Thanks everyone that's joining. We'll go ahead and get started in a minute or so.
So I see the the.
Rate of participants. Joining is.
Leveling off.
So I think I can get started with just some general introductions. This meeting is being recorded because we're gonna make it available.
Later for other people who can't join this this time slot to watch.
And
This is.
Really exciting for us. This is really the.
Conclusion, or I don't know. This is after 5 years of
Working on this. This is our big challenge.
And we are really excited, because I think we've learned a lot from the way we've done this the last time we have more data. Now.
I think we have more solid tasks. I think we have a better understanding of participants.
But this is still this is the 3rd time we're doing a prediction challenge. And.
I mean Primoth, who's gonna take over in a second.
It's gonna explain this a bit more. But I still consider this to be a bit of an experiment.
This, this model of using prediction challenges to.
Really test different computational approaches.
For the predictive value is
Has been.
Proven to be extremely successful, and things like structure, prediction, or so.
And and we are trying to move this onto a much more complex field namely, vaccine outcomes.
So I'm super excited to also see like.
A bunch of people here who I don't know.
And a bunch of people who I do know from the from the last round.
So this has been.
Exciting. The last time around is good to see people coming back.
So let's get started and promote. Why don't you take over and and all the credits here for much of the work? Go to promote.
And team.
Not so much for me.
So please from our take it away.
God, you're muted.
Yeah, thanks for noticing that.
Yeah. So thanks beyond for the kind introduction. Yeah. So the prediction is a competitive event in which individuals are group
Make predictions specific to outcomes based on their knowledge expertise in the field.
And and this exploration of prediction contests started long back in 19 nineties to bridge the gap between the structure prediction.
Community which is mostly at that time, and and with with with the with the bigger community, which is, which is still which was striving at that time, which is computational biology community, and that led to.
In in past 30 years there are around 13 different.
Casp or the protein challenges. And after this challenge, you see that a lot of different areas in the science, including the biological sciences.
Where, where the community prediction challenges are pretty much common, and also in other sciences, and also tools
So all the general problems. There are a lot of community platforms. We establish.
Specifically Kaggle, which is by Google and and the dream challenges, which is particularly for the biological challenges. So so.
So why there is need of these prediction challenges because they are quantitative and unbiased.
Evaluation.
Of model performance. They do. They are drives from all the background, and and and make them strive for the accuracy and out for form.
There. There are other participants, and they encourage
Wide range of participants and contribute to a deeper understanding of of topic topic in in hand. And there are a lot of different obstacles in developing models, in in Bali application and specifically systems.
It's because if you see systemology
Studies they are wearing in system designs. They are very specific to the topic which they study, and and data is generally multi-dimensional and heterogeneous, and there are a lot of.
Parameters. They are in range of thousands.
Specifically, if you consider the assay like. Rna saig.
And and very few steady participants are enrolled for those studies. So it makes.
The.
The agenda generalizability and the prediction performance of these models.
Testing very hard as as a problem.
And then it's it's also needed for accountability of variability between the study subjects or individuals to ensure that whatever models people build, they are, they are robust and and accurate.
And also, and also these studies are mostly pre post, and also time points are involved. While collecting these samples. So so to capture dynamics. It's it's pretty important for for systemology models.
So to out.
To overcome. These problems, we establish Cmib as a resource and and a community platform to develop and test computational models.
In in in vaccinology and and to understand vaccine induce immunity to border.
So today we are going to do a deep dive of Cmb project which we are running from last 5 years, and and how we define Cmib.
Challenge or or contest, and and what what kind of information is available, like the task, the data and and all the all of the resources which we are providing through the website. We also.
Wanna give a few examples of the example models which we establish in past to channel challenges.
And and those are readily available for everyone of every one of you to to reuse and and and make your predictions better. And then we also talk about the all the resources brief overview. And then we plan to hold a. QA. After that.
So let's jump into the product. Project overview.
So open cup, which is caused by about Persis, which is gram negative bacterium in an upper respiratory tag
It causes short and long term. Symptoms short term symptoms include lower fever, mile, cuff, and apnea in.
In in infants, and there are also long term symptoms like cuffing feeds, ending with the oof sound.
