Мероