iGEM 2020 — Team GunnVistaPingry_US

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# Team:GunnVistaPingry US/Notebook

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* Team
  + [Team](/Team%3AGunnVistaPingry_US/Team)
  + [Attributions](/Team%3AGunnVistaPingry_US/Attributions)
  + [Collaborations](/Team%3AGunnVistaPingry_US/Collaborations)
* Project
  + [Description](/Team%3AGunnVistaPingry_US/Description)
  + [Design](/Team%3AGunnVistaPingry_US/Design)
  + [Proof of Concept](/Team%3AGunnVistaPingry_US/Proof_Of_Concept)
  + [Implementation](/Team%3AGunnVistaPingry_US/Implementation)
  + [Results](/Team%3AGunnVistaPingry_US/Results)
  + [Engineering](/Team%3AGunnVistaPingry_US/Engineering)
  + [Contribution](/Team%3AGunnVistaPingry_US/Contribution)
  + [Notebook](/Team%3AGunnVistaPingry_US/Notebook)
* [Safety](/Team%3AGunnVistaPingry_US/Safety)
* [HP](/Team%3AGunnVistaPingry_US/Human_Practices)
* Awards
  + [Education](/Team%3AGunnVistaPingry_US/Education)
  + [Entrepreneurship](/Team%3AGunnVistaPingry_US/Entrepreneurship)
  + [Model](/Team%3AGunnVistaPingry_US/Model)
  + [Software](/Team%3AGunnVistaPingry_US/Software)
* [Poster](/Team%3AGunnVistaPingry_US/Poster)
* [Presentation](https://video.igem.org/videos/watch/a8686af6-ec52-4189-837c-2f4563638039)

Notebook

* [JUNE](#june)
* [JULY](#july)
* [AUGUST](#august)
* [SEPTEMBER](#september)
* [OCTOBER](#october)

Phase 1
![](/wiki/images/5/5e/T--GunnVistaPingry_US--TimelineMascot-1.png)

JUNE

Week 1 (15th of June- 21st of June)

Tuesday

* June 16, 2020

+ Discussed commonly asked questions about iGEM and organized future meetings.

Wednesday

* June 17, 2020

+ Introduced Dr.Perli, Assigned groups/roles, Reviewed deadlines, Medal criterias, Finalized project idea, Reviewed Basic CRISPR knowledge

Week 2 (22nd of June- 28th of June)

Wednesday

* June 24, 2020

+ Reviewed project goals and purpose of the project with Dr.Perli and assigned to review the original algorithm’s paper “Compact and highly active next- generation libraries for CRISPR-mediated gene repression and activation”

Week 3 (29th of June- 5th of July)

Tuesday

* June 30, 2020

+ Summarized and reviewed previous CRISPR research paper that we will be building upon. Reviewed what variables the original algorithm takes in.

Wednesday

* July 1, 2020

+ Assigned the biology focused team to research and analyzing genes.

Phase 2
![](/wiki/images/e/e0/T--GunnVistaPingry_US--TimelineMascot-2.png)

JULY

Week 4 (6th of July- 12th of July)

Wednesday

* Wednesday

+ Reviewed the research on genes and their secondary structures and discussed logo designs

Week 5 (13th of July- 19th of July)

Tuesday

* July 14, 2020

+ Discussed updates and progress on research of specified genes, assigned team members specific tasks such as logo designing, compsci and bio specific tasks.

Week 6 (20th of July- 26th of July)

Tuesday

* July 21, 2020

+ Reviewed possible logo designs, discussed iGEM questions with our iGEM mentor Craig M. Trester, introduced benchling and discussed the code.

Wednesday

* July 22, 2020

+ Explored Benchling and discussed ViennaRNA

Week 7 (27th of July- 2nd of August)

Wednesday

* July 29, 2020

+ Discussed and reviewed how our project involves PCR with Dr.Perli

Phase 3
![](/wiki/images/6/64/T--GunnVistaPingry_US--TimelineMascot-3.png)

AUGUST

Week 8 (3rd of August - 9th of August)

Wednesday

* August 5, 2020

+ Discussed wiki building, fundraising and Dr.Perli presented slides on qtPCR and its importance in this project and how we can quantify the success of CRISPRi transcription regression

Week 9 (10th of August - 16th of August)

Wednesday

* August 12, 2020

+ Mentor Craig Trester and Dr. Perli answered questions about judging rubric/form, sponsorship, global meetups, and discussed the tasks the project involves in greater detail.
+ Separate meeting with compsci group looking over programs and data files which needs to be installed for the program to work, reviewed concepts and ideas for machine learning.