And and then the meeting and exhaustion like like this sentence.
And there is a prevention which is established by the dtp vaccination which is given at the childhood, and also the booster vaccination is given.
An epidemiology explains that. Yeah, there are. There are wide.
The big number of debts has been happened in the past. And also, yeah, the the disease is spreaded all all across the.
Go.
So that if you see the case load in in us itself, the data is did. I'm I'm showing is from the Cdc. So until 1994, the Caseload is.
Around 200 to to 50,000 cases per year.
And there was introduction of whole bacterium, which is Wp. Introducing late 19 forties, and that led to dramatically degrees in Caseload and and by 1970, 1980 there was no need. Resurgence of
This disease.
But around 1980 90, there was a concern about the reactogenicity of wholesale bacterium vaccine that led to
Us. Government to decide the replacement of w vaccine with the Aclis vaccine.
And which was introduced in 1995, and after introduction this vaccine in 1st 10 years the Caseload was pretty much steady, but by 2,005. Yeah. The cases were started to increase again, and then that led to introduce a booster vaccination around that time. Not just in us, but but all all other other countries.
And and after that also, you see the cases where, increasing and around 90 2,013, it was declared as a epidemic in in California, specifically.
So.
So so the waiting immunity from Ap purchases. Vaccination is important to study and and to compare with the the old vaccine, which is a w vaccine.
So to to go over the our recruitment strategy. How we study this problem. I replace my colleague Lisa, to talk about the criminal data that we had.
Thank you very much, and welcome everyone.
So for the Cpv project we enrolled a total of 71 adults and those adults were primes during infancy with either the Ap. Or Wp. Vaccine.
When those owners were enrolled they were eligible for to get a taboocation. But before they got this boost of vaccination they donated blood at up to 3 different time points, and those time points formed our baselines, as you can see here.
Then they got a taboo vaccination, and after the booster vaccination they donated blood at an additional 5 different time points which form our post vaccination measure, time points.
Then we use the blot and the plasma of these time points to perform different experiments.
Next slide, please.
Thank you.
And here you can see an overview of those experiments. So one of them is, for instance, that we measured the immune cell of the blood using flow, setometry.
So we measured a total of 37 cell populations, and one of them is, for instance, the monocyte of the blood.
Then the second one is the Antigen specific antibody measurements in the plasma, and we are using Olink to measure these.
We are measuring the total Itg level.
Levels, but also the Itg subtypes and all those itgs are specific for define antigens. So these isotypes. They recognize, for instance, the protest as an Antigen, but it can also be the non purchase vaccine antigens, such as that's in a stock so.
Then a 3rd essay that we are performing. Is the oink and legend which are we are performing these to measure the Cytokine concentrations in the plasma of 48 different Cytokines, and one of them is, for instance, l. 6.
Then the 3rd S. 4th essay is the bulk Rna sequencing of the Pmc's and those form our transcriptomics. Data.
And the last one. This one is actually a bonus, and the 1st time that we are.
Adding these data to the prediction challenge. So right now, we are adding the specific T cell responses. So we performed a flu response on all the donors.
And we.
So, for instance, for the Msa, we are measuring the percentage of CD 4 positive T cells that are activated in our specific.
And then with a flu spot, we are measuring the Cytokine polarization. So we are measuring, for instance, in from Gamma. STH. 1, 5 for ch. 2 and l. 17 for th 17 cells.
And with that I would like to give the word back.
Thanks, Lisa. So so all 6 assets for about 1 71 subjects.
So so this data is generated by Cp grant, and this data will be pushed into the central database alongside the we also have data from external funded and and a lot of information coming from the literature to to map map map ids, etc. So all this data we put together into the central database, which will be made available through.
The data Api's to see my big consultions labs, and on all the users.
Who are participating for the contest. Also, we are holding back the response data for the challenge data set, which is restricted and which will be released after the Annual Protection challenge, which I'll be talking in in detail shortly.
So.
So having this as a data is itself is.
Having a lot of information, and to make it simple for contestants, we make a schema in a very standardized format, so that it dies consistently read. And we divide the schema into a subject and specimen, information, table subject and specimen there. And there are information about the experimental data sets in all these 7 data tables. And also we provide id mapping information through.
