Difference between revisions of "Team:SJTU-Software/project"

Introduction
Our software, BASE, has four functions: search, recommendation, evaluation and upload.
Via search function, users can search for parts or devices using IDs or features as keywords.
In recommendation interface, users can draw their devices. They can also give some keywords when drag an icon to the chain to get a list of parts which fit the require and other parts best.
When using evaluation, users firstly enter a device that they designed, then our software can give advice for each part to improve their devices.
Finally, users can upload their device to the IGEM part registry and BASE’s database.
For the first three functions, we develop a set of scoring system to evaluate the effectiveness and ease of use of the parts and devices.

Method
We get the data of parts from IGEM part registry. A total of 14971 bio-bricks are recorded in the database. Then we divide them into two groups, parts and devices, according to whether the biobrick has subparts.
Among them, ??? are parts and ??? are devices.
For each bio-brick, there’re four different websites:
http://parts.igem.org/cgi/xml/part.cgi?part=BBa_???
http://parts.igem.org/cgi/partsdb/part_info.cgi?part_name=BBa_???
http://parts.igem.org/partsdb/get_part.cgi?part=BBa_??? 
http://parts.igem.org/Part:BBa_???:Experience
When collecting data, we simply replace the ??? with the bricks’ ID.
We then extract information from the websites. The information include Part_status, Sample_status, Part_results, Uses, DNA_status, Qualitative_experience, Group_favorite, Star_rating, Del, Groups, Number_comments, Ave_rating. And we take most of the above factors into account when scoring bio-bricks.
As for optimizing the weight of these factors, we firstly analyze the distribution of value of the factors to choose the factors that can distinguish the parts most effectively. Then we select 40 parts and 40 devices as the training sets. Finally we get the weight by combining results of several methods.

Results
1.scores for different values of factors
To build a scoring system, we start at giving scores to the values of these factors. With the help of wet lab researchers, we rank the values of discrete type according to their effect on researches, and choose a relatively good method to transform successive values into values between 0 and 1.
For discrete values, we have a scoring table as below.
Table1:

For those successive values, such as used times, average rating, number of comments, we develop two scoring methods. The “average rating” factor has only 5 values, so we just simply score it as a arithmetic progression. As for the other two factors, the distribution of values seems very unbalanced. And since we can be convinced that a brick is good when it’s used several tens times and the feedbacks are good, there’s no need to force a brick to get used for a thousand times before it’s recommended to other users, though some of the parts are actually used hundreds or even thousands of times. So we calculate the score by the expression below.

Score=log(n+1)/log(nmax+1)

The n in the expression refers to the values. By using this expression, we reduce the effect of extreme values and make the scores more convincing.
And the optimized weight of the factors are shown in the table below.
Table2:

The above scoring system are used to evaluate all the bricks in our databases. It become effective in all the functions except upload. However, we still have another scoring system for devices.
2.devices scoring method with relationship between parts
This method is mainly used in the evaluation function. For a device which is just designed by users, the score we get through the first method actually mean nothing, as there’re no information for the device on the registry. So we need to develop a new evaluation system based on its composing parts and relationships between the parts.
When evaluating the relationships between parts, we take several factors into consideration, such as the frequency and the average score when the parts are used together and so on.
Firstly the weight of the two aspects is confirmed. The default ratio is 65% for the parts and 35% for the relationships. In the first aspect, the weight of different types is dynamic. It’s influenced by the number and type of the parts. However it still shows the different significance of the parts. But in the second aspect, all relationships share the same weight.
Then the scoring begins. Given that wet lab researchers care more about the outcome of a device, we search for functional coding parts in the device, and optimize it in the first place. After the user locks the functional parts, we start to optimize other parts. The order is decided according to their type and location in the device.
Since there’re two scoring system for devices, the weight in the second one is adjusted to make the scores made by different method close so that scores for new devices can have the comparability with those already in the database.
3.Adding parts one by one
This method is mainly used in recommendation function. It’s similar to the second one, but it only cares about the new adding part and relationships when doing the recommendations. The weight’s also adjusted to fit the other two method.

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