Saturday

* August 15, 2020

+ Worked on compiling the program and got compatible files, Dr.Perli explained how to move forward with the code, and had difficulties with the github since it didn't have everything we needed.

Week 10 (17th of August - 23rd of August)

* No update

Week 11 (24th of August - 30th of August)

* No update

Phase 4
![](/wiki/images/6/64/T--GunnVistaPingry_US--TimelineMascot-3.png)

SEPTEMBER

Week 12 (31st of August - 6th of September)

Monday

* The project is started with sgRNA machine learning Python scripts used for Weissman lab’s
  next-generation CRISPRi and CRISPRa library designs as base. These pre-developed scripts were
  hosted on github. (Ref: <https://github.com/mhorlbeck/CRISPRiaDesign>)
* Spent time reviewing and debugging Weissman code

Tuesday

* Continued reviewing the code base and flow
* Started setting up the environment in AWS

Wednesday

* Continued understanding the Weissman code base
* Set up github

Thursday

* Started setting up Ubuntu environment on AWS
* Started setting up multiple versions of Python environment - v3.6 and v3.8

Friday

* Completed setting up Ubuntu and Python
* Setup secured version of Jupyter notebook server and exposed the whole setup over DNS
* Ran Weissman code on Python v3.6 which did not work

Saturday

* Environment setup is complete and is hosted successfully on AWS. This contains code and all
  the data files required
* Started porting Weissman code base from Python v3.6 to v3.8

Sunday

* Continued porting the code to Python v3.8
* Started fixing the issues to make the code functional on v3.8

Week 13 (7th of September - 13th of September)

Monday

* Completed porting the code v3.8
* Continued debugging issues to make the code functional

Tuesday - Sunday

* Fixed screen processing experiment module to generate scores from the experiment data
* Started working on algorithm to generate bowtie index files for the human genome based on
  hg19
* Time was completely spent on debugging Weissman code to make it functional on Python v3.8
* Discussed progress and improvements on the Weissman code with Siva. Planned future implements of the code, outlined the bio teams tasks and reviewed how the code interacts with the TSS database of the hg38. Rearranged schedule and meetings.

Week 14 (14th of September - 20th of September)

Monday

* Finally was able to make Weissman code functional on v3.8. All the issues were resolved and
  code was successfully run
* Also, genome data from the lab was provided by Dr. Perli

Tuesday - Friday

* Transformed qRT\_pcr\_data scores obtained by Dr. Perli from excel and normalized the data
* Started Experimenting Normalizing the data and different features.

Saturday - Sunday

* Started using the normalized experimental data and apply machine learning to tune the
  model

Week 15 (21st of September - 27th of September)

Monday - Tuesday

* The original model that was previously created and trained using phenotype based analysis and
  scores did not give good results when the guides suggested by the library are used in the
  lab.
* Discussed wiki goals, explored the Weissman code and brainstormed names for our project.
* Reviewed the iGEM CRISPR AWS Installation Instructions to make it easier for the compsci group to do the installation.

Friday- Sunday

* We continued working training and fine tuning the original model using good quality data,
  generated and measured by Dr. Perli in his lab
* Tested guide Used scores to predict guides for genesscores with Dr. Perli

Week 16 (28th of September - 16th of October)

Monday - Thursday

* Loaded scripts and empirical data to train the model and predict sgRNA activity
* Box and Scatter Plots are drawn to analyze the correlation between the features and scores
  evaluated in the lab
* Features are tuned and, square of distance of guide from primary/secondary tss is added as an
  additional feature and generated TSS annotations using FANTOM dataset
* Calculated the parameters for empirical sgRNAs

Friday- Sunday

* Continued working on training and tuning machine learning models to predict sgRNA
  activity
* Other features that were used in training the model are: length of guide, distance from tss,
  square of distance from tss, homo polymers, Chromatin accessibility (DNase, FAIRE, MNase),
  Nucleotide Dimers at each position, Secondary Structures, strand
* Trained and created different models with different combinations of the above features
* Started evaluating the results of the above models and continue with tuning these models

Phase 5
![](/wiki/images/9/91/T--GunnVistaPingry_US--TimelineMascot-4.png)

OCTOBER

Week 17 (5th of October- 11th of October)