The metadata information for an example like we we say here on civilian id, and so so the gene name and everything else can be fetched from the metadata tables.
And and all. I'm not going in detail. But the schema information and the database information is provided on the website for for the deep dive.
So now, after having the data base in very standardized format established, we open this data for this movie challenge. And I'll be talking in detail about what components are there for this? Cmb challenge.
So in total, we are planning to do 3 challenges. And the 3rd challenge, which is open for public which consist of
1 1 7 steady subjects coming from the past, combined together 96 plus 21. That makes our 1 7, th and we have the new cohort of 54 steady participants which which is set for the challenge data sets. And this challenge is is.
Yeah.
Open now, and there are lot of different components. Starting from the, I mean profiling data sets which is divided into training and test data set or challenge data sets. And then we have.
Task, or questions to answer by the contestants, and then, after having this question answered, we provide the.
The submission format of for for
For submitting models. And then we evaluate these models, and at the end we have prices. I'll I'll go over this all these components one by one.
So so this is a brief overview of data, so that the entire 171 subjects is, Do are divided into training and challenge data set. And for challenge data set for training data sets. There are 3 cohorts, 3 independent cohorts, 2021 and 22 which will be used for the building models.
And for this, for this training data set we give both pre-vaccination with 3 time points, and then the post-vaccination of 4 to 5 time points that includes the outcome or the ground truth. And we also provide the demographic information about this subjects, and and and and and participants are welcome to.
Design their own features by doing the feature engineering and the challenge data sets which include one cohort of 54 participants and for which we just provide the pre-vaccination information and the oppose vaccination. Information or response data is hidden from the participants which will be released for the model evaluation at the at the end of the contest.
Now. Since as as I've just said, the the data set includes the training and test and for the training and test in total, we have the 4 different cohorts. So there is natural question about how do we make create a comfortable mattresses which are suitable, the suitable for the model development. So it's it's it is essentially the data processing and.
The normalization before going this data into the
Into the specific models. So this data processing and normalization approaches are very specific for the users with their expertise.
But but learning from the past 2 challenges team is also providing the standardized data.
For for contestants who might not have the expertise in data normalization where these mattresses can be directly used.
And and and and and models can be developed. So the entire data is provided into 3 different
Formats. The 1st one is a raw experimental data which is coming directly from the experiments, which is very close to experimental. We run, then the second one, which is the harmonized data. It is exact. Same data is just that for all 4
All 4 cohorts. We have the exact number of the features, so that it's easy for the data and for the data processing the next one, which is the process data which we also termed as a batch, corrected data which I'll be explaining in in in a moment.
So where do you find this data?
So you go to the Cmp website.
C maybe.org.
And if you go to the prediction, challenge, click here.
Then, yeah.
There is a lot of information which we'll be covering in in a moment. I'll directly jump into data and resources.
So here, you see
The information about a study design and data sets is explained.
And then we have information about the trading and challenge data set. Then we say here, where the raw data sets are available.
I'll I'll be going into St in a moment. And also we provide the information of the harmonize and process data sets.
I'll go into the Github which we created.
We where we explain as a step, one of data harmonization, what we exactly did. So we standardize data for the different formats to ensure the consistency of features and sample identifiers.
And and we also do the feature mapping across all 4
Data sets.
Then in the second step, where we pro, we create the process data where we do data normalization and batch corrections.
Step by step. And and all this information is provided as a as a code in here.
Now the natural question is,
Like how to discriminate, or what information does this.
Pipeline gives. So for an example, I'll give you an example of the self frequency analysis.
So you see. So there are clear batch effects of all 4 data sets. When you see the PC plot and also on the PVC variance part. You see, data set has the clear 90% of explained variance, and the actual signal is somehow hidden.
And how to.
Get back this signal and make data more informable. So for this purpose, we do data normalization. And when you do that, you see the the expected signal is jumped up, and the data signal is went down, and all 4 data sets are pretty much consistent.
And after batch correction. Yeah, the data is even better now.
So all this information is available on the.
Website.
Alright! One more time!
Here, and if you go to the Stpp side.
