igemsoftware2017 · tedfytw1209 · Oct 31, 2017 · Nov 1, 2017 · Nov 1, 2017 · Nov 1, 2017
diff --git a/IoT/IoT talk/README.md b/IoT/IoT talk/README.md
@@ -3,18 +3,22 @@ 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.<br />
+#### 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
 --------
 The `iot_sensor.ino` is a part of our IoT talk system that can sensing the weather information,
 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.
 ```
 <Bridge.h>
 <BridgeClient.h>

diff --git 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
@@ -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
+After training , model output a `.npz` format file ,it can be analyzed easily 
+by the function of `tl.files.load_npz`.
diff --git a/LICENSE b/LICENSE
diff --git 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
@@ -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
@@ -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.