Monday - Wednesday

* Continued working on training the models with new changes and is evaluated again
* Distances are linearized using SVR and other parameters are used as bin values
* ElasticNetCV is used as regressor
* Trained and tested multiple random sets with same set of features and observed AUC-ROC & R2
  values to determine the best model
* Constructed the sgRNA libraries/models and picked the top sgRNAs for a library, given
  predicted activity scores and off-target filtering
* Finally a best model with better statistics is selected as final model

Thursday - Sunday

* Work to draw multiple graphs and tune the model
* Work also in progress to compare scores predicted by our model vs Weismann library vs lab
* Changes done offline
* Compsci group installed the AWS installation and worked with the jupyter guide and downloaded PUTTY.exe

Week 18 (12th of October - 18th of October)

Monday - Wednesday

* All compsci students worked on installing private AWS
* Worked with the code and PUTTY

Thursday - Sunday

* Work to draw multiple graphs and tune the model
* Work also in progress to compare scores predicted by our model vs Weismann library vs lab
* Git repo is created:
* Put all activity
* Updated README.md file
* Replaced the python version in README.md file and updated the README.md file and .gitignore, created a in depth version of tasks that need to be finished and assigned
* Created the iGEM\_CRISPRi\_Library\_Design.ipynb, Merged branch ‘dev’ on github, updated sgRNA\_learning.py, Normalized lab scores, Made gene exclusion as configurable, Added boxplots for normalized TSS data and validated the model for a different gene.:
* Updated model to linearize the relationship between the distance and the target score, Edited GeneFoldList

Week 19 (19th of October - 25th of October)

Wednesday - Friday

* Enhanced model with additional parameters, Selected the best training data set for the model, Added scatterplots, Created BowTie Output Directory, Worked on Printing features identified by the regressor along with coefficients
* Changed the Gene excluded to EIF4G1, Displayed comparison data in terms of ranks, Changed from log(1/scores) to 1-scores, Compared Weissman and iGEM scores using mean squared error, poisson and gamma
* Updated Off-target Stringency by making changes related to support off-target stringency
* Added support to find guides for given genes by adding a cell to take a gene name as a variable and find the guides and score them and cleaned up the code.
* Fixed off-target stringency by debugging for off-target stringency and removed poisson and the gamma as the target scores can be negative
* Created a gene selector that finds guides for a given gene and scores them using the model and calculates off-target stringency, created a score comparison study of Weissman Lab Scores and iGEM algorithm score with Dr. Perli's measured scores. Updated iGEM\_CRISPRi\_Library\_Design.ipynb, Deleted unnecessary files

Week 20 (25th of October - 26th of October)

Monday

* We worked on wiki design with guidance from Lauren Turetsky
* Discussed and worked on the final touches/updates

Contact:
navya.lavina@gmail.com
For more info please visit [ODIGOS](https://odigos.cloud.netrovert.net/) website

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# Team:GunnVistaPingry US/Engineering

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* Team
  + [Team](/Team%3AGunnVistaPingry_US/Team)
  + [Attributions](/Team%3AGunnVistaPingry_US/Attributions)
  + [Collaborations](/Team%3AGunnVistaPingry_US/Collaborations)
* Project
  + [Description](/Team%3AGunnVistaPingry_US/Description)
  + [Design](/Team%3AGunnVistaPingry_US/Design)
  + [Proof of Concept](/Team%3AGunnVistaPingry_US/Proof_Of_Concept)
  + [Implementation](/Team%3AGunnVistaPingry_US/Implementation)
  + [Results](/Team%3AGunnVistaPingry_US/Results)
  + [Engineering](/Team%3AGunnVistaPingry_US/Engineering)
  + [Contribution](/Team%3AGunnVistaPingry_US/Contribution)
  + [Notebook](/Team%3AGunnVistaPingry_US/Notebook)
* [Safety](/Team%3AGunnVistaPingry_US/Safety)
* [HP](/Team%3AGunnVistaPingry_US/Human_Practices)
* Awards
  + [Education](/Team%3AGunnVistaPingry_US/Education)
  + [Entrepreneurship](/Team%3AGunnVistaPingry_US/Entrepreneurship)
  + [Model](/Team%3AGunnVistaPingry_US/Model)
  + [Software](/Team%3AGunnVistaPingry_US/Software)
* [Poster](/Team%3AGunnVistaPingry_US/Poster)
* [Presentation](https://video.igem.org/videos/watch/a8686af6-ec52-4189-837c-2f4563638039)

Engineering Success

* [Phase 1](#phase1)
* [Phase 2](#phase2)
* [Phase 3](#phase3)

Quality of guide RNAs can be measured only after doing a lab experiment. But when you cannot get to a lab, we can use software to do quality scoring. While our goal is to design and build a software tool based on machine learning, we wanted to start based on something that already exists.