For the 3rd challenge. Yeah, the raw data sets are.
Made a through the training and challenge data sets.
In in individual.
And if you go to the harmonizan presses, there are 2 kind of data sets. The 1st one is harmonized data which is available as Rds file for the R user. And if people are using other programming languages datasets are also made available in Ntsa files in here.
Similarly for the batch. If I corrected data.
Yeah, rt, file is available. Also, we make the Tc files.
Now, after having access to this data, we provided tasks which are basically the.
The the questions which which we
Found from our previous publications in in and where we we identified 3 kind of tasks. One is related to general vaccine responses. Would we find that plasma Igg labels were increase post booster vaccination against purchase toxin on day 14. So we ask you to predict
This response, the the the actual task questions. I'll be reading in a moment. The second interesting general response which identified is the monosy prefency increase on day one post booster vaccination. And also we also have the 3rd task, which is very specific to bottler purchases vaccination where the some of the Ap. Prime individuals showed the individual.
Expression on day 3 for the pro inflammatory genes. And here we form tasks by asking.
C. Cell 3 Gene. Expression.
So.
So this task come into a.
3 groups, 1, 2, 3, for I'll give example of antibody title, task.
Where the 1st task, where we asked to rank individuals or steady participants by Igg antibody against the purchase toxin that we detect in plasma, or they opposed.
Day 14 post of vaccination where the ranking should be done on the predicted values. Then the second
Second task, which is.
Exact similar. It's just that participants needs to calculate the full change values. And and then those on on the basis of those full change values the rank should be calculated. I'll be giving example in in a moment. So the idea is to have
To model as many as possible. But, however, contents are not required to submit all all the tasks, even if they submit one or a few that that should.
So this is how you do ranking. So you have the subject. Id.
And then the baseline. Values are provided for. The challenge data sets. You will.
Predict the post vaccination response. Let's say for Day 14. And those are here. Then you calculate the full change values, using the baseline and the post vaccine values and then using the post vaccination values, you calculate the ranks and using the full change values. You calculate the full change based ranks. And then you submit this ranking to on the See, my Vp website.
So the exam, the example file looks like looks like this. So if the submission involves all the tasks completed. So you have ranking, looks something like this.
If it it can, contains only 2.
2 tasks answered, so yeah, for an example, one and 3.1 is answered. So we ask you to fill in those columns and leave blank. All other columns.
Yeah. And after you submit, your submission files in in this format, which is Tsv file.
We compile all the submissions and do the spare man correlation coefficient.
Calculation as a firsting step. Where, after you submit the predicted ranks, we see if there are any blank sales, or the na is put in there. It's it's it's it's sometimes. People discard some of the subjects because they are not following some some trends. So so that end up in na. So yeah, we replace those with the median rank of the submitted files.
And using this modified or imputed file, we calculate this Pairman coalition between the predictor and the actual ranks, which is which is hidden.
Then the as a second step we design a point system where, if you are a.
Task winner. So we are giving the 8 points. If you are standing between the 98
Person confidence. Internally we assign 6 points, and if your Spearman correlation coefficient is significant, we give 1 point. So for each task there are maximum, 8 points will be given, and for all 6 tasks, maximum, 48 points will be given, and this is the simple example. Where where we are, we identify the winners. So after giving the individual points as per
As per models predicted. The all the tasks.
We calculate, or we sum all the ranking points.
Which are, which are which we term as a final points, and the winner which has the maximum points.
Which is the 1st winner, and then the second and 3rd minute will will be calculated.
And all these details are available on the website.
And after identifying winners we have prices.
And the awards given to the participants, which are very similar to the second challenge, where all the submitters will be with at least one significant sparement, correlation coefficients will be acknowledged as the co-authors in the manuscript which will be written. Following the challenge also, we have the monetary prices of total, 5,000, where first, st second, and 3rd place is identified and awarded accordingly. Now, and the details about is, prices are.
Is also available on the website. And this is a stick pick.
Yeah, so so far, we talk about different components of the website. And the some summary looks like this. We have the data generated by by Lisa and her team.
And this data is divided into training. And the challenge where in training data sets, we provide both pre and post vaccination for the challenge data set which will be used for the prediction purposes.
