, gene): + self.chromosome[gene] = random.randint(self.boundMIN,self.boundMAX) + +if __name__ == "__main__": + pool=Pool(pools) + t = 1 + mypop= [] + mypop.append(OneMax(ini_scorecard)) + if scorecard != '0': + tmpscore=make_sc(scorecard) + mypop.append(OneMax(tmpscore)) + t += 1 + + for i in range(Population-t): + #chromosome 是0到1000的數所組成的 + mypop.append(OneMax([round(random.randint(0, 1000)) for gene in range(400)])) + + env = Environment(OneMax, maxgenerations=ngen,size=Population,optimum=None, population= mypop) + env.run() \ No newline at end of file diff --git a/predict model/SCM/optimize_max.py b/predict model/SCM/optimize_max.py deleted file mode 100644 index cf5391b..0000000 --- a/predict model/SCM/optimize_max.py +++ /dev/null @@ -1,667 +0,0 @@ - -import genetic -import random -import math -from optparse import OptionParser #markliou -from multiprocessing import Pool -#============== Initial Population ====================# -# usr option method markliou -######size=60###### -cmdparser = OptionParser() -cmdparser.add_option("-f", "--file", action="store", dest="filename", default='example/istr', help="input a training data filename") -cmdparser.add_option("-g", "--gap", action="store", dest="gap", default=0, help="gap number, default is 0") -cmdparser.add_option("-v", "--cvfold", action="store", dest="nfold", default=10, help="cross validation fold, default is 10") -cmdparser.add_option("-G", "--Generation", action="store", dest="ngen", default=20, help="number of Generation, default is 20") -cmdparser.add_option("-P", "--Population", action="store", dest="population", default=60, help="number of Population, default is 40") -cmdparser.add_option("-V", "--Weight1", action="store", dest="w1", default=0.9, help="Weight 1 AUC, default is 0.9") -cmdparser.add_option("-W", "--Weight2", action="store", dest="w2", default=0.1, help="Weight 2 Correlation, default is 0.1") -cmdparser.add_option("-s", "--scorecard", action="store", dest="scorecard", default='0', help="input scorecard name") -cmdparser.add_option("-p", "--pools", action="store", dest="pools", default=10, help="number of pools") -(options, args) = cmdparser.parse_args() -if(options.filename == 0): - print (" the input filename is not specified!! Please use -f or --file option...") - exit(" For more information, use -h or --help") -filename1 = options.filename -v1= open(filename1) -gap = int(options.gap) -nfold = int(options.nfold) -ngen = int(options.ngen) -W1 = float(options.w1) -W2 = float(options.w2) -scorecard = options.scorecard -Population = int(options.population) -pools = int(options.pools) - -gen_score=[] -samplelist= v1.readlines() -v1.close() -f = {}; -##### - -aalist= ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y'] -dipep_0= {} -dipep_1= {} -for x1 in aalist: - for x2 in aalist: - dipep_0[x1+x2]= 0 - dipep_1[x1+x2]= 0 - -sodipep_0= sorted(dipep_0.keys()) -sodipep_1= sorted(dipep_1.keys()) - -for tmp in range(1): - k = 0 - samplelistsub = [] - - for x1 in aalist: - for x2 in aalist: - dipep_0[x1+x2]= 0 - dipep_1[x1+x2]= 0 - - - for line in samplelist: - if (tmp==k%1): - samplelistsub.append(line) - k = k+1 - - for line in samplelistsub: - tmpp = line.split('\t') - seq= tmpp[0] - tmpp = tmpp[1].split('\n') - tmpp = tmpp[0] - clas = tmpp[0:1] - #print seq - if int(clas)==0: - for aa in range(0, len(seq)-(gap+1)): - tempaa=seq[aa]+seq[aa+(gap+1)] - dipep_0[tempaa]= int(dipep_0[tempaa])+1 - #print tempaa - - elif int(clas)==1: - for aa in range(0, len(seq)-(gap+1)): - tempaa=seq[aa]+seq[aa+(gap+1)] - dipep_1[tempaa]= int(dipep_1[tempaa])+1 - y=0 - for x3 in sodipep_0: - y= y+dipep_0[x3] - - - z=0 - for x4 in sodipep_1: - z= z+dipep_1[x4] - - scoring = [] - so_scoring= [] - for x5 in sodipep_0: - eachscore = float(((float(dipep_0[x5])/y)-(float(dipep_1[x5])/z))*1000) - scoring.append(eachscore) - so_scoring.append(eachscore) - - so_scoring.sort() - new_scoring= [] - for i in scoring: - n= (i-so_scoring[0])/(so_scoring[-1]-so_scoring[0])*1000 - new_scoring.append(round(n)) - - #f[0]=new_scoring - f[tmp] = new_scoring - - avg_aa_score = [0 for i in range(20)] - k=0; - for i in range(400): - if (k == 20): - k=0 - avg_aa_score[k] = avg_aa_score[k] + f[0][i] - avg_aa_score[int(i/20)] = avg_aa_score[int(i/20)] + f[0][i] - k = k+1 - - for i in range(20): - avg_aa_score[i] = avg_aa_score[i]/40 - - ori_dipep_score = f[0] - #print (f[0]) - #print (avg_aa_score) - -#========== k-Fold Cross Validation ==================== -v1= open(filename1) -samplelist= [n.strip('\n').split('\t') for n in v1.readlines()] -v1.close() - -###batch=samplelist -#read and make a scoring card -def make_sc(filename2): - f3 = open(filename2) - count = 0 - tmpscore = [] - for line in f3.readlines(): - if count==3: - tmpscore1 = line.split('[') - tmpscore2 = tmpscore1[1].split(',') - for tmpval in tmpscore2: - t = tmpval.split(']'); - tmpscore.append(float(t[0])) - count = count + 1 - - scoring= {} - count = 0 - f2= open("dipep") - - for line in f2: - scoring[line[0:2]]= tmpscore[count]; - count = count+1 - f2.close() - sosco_key= [n for n in scoring.keys()] - sosco_key.sort() - - return sosco_key,scoring,tmpscore - -#========== cac_score ======================= (input seq output score) -def cuc_sc(seq,sosco_key,scoring): - gap=0 - dipep_test= {} - for x1 in sosco_key: - dipep_test[x1]= 0 - - for aa in range(0, len(seq)-(gap+1)): - tempaa= seq[aa]+seq[aa+(gap+1)] - dipep_test[tempaa]= int(dipep_test[tempaa])+1 - # caculate score - gg=0.0 - for x6 in sosco_key: - gg+= float(scoring[x6]*dipep_test[x6]) - - score = float(gg/(len(seq)-(gap+1))) - - return score - -#=========== construct 400 names of dipeptide ========== -sosco_key= sorted(dipep_0.keys()) - - -boundMAX = 1000 -boundMIN = 0 - - -def average(x): - assert len(x) > 0 - return float(sum(x)) / len(x) -#regularation -def pearson_def(x, y): - assert len(x) == len(y) - n = len(x) - assert n > 0 - avg_x = average(x) - avg_y = average(y) - diffprod = 0 - xdiff2 = 0 - ydiff2 = 0 - for idx in range(n): - xdiff = x[idx] - avg_x - ydiff = y[idx] - avg_y - diffprod += xdiff * ydiff - xdiff2 += xdiff * xdiff - ydiff2 += ydiff * ydiff - - return diffprod / math.sqrt(xdiff2 * ydiff2) - - -def p_evaluate(chromosome): - sco= [] - x= 0.0 - dipep_test= {} - for x1 in sosco_key: - dipep_test[x1]= 0 - - - ts_scor_clas= [] - ts_file= [] - - - index = 1 - indexl = [] - - #samplelist have all sample - - for line in samplelist: - - - seq= line[0] - clas = line[1] - - for fea in dipep_test: - dipep_test[fea]= 0 - - #making dipeptide score card - for aa in range(0, len(seq)-(gap+1)): - tempaa= seq[aa:aa+(gap+2)] - dipep_test[tempaa]= int(dipep_test[tempaa])+1 - gg=0.0 - j= 0 - for x6 in sosco_key: - gg+= float((chromosome[j])*dipep_test[x6]) - j+=1 - ts_scor_clas.append(gg/(len(seq)-(gap+1))) - ts_scor_clas.append(clas) - - # ts_scor_clas=[gg/(len(seq)-(gap+1)),clas] - ts_file.append(ts_scor_clas) - ts_scor_clas= [] - indexl.append(index % nfold) - index = index + 1 - - - auc_score = 0 - kfoldacc = 0 - - #nfold=10 - for i in range(nfold): - test = [] - train = [] - count = 0 - - # make 1 test group,and 9 train group - # ts_file is a list of score [[gg/(len(seq)-(gap+1)),real],......] - for selectedIndex in ts_file: - if indexl[count] == i: - test.append(ts_file[count]) - else: - train.append(ts_file[count]) - count = count + 1 - - # Train to find Best Score , train is a list of score [[gg/(len(seq)-(gap+1)),real],[gg/(len(seq)-(gap+1)),real]......] - for everysample in train: - sco.append(everysample[0]) - sco.sort() - - - TPRl=[] - FPRl=[] - x= sco[0] #x is the min - maxacc = -1 - - #sco is a list of score [predict,predict,predict] sorted,from small to large - while x<= sco[-1]: - thre= x - TP=0.0 #true positive - FP=0.0 - TN=0.0 - FN=0.0 - TPR= 0.0 - FPR= 0.0 - for everysample in test: - ss= everysample[0] #predict score - clas= everysample[1] #real clas - if ss> thre: - clas2= "1" - else: - clas2= "0" - - if clas== "1" and clas2== "1": - TP+= 1 - elif clas== "1" and clas2== "0": - FN+= 1 - elif clas== "0" and clas2== "1": - FP+= 1 - elif clas== "0" and clas2== "0": - TN+= 1 - TPR= TP/(TP+FN) - FPR= FP/(FP+TN) - TPRl.append(TPR) - FPRl.append(FPR) - acc = (TP+TN)/(TP+FP+TN+FN) - if (acc > maxacc): - maxacc = acc - x= x+1 - - ARC=0.0 - area=0.0 - high=0.0 - bottom=0.0 - ###something wrong - for pp in range(0, len(TPRl)-2): - high= float(FPRl[pp])-float(FPRl[pp+1]) - bottom= float(TPRl[pp])+float(TPRl[pp+1]) - area= float(bottom*high)/2.0 - ARC+= area - #print(ARC) - auc_score = auc_score+ARC - kfoldacc = kfoldacc + maxacc - - auc_score = (auc_score/nfold) #average - #print('auc_score : '+str(auc_score)) - kfoldacc = (kfoldacc/nfold) #average - - #caculate aa score for each aa - cur_aa_score = [0 for i in range(20)] - k=0 - for i in range(400): - if (k == 20): - k=0 - cur_aa_score[k] = cur_aa_score[k] + chromosome[i] #後項 - cur_aa_score[int(i/20)] = cur_aa_score[int(i/20)] + chromosome[i] #前項 - k = k+1 - for i in range(20): - cur_aa_score[i] = cur_aa_score[i]/40 - - corr_score = pearson_def(avg_aa_score, cur_aa_score) - #corr_dipep_score = 1-pearson_def(ori_dipep_score,self.chromosome) - score = (W1*auc_score + W2*corr_score)*100 - #conpute score w1=0.9 w2=0.1 ，smaller better - return kfoldacc,score - - -class Environment(genetic.Environment): - def evaluate_pop(self): - chrom_list=[n.chromosome for n in self.population] - acc_score = pool.map(p_evaluate,chrom_list) - - num=0 - - print('===== acc_score =====') - print(acc_score) - for ind in self.population: - ind.kfoldacc = acc_score[num][0] - ind.score = acc_score[num][1] - num += 1 - - -class OneMax(genetic.Individual): - boundMAX = 1000 - boundMIN = 0 - length = 400 - #optimization = genetic.MINIMIZE - - def evaluate(self, optimum=None): - sco= [] - x= 0.0 - dipep_test= {} - for x1 in sosco_key: - dipep_test[x1]= 0 - - - ts_scor_clas= [] - ts_file= [] - - - index = 1 - indexl = [] - - #samplelist have all sample - - for line in samplelist: - - - seq= line[0] - clas = line[1] - - for fea in dipep_test: - dipep_test[fea]= 0 - - #making dipeptide score card - for aa in range(0, len(seq)-(gap+1)): - tempaa= seq[aa:aa+(gap+2)] - dipep_test[tempaa]= int(dipep_test[tempaa])+1 - gg=0.0 - j= 0 - for x6 in sosco_key: - gg+= float((self.chromosome[j])*dipep_test[x6]) - j+=1 - ts_scor_clas.append(gg/(len(seq)-(gap+1))) - ts_scor_clas.append(clas) - - # ts_scor_clas=[gg/(len(seq)-(gap+1)),clas] - ts_file.append(ts_scor_clas) - ts_scor_clas= [] - indexl.append(index % nfold) - index = index + 1 - - - auc_score = 0 - kfoldacc = 0 - - #nfold=10 - for i in range(nfold): - test = [] - train = [] - count = 0 - - # make 1 test group,and 9 train group - # ts_file is a list of score [[gg/(len(seq)-(gap+1)),real],......] - for