After research, we found a machine learning based algorithm used by WeissMan’s lab.
[(https://github.com/mhorlbeck/CRISPRiaDesign)](https://github.com/mhorlbeck/CRISPRiaDesign)

But that source code is very old and currently not functional. It can be seen from below snippet that there are others who also want to use same tool but having issues that are not fixed in years.

![](/wiki/images/a/a4/T--GunnVistaPingry_US--Engineering5.png)

It is a very interesting and exciting experience on how we finally could take it to a level that can be a base for our project.

Phase 1

**Researched** into different modules used by the above project and tried to understand what those modules are, what kind of algorithms are used.

We **Designed** AWS Based Cloud Platform that is accessible to the entire team over the internet and setup an environment as mentioned in the project.

We started the **Development** on Python 2.7 as mentioned in the project and fixed a few initial bugs we encountered.

When we **Tested** The flow stopped at creating Secondary structures relating to RNA.

![](/wiki/images/6/6e/T--GunnVistaPingry_US--Phase1.png)

Phase 2

We **Researched** into ViennaRNA again and finally found issues with Python version incompatibility.

We **Designed** again a virtual environment with Python version 3.8 in the same cloud environment.

Now we ended up with a Development effort of porting all current modules of above github project into 3.8.
When we started **Testing** the flow stopped now at a different stage where Off-target stringent related code required few bowtie index files to test alignment.

![](/wiki/images/2/2d/T--GunnVistaPingry_US--Phase2.png)

Phase 3

We had to **Research** into bowtie indexes that need to be built for different flank sequence regions and other regions.

We had to take help our Dr. Perli to understand the semantics of these indices and **Design** our environment again with appropriate bowtie modules.

We Created bowtie indices files by downloading appropriate genomic data and then made code modifications as part of **Development** to use these indices.

When we **Tested** we finally landed with some code that can be base for our main project which tuning algorithm after training with Quality Data.

In these different phases we **Learnt**

* How to setup and work in AWS Cloud Environment
* Machine algorithms like lasso/Ridge/ElasticNetCV
* Statistical Analysis of Data
* What is Vienna RNA
* Various versions of bowtie indexes how they help with faster alignment testing instead of going through the entire genome.

In this whole process we Improved the current code and brought it to a stage that can be used by others. Our project based on this base and works speaks itself of this Engineering Success!!!

![](/wiki/images/3/3a/T--GunnVistaPingry_US--Phase3.png)

Contact:
navya.lavina@gmail.com
For more info please visit [ODIGOS](https://odigos.cloud.netrovert.net/) website

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[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:GunnVistaPingry US/Experiments

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* Team
  + [Team](/Team%3AGunnVistaPingry_US/Team)
  + [Attributions](/Team%3AGunnVistaPingry_US/Attributions)
  + [Collaborations](/Team%3AGunnVistaPingry_US/Collaborations)
* Project
  + [Description](/Team%3AGunnVistaPingry_US/Description)
  + [Design](/Team%3AGunnVistaPingry_US/Design)
  + [Proof of Concept](/Team%3AGunnVistaPingry_US/Proof_Of_Concept)
  + [Implementation](/Team%3AGunnVistaPingry_US/Implementation)
  + [Results](/Team%3AGunnVistaPingry_US/Results)
  + [Engineering](/Team%3AGunnVistaPingry_US/Engineering)
  + [Contribution](/Team%3AGunnVistaPingry_US/Contribution)
  + [Notebook](/Team%3AGunnVistaPingry_US/Notebook)
* [Safety](/Team%3AGunnVistaPingry_US/Safety)
* [HP](/Team%3AGunnVistaPingry_US/Human_Practices)
* Awards
  + [Education](/Team%3AGunnVistaPingry_US/Education)
  + [Entrepreneurship](/Team%3AGunnVistaPingry_US/Entrepreneurship)
  + [Model](/Team%3AGunnVistaPingry_US/Model)
  + [Software](/Team%3AGunnVistaPingry_US/Software)
* [Poster](/Team%3AGunnVistaPingry_US/Poster)
* [Presentation](https://video.igem.org/videos/watch/a8686af6-ec52-4189-837c-2f4563638039)

Experiments

[Heading 1](#div1)
[Heading 2](#div2)
[Heading 3](#div3)

Heading 1

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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Heading 2

Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Heading 3

Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Contact:
navya.lavina@gmail.com
For more info please visit [ODIGOS](https://odigos.cloud.netrovert.net/) website