We only provide the pre vaccination post vaccination information is hidden. Then we have 3 tasks, or 3 group of tasks or or 6 tasks which participants will be answered, and they will submit the.
Ranking files, and after that we score them and and and announce the end of the challenge.
And now I'll be going over some of the example models which we identified from from past 2 challenges.
So this is the manuscript. Coming from the 1st internal challenge where only Cmi Pb consortium members were participated. And we 1st team, which identified the models from the literature.
And then the 2 teams which establish multi integration based models using jive and the multiple coincension analysis or mci, and yeah, and this is one of the
This is one of the good source to to refer. The link of paper is also provided here, which will be passing you after this presentation.
Then us.
From the second challenge. We submitted this manuscript into by archive and is is under screening where there are around 25 new models. In addition to 32 models which we identified. The 1st challenge.
Where?
Also also a given where we we also have methods from individual participants. And and just to say that all the teams which participate in the second challenge are awarded in the in the authorship listed here.
So.
I'll I'll be briefly talking about the winning model from the second challenge, and how it looks like broadly. But I would also recommend to go over this manuscript, and the Github Repo, where we have listed all 50 plus models with their codes, and and how they how they perform, etc. As as a resource.
So the winning model from the second challenge looks like this. So which is divided into 3 distinct components this is from the University of Minnesota group and they
Did the dimension reduction through.
Different steps of data processing, and also linear mixed models which they use towards the end. So they take raw experimental data as it is from the website.
They 1st identified the common feature similar to the data processing pipeline that we provided. Then they do the time points based batch effect correction and the data imputation.
Of mixing both training and challenge. And after that they have this imputed data. And after this they go for the dimension reduction model testing, which they do for last over age, Plspc and the mci, and at the end, out of this 5 they identify Mci outperforms others which they choose at the final model for for downstream analysis. And they take Mci. 10.
Factors.
And and global scodes are further used for the linear mix effect models and.
And there they divide their training data in the.
And say, a factors constricted on the training data, on the in the training and test data set and they do 5 cross validation, and and they evaluate
Data model on the test data sets.
And they identify a best performing model which they take as a final model for for for doing the prediction on the challenge data set, and after having this prediction, they submit the ranks on on the same website.
Yeah. So this model is extensively being talked in the manuscript, which will be coming soon. And yeah, and also, if you wanna do the deep dive. Yeah, the. We also had the debris session after the second challenge, where all 3, all top 3 winners presented their results. And and and this is exciting resource.
To listen in and and get more information about about the their models.
Yeah. With. With this I pass on to Shelby to talk about all of the resources that we provided through website.
Thanks. Perman, hi everyone.
Can we go to the next slide? Please.
So here is an overview of the resources that are currently on the Cmi Pb website. And I won't go through this in too much detail. But these slides will be posted on the website after this meeting, so that you are able to access the resources from each of these links here.
But I particularly want to highlight the solution center, which is in the bottom right hand corner.
Next slide, please.
Here's an overview of the solution center and specifically for the 3rd challenge. You can see it highlighted in purple here. So we encourage that participants ask questions on the solution center. And the reason for this is so that it's accessible to all the participants. In case someone has the same question as you.
And also on the dis on the solution center. Well, we will be posting the meeting recordings and slides as well as any relevant announcements here.
Next slide, please.
So in order to post on the solution center, you'll need to make an account, and you can do so by going to the website and clicking the sign in to Cm. IP. Button in the upper right hand corner. Here you'll be guided through some prompts to create an account, and then you can confirm that you're logged in.
By seeing your email in the upper right hand corner, as you can see here, you will also need to make an account to submit your prediction, as well.
Next slide, please.
And this is just a brief overview of submitting a prediction. We'll go into some more details.
In the future informational sessions, but from he from the homepage. Once you are logged in by clicking the submit prediction button in the upper right hand corner, and from there you'll be taken to the submissions page, where you can upload your file and submit directly from there.
Next slide, please.
Awesome, and I'll hand it over to Mari to explain a little more about the timeline and the Cmi Pv. Team before we go into some questions.
Thanks. Shelby. So yeah, today, we had our 1st informational session. We wanted to let you know that we have 2 more coming up. So.