selectedIndex in ts_file: - if indexl[count] == i: - test.append(ts_file[count]) - else: - train.append(ts_file[count]) - count = count + 1 - - # Train to find Best Score , train is a list of score [[gg/(len(seq)-(gap+1)),real],[gg/(len(seq)-(gap+1)),real]......] - for everysample in train: - sco.append(everysample[0]) - sco.sort() - - - TPRl=[] - FPRl=[] - x= sco[0] #x is the min - maxacc = -1 - - #sco is a list of score [predict,predict,predict] sorted,from small to large - while x<= sco[-1]: - thre= x - TP=0.0 #true positive - FP=0.0 - TN=0.0 - FN=0.0 - TPR= 0.0 - FPR= 0.0 - for everysample in test: - ss= everysample[0] #predict score - clas= everysample[1] #real score - if ss> thre: - clas2= "1" - else: - clas2= "0" - - if clas== "1" and clas2== "1": - TP+= 1 - elif clas== "1" and clas2== "0": - FN+= 1 - elif clas== "0" and clas2== "1": - FP+= 1 - elif clas== "0" and clas2== "0": - TN+= 1 - TPR= TP/(TP+FN) - FPR= FP/(FP+TN) - TPRl.append(TPR) - FPRl.append(FPR) - acc = (TP+TN)/(TP+FP+TN+FN) - if (acc > maxacc): - maxacc = acc - x= x+1 - - ARC=0.0 - area=0.0 - high=0.0 - bottom=0.0 - for pp in range(0, len(TPRl)-2): - high= float(FPRl[pp])-float(FPRl[pp+1]) - bottom= float(TPRl[pp])+float(TPRl[pp+1]) - area= float(bottom*high)/2.0 - ARC+= area - auc_score = auc_score+ARC - kfoldacc = kfoldacc + maxacc - - auc_score = (auc_score/nfold) #average - kfoldacc = (kfoldacc/nfold) #average - #caculate aa score for each aa - cur_aa_score = [0 for i in range(20)] - k=0 - for i in range(400): - if (k == 20): - k=0 - cur_aa_score[k] = cur_aa_score[k] + self.chromosome[i] #後項 - cur_aa_score[int(i/20)] = cur_aa_score[int(i/20)] + self.chromosome[i] #前項 - k = k+1 - for i in range(20): - cur_aa_score[i] = cur_aa_score[i]/40 - - corr_score = pearson_def(avg_aa_score, cur_aa_score) - #corr_dipep_score = 1-pearson_def(ori_dipep_score,self.chromosome) - self.kfoldacc = kfoldacc - self.score = (W1*auc_score + W2*corr_score)*100 #conpute score w1=0.9 w2=0.1 ，smaller better - - def crossover(self, other): - - - - chromosomesList = []; - chromosomesList.append(self) - chromosomesList.append(other) - - papa = self.chromosome[:] - mama = other.chromosome[:] - - #??? - child1 = self.__class__(papa) - child2 = self.__class__(papa) - - #---------- Find Different ------------------------- - indif = [Temp for Temp in range(1,self.length) if papa[Temp] != mama[Temp]] - ndif = len(indif) - if ndif == 0: - return self, other - - #---------- OA ------------------------------------- - - oaarray= [] - level=2 - factor= ndif - temp= level-1 - - #number of exp setting - J=1 - while (((level**J)-1)/temp)< factor: - J+=1 - - exp= int(level**J) - - #initialize the array ; oaarray factor*exp - oaarray= [[0 for j in range(factor)] for i in range(exp)] - - #******not understand*****# - remaind= exp - pow_level_k= 1 - for k in range(0, J): - j= int((pow_level_k-1)/temp ) - remaind/= level - for i in range(0, exp): - oaarray[i][j]= (int(i/int(remaind)))%level - pow_level_k*= level - - pow_level_k= level - for k in range(1, J): - j= int((pow_level_k-1)/temp ) - for s in range(0, j): - for t in range(1, temp+1): - if (t+j+s*temp) 0.0: - child1.chromosome[indif[pp]]= mama[indif[pp]] - child2.chromosome[indif[pp]]= mama[indif[pp]] - else: - child1.chromosome[indif[pp]]= papa[indif[pp]] - child2.chromosome[indif[pp]]= mama[indif[pp]] - - if abs_MED_list[pp]== min(abs_MED_list): - if child2.chromosome[indif[pp]]== papa[indif[pp]]: - child2.chromosome[indif[pp]]= mama[indif[pp]] - elif child2.chromosome[indif[pp]]== mama[indif[pp]]: - child2.chromosome[indif[pp]]= papa[indif[pp]] - - child1.evaluate() - child2.evaluate() - chromosomesList.append(child1) - chromosomesList.append(child2) - - chromosomesList.sort(key=lambda x :(x.score) ,reverse=True) - new_child1,new_child2=chromosomesList[:2] - - return new_child1, new_child2 - - def mutate(self, gene): - - self.chromosome[gene] = random.randint(self.boundMIN,self.boundMAX) - - -if __name__ == "__main__": - pool=Pool(pools) - t = 1 - mypop= [] - mypop.append(OneMax(f[0])) - if scorecard != '0': - sosco_key,scoring,tmpscore=make_sc(scorecard) - mypop.append(OneMax(tmpscore)) - t += 1 - - for i in range(Population-t): - #chromosome 是0到1000的數所組成的 - mypop.append(OneMax([round(random.randint(0, 1000)) for gene in range(400)])) - - env = Environment(OneMax, maxgenerations=ngen,size=Population,optimum=None, population= mypop) - env.run() \ No newline at end of file diff --git a/predict model/SCM_color/README.md b/predict model/SCM_color/README.md index ccb4b3d..e1ac104 100644 --- a/predict model/SCM_color/README.md +++ b/predict model/SCM_color/README.md @@ -1,10 +1,8 @@ -####################### +SCM_visualize +-------- +To use the peptide visualization software,the following requirements are needed. -This code is from "" with some modification - -To use the peptide visualize software,the following requirements are needed. - -`pymol` can ce download in +`pymol` can be downloaded in `http://www.pymol.org/` `python==3.6` with packages :`optparse`,`numpy` @@ -13,27 +11,39 @@ You can use `pip` to install these packages pip install optparse pip install numpy ``` ->Start to use the software ->========= ->### First , you needed to make the propensity score file for the perl script. ->Run the `make_PS.py` to make the file -> ->``` ->python make_PS.py -f [scorecard] -t [type:PS or DPS] -n [normalize] -o [outputname] ->``` ->For example : ->``` ->python make_PS -f example_scorecard -t DPS -n -o DPS.tab ->``` -> ->### Second , using the perl to draw the peptide. -> ->``` ->perl SCM_color_v3.pl ->``` ->Then,key in the PDB , propensity score , smooth parameter and the output file(.pml). -> ->### Third , open the PDB file with pymol, then click file>run and choose the .pml file. -> ->### Finally , you can see the visualize scorecard result of any peptide. +Start to use the software +========= +### First , you needed to make the propensity score file for the `perl` script. +Run the `make_PS.py` to make the file + +``` +python make_PS.py -f [scorecard] -t [type:PS or DPS] -n [normalize] -o [outputname] +``` + +PS means to caculate the peptide propensity , DPS means to caculate the dipeptide propensity. + +For example : +``` +python make_PS -f example_scorecard -t DPS -n -o DPS.tab +``` + +### Second , using the perl to draw the peptide. + +``` +perl SCM_color_v3.pl +``` +Then,key in the `.PDB` , propensity score , smooth parameter and the output file(.pml). + +### Third , open the PDB file with pymol, then click file>run and choose the .pml file. + +### Finally , you can see the visualize scorecard result of any peptide. + +#### #Auto Perl script +If you dont don't want to key in every pdb file by our hand. +You can use `auto_perl.py` to automatically search for `.pdb` file and run visualize `perl` script. +``` +python auto_perl.py -s [propensity score file] -t [DPS or PS] +``` +PS means to visualize peptide by peptide propensity , DPS means to visualize peptide by dipeptide propensity. +This code is from [Shinn-Ying Ho's Lab website](http://iclab.life.nctu.edu.tw/iclab_webtools/SCMBYK/download.php) with some modification. \ No newline at end of file