![](/wiki/images/7/7f/T--GunnVistaPingry_US--Top1.png)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:GunnVistaPingry US/Results

[![](/wiki/images/8/84/T--GunnVistaPingry_US--Logo1.png)](/Team%3AGunnVistaPingry_US)

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* Team
  + [Team](/Team%3AGunnVistaPingry_US/Team)
  + [Attributions](/Team%3AGunnVistaPingry_US/Attributions)
  + [Collaborations](/Team%3AGunnVistaPingry_US/Collaborations)
* Project
  + [Description](/Team%3AGunnVistaPingry_US/Description)
  + [Design](/Team%3AGunnVistaPingry_US/Design)
  + [Proof of Concept](/Team%3AGunnVistaPingry_US/Proof_Of_Concept)
  + [Implementation](/Team%3AGunnVistaPingry_US/Implementation)
  + [Results](/Team%3AGunnVistaPingry_US/Results)
  + [Engineering](/Team%3AGunnVistaPingry_US/Engineering)
  + [Contribution](/Team%3AGunnVistaPingry_US/Contribution)
  + [Notebook](/Team%3AGunnVistaPingry_US/Notebook)
* [Safety](/Team%3AGunnVistaPingry_US/Safety)
* [HP](/Team%3AGunnVistaPingry_US/Human_Practices)
* Awards
  + [Education](/Team%3AGunnVistaPingry_US/Education)
  + [Entrepreneurship](/Team%3AGunnVistaPingry_US/Entrepreneurship)
  + [Model](/Team%3AGunnVistaPingry_US/Model)
  + [Software](/Team%3AGunnVistaPingry_US/Software)
* [Poster](/Team%3AGunnVistaPingry_US/Poster)
* [Presentation](https://video.igem.org/videos/watch/a8686af6-ec52-4189-837c-2f4563638039)

Results

[Heading 1](#div1)
[Heading 2](#div2)
[Heading 3](#div2)

The goal of this project is to determine which sgRNA strands are best suited for use with CRISPRi. We improved upon the existing Machine Learning Algorithm to create a model to find quality sgRNA. 812 possible correlative factors in 18 genes were considered, with 180 used to train the model and 10 records of the EIFG1 gene as test data. A total of 13 factors were found to have meaningful correlation in predicting sgRNA success. The training data set was randomly divided into 17 gene folds where each fold has a further distribution of 94% and 6% for training and test data sets respectively.

Below is the table of scores predicted by the Weissman Lab’s Algorithm, the iGEM Algorithm, and team advisor Dr. Perli’s lab score.

![](/wiki/images/5/5d/T--GunnVistaPingry_US--Engineering1.jpg)

When we compared the Mean Tweedie Deviance for the Weissman Lab’s Scores and iGEM Scores with the ones measured in the lab for the gene EIF4G1, we found the following results.

Weismann: 0.14461545542489534
iGEM: 0.1387975491703964

<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_tweedie_deviance.html>

We have used Mean Tweedie Deviance calculation with power value ZERO, calculating Mean Square Error.

We clearly see that the iGEM scores are closer to lab scores with a lower deviance value.

We also measured the deviance against all other genes for which Weissman Scores and Lab Scores are available. Unlike EIF4G1 above, the guide scores of the genes measured in the lab were used to train the model. Nevertheless, the scores predicted by the model are still meaningful as ML models do not remember trained data, instead they remember features impacting the score along with the weights of those features.

![](/wiki/images/7/79/T--GunnVistaPingry_US--Comparison_Table.jpg)

Clearly, it can be seen that except for two genes (FMR1 and GNB2L1), for all other genes the, scores measured by the iGEM algorithm are very close to lab scores.

During our research, we also found out that the scores measured in the lab were based on qRT-PCR and have quadratic correlation with the distance of the guide RNA from TSS.

The following diagrams are the scatter plots of the scores against distance of the guide from TSS:

![](/wiki/images/4/4b/T--GunnVistaPingry_US--Engineering3.jpg)

![](/wiki/images/f/f7/T--GunnVistaPingry_US--Engineering4.jpg)

Clearly, the distance is in a nearly quadratic relationship to the score, while the relationship to the square of the distance is linear.

After adding the square of the distance as a dependent parameter, our regression model found it as one of the features that explain the score.

Contact:
navya.lavina@gmail.com
For more info please visit [ODIGOS](https://odigos.cloud.netrovert.net/) website

![](/wiki/images/7/7f/T--GunnVistaPingry_US--Top1.png)