On October 8th we'll have our second informational session, and then on November 14, th we'll have oh, sorry.
The informational session is October 4, th and then November 8, th we'll have our 3rd informational session. So what I was hinting out right now. Following each informational session. We'll have office hours. So our 1st one is coming up on September 9, th and we'll be sending more information for that as well.
And during these sessions you can just hop in and out and ask individual questions about your models, or if you're confused about anything use this time as like a 1 on one sessions. You can ask questions, do promote, or any of us.
And then we'll have 2 more as well. On October 8 and November 14.th
And then following that our final submission, due date is on November 20. So you have less than 3 months to submit your models.
And then on December 6, th we'll be announcing winners and releasing the non longitudinal test data.
Awesome next slide, please.
And with that we just wanted to thank everyone that's been involved throughout this whole process. It wasn't just those of us who presented today. This was a huge feat. Involving people from the client sign lab here, at Yale, and then grand slab at Ucsd.
A lab at lg, yeah. And also the Peters lab,
I also. Lj, so.
Yeah. Next, slide.
Yeah, these are some of the current members who are working on the project. So we also wanted to thank everyone that's been involved.
And with that we'll love to have questions so.
Next slide. If you could raise your hand using the zoom function, I'll call your name out, and if you can unmute and ask a question.
That'd be fantastic.
So so and actually following up on that. I mean, we've done this before. And the problem is always.
At this 1st session.
I mean, people are just getting started.
So there is no such thing as a wrong.
Or stupid question definitely. I mean, people always say this, but in this it really is true.
And the problem is always that at the very end.
That's when all the questions come up. So we've been trying to stagger these sessions.
To make sure that people can at any time point information.
So.
Yeah.
So I would very much encourage people to ask stuff now and again like this.
Marcus! Go ahead!
Hello! Hello! Everyone!
So my 1st question is.
So.
Kate has to be meeting more than one model.
And if I can't even one than one model, how it's gonna be evaluated, all of them is gonna be evaluated, or just the last one.
As the the last time you could.
So we made like 3 models, but only the last one will be evaluated.
So this is gonna be.
The same way that was before, or did you want to change it?
Thanks, Marcus, for the nice question. So there are 2 parts of it.
So the 1st part is in like, each team can submit different models.
So there is no restriction on. There are no restriction on the number of model submitted or different kinds of model submitted. So let's say, the entire team is divided and submitting like 3 different models.
Model one model, 2, and model 3. So these are the independent models which are submitted. Now, the second part of the question is.
When you submit the model one. So what happens like before the due date before the challenge date, one month before you submit the model. And that's what is like deposited in in our records. Now, you thought like that. There is. There is some improvement and and you wanna submit the second model.
Or the the second model for the 1st model, and and and likewise so whatever you model, you submit before the deadline, which will be considered for the final evaluation.
But as a team you can submit the different kinds of models.
I hope that that answers your question.
Next I saw a berry race.
Thank you. So the harmonised.
Yes, thank you.
Theatre seems really.
Useful, and a great.
Starting point. I was wondering from the last.
Challenge, if those winners.
They? They seem to do their own thing.
Processing step? If they use the harmonized data, would they still have one? Or is there.
Differences? Or was your harmonization inspired by that.
What, what would you recommend?
Hmm.
I, yeah, we would encourage people to use the harmonized data in a sense, to understand how the harmonized precesses is being done.
And use the code and the resources that we provide. But it's it's not. I wouldn't say like
You cannot win. Or you could win using this data. So it's it's up to contestants. Choice like.
What, what? Exactly, what exact data they want to use. But we be provided this data sets, and then the code as a resource.
I I think the question is somewhat differently.
So so. So I think Barry is pointing out that that.
This might be an advantage to not use it.
And do your own preprocessing can correct me.
But clearly, I mean, we are not gonna punish anyone. No, no, anybody can. use the the Pre process data.
But
There might be good reasons to do it differently, and that was one of the reasons why some of the people want in the past.
So don't just take it.
And and that's 1 of the things we would like to learn right if if there's a better methods for preprocessing.
That would be fantastic to figure out.
Thank you, Barry.
Next I had.
I think, raised your hand.
Hi.
So yeah, I had to concern.
That that's very good.
The to confirm that in Japan.
Palatin, and.
Done with number.
Can we start with early limited number per tab.
Yeah, I think you can submit a number of multiple models per task is just that. You need to put all your models for individual tasks it as a 1 submission file, but that is what is being evaluated.
Like gathered together through the point system, and then the winners are unannounced.
But yeah, you're you're welcome to submit as many of as many number of models you wanted.
Thank you. Thank you. At the consent I drew a bad deal. Yeah.
The.
But to the bad, the pattern that got it. But.
It's it.
Pre-med.
Equally prevented and evade it moved, prevented in some part.
So I can take this, or Lisa can correct me. Then.
So it is actually a.
Prevalent throughout the world.
The.
White.
Equally. And it's.
Found pretty much everywhere.
It's just, and then very often, like when you even do like.
Sequencing of microbiomes in the lung. You'll find it.
But the people who get infected typically, I mean the the.
Obviously, that is this colonization infection? Right? So so.
That happens primarily to infants, at least where it's really dangerous.
And
So the the short, the long answer is, it is prevalent.
Everywhere.
And
Yeah, that's pretty much it. Sorry I didn't.
Do a good job with this one.
10, converted.
Wheel.
Thanks, Mahita.
Next to Nayana. If you can unmute yourself and ask your question.
Hi bringing back!
The question regarding which would, what would be the correct choice to use? What data.
Just a curiosity depending upon which route or process we use. But we are going to be assessed.
On the final answers that you guys have already obtained. I believe so are your values biased to any specific data set that you use that might make any difference.
Just.
Yeah.
So. So this is one of the reasons why we're using ranking as a method.
Or evaluating the performance right.
So.
All these.
Processing methods typically do not change. No, typically, none of them changes the rankings.
Of stuff.
So you might have. You know you'll have a.
Re configuration of what is high, like the absolute values. But what is the highest value versus the lowest value is always gonna be.
The same for the variables that we're looking for in the readout.
So for for
I hope that makes some sense so.
yeah.
Okay. Thank you.
Thank you. Next, I have.
Okay? Sorry. I have a very silly question. So if I understand, can you confirm that is any batch corrections even needed on the test data. So, for example, if your test data is from the same batch in the future, so you don't apply any other batch to them.
In which case, I guess, whatever effective applying to the training data is only for the robust training and won't affect the test performance. Is this correct?
Yeah.
Yep, so so so there are 2, I think. Insights of this question. The 1st one is.
We're taking like a pre and post vaccination information data from the training data. All 3 cohorts on, and only the pre vaccination data.
Which is a challenge, data in basket effect correction, pipeline, which is just to make sure all data sets are consistent for the model building. But the the hidden data set, which is a challenge data set, is is completely out of this batch of it. Correction, pipeline.
Okay, that's really cool. So so I guess no additional.
Was applied to that Hayden challenge.
Okay.
6.
Thanks, Wing. Next I have Joan.
Thank you. I have a question specifically for the data itself.
So for the antibody titers I saw there were like, not only one iso types. There's several iso types in there.
When we consider about the
Like the the antibody response level.
Do you have any suggestions? If we should like, focus on.
Only, for example, the Ig. One.
Antibody levels. Or we should.
Be better to.
The the antibody levels for all the isle types.
Since we measure them all separately. So the Itg 1, 2, 3, and 4.
I would advise you to try to build a model with all those different measurements. We also have the total Itg, so.
That might cover your kind of average when when you're building a model.
I hope that answer.
So the total.
Yeah. So the total Ig should.
If that's like, you measure all the iso types together.
Or.
Hello!
Yeah, that's correct. Yes, and but these are all Antigen.
Great.
And Buren can elaborate on this.
Yeah, no. Just prima. What is the prediction task?
It's total.
I believe that is total. Ig, right?
Yeah.
So, and and that ultimately Jan.
We would love.
To have a model that can separately predict the different isotopes.
But with the current state. I mean, we found in the past that if you make it more complex and ask for these things.
It just confuses everyone.
So right now, the only thing that needs to be predicted is total. Igg.
No. At the same time.
You're gonna have all these other data, and I think immunologically, it would be super exciting.
To say, Okay, you can find.
Who's going to be high in Igg? 4.
Versus Igg. One.
So. But essentially, it's taken outside the context, we make all the data available. So you can. If those are questions that you are interested in.
Immunologically, which is, which are super exciting.
Yeah.
Go for it, but for the contest. It's a separate measurement. So the total Ig is not.
It's not like a sum.
It's Lisa, can you come? Confirm. So it's essentially a separate type of assay.
That just has like a general. Okay?
John, did that answer your question?
Did.
Oh, yeah, yeah, I think, yeah, for why? I asked, this is because I would want to, because the the prediction models, I'm thinking, is.
Because I I use the you know, some matter for the older version of the data, and it doesn't.
Really the good to predict. The Igg, like the.
Actually totally it level. So that's why I'm thinking also, if we.
Could do this prediction. For the individual
Isil types. Them.
So, yeah, well, we definitely want to try with the new.
Like the data. See what happened there.
Thank you.
Cool good luck.
Awesome. Thank you.
And then next we have Eve.
Hi, are there any sort of limitations on the originality.
Or you know the simplicity of the models that we submit to last year.
Absolutely not.
Please please duplicate yourself.
I mean no, no, that that would be the best right if you can.
If you perform well last time, and you submit the same one.
Absolutely. There's no need for originality. There's a need for performing well.
Thanks, Eve! And then Julie.
And I. I have a question regarding the baseline data.
And did you say there's like 3 measurements.
But we are given only one.
Baseline.
Data. So it's just how is it calculated.
I think.
Yeah, your question is about how many baseline values we are giving for training and test data.
So.
Yes. Did you give them individually, or do you give only one.
We are giving all 3 baseline data.
Okay. And then we decide how we want to use them.
Yeah, whether you're not like.
Take a median or or the the best way you you think is.
Okay. And how did people do? Maybe you mentioned that already.
Yeah.
Yeah, I.
I can see most people took day 0.
Directly, and wherever they find there are like missing values where there they took the.
Like help from the other baselines. So that was one method which I've seen. Yeah.
Most of time.
Okay. Thank you.
But actually, this is something we should maybe also have a separate post about on the on the Forum.
I mean.
The old data.
Which the the original training data didn't have multiple baselines.
And so at some point that has made it hard to kind of like, take the.
The nuance and how to deal with multiple baselines has is actually another interesting question that.
Yeah, I mean, I'm I'm hoping we can find out a.
A good way for the how to do it.
Thanks. Jul.
Mahida, I see a hand.
Yeah, I did. Regarding data. I remember that.
And there are we are! There are we are we on.
The the team and the.
Who told me I couldn't even.
And.
So yeah. Todd.
Yeah, after the.
Yeah, are we?
I I could put together.
Today, it's a.
It's that right?
Yeah, the like, the.
So, pramar, can you please repeat the question, cause? I'm also not sure if I completely got it.
I think I.
And make sure. Yeah.
I think I understood that all 3 vaccination baseline are they put together at day 0.
I think is is this is the question.
Right me, that.
Needed.
Okay, so.
So so like, we provide data for.
Day 0 day, minus 14, which is calculated from the day of vaccination and minus.
30 or 31, and we provide like sample labeling. And the day pre day related to the vaccination as as a concrete measurements.
Then it's it's up to you like whether you wanna pick just day 0 or you know.
Do the the average, or or whatever best way you think is to consider as a baseline for your models.
For prediction purposes.
Like.
They did.
I hope you answer your question otherwise. Yeah, we can always meet during the.
Open house session, which is coming next Monday to discuss one.
Doom.
Mahita any other last questions.
Nit.
After driving.
You have another question.
No, no.
Powder.
No other questions. I think we can stop it here.
So.
Yeah. Promot. If you can go next slide.
Yeah. So again, we'll have our office hour session on Monday. So if you can feel free to drop by and we'll send out the zoom information as well. But it's also available on the solution center. So.
Yeah, thank you all for joining today. And we look forward to seeing your submission. So thank you. Everyone.
Thank you. Folks. Thank you.
Thank you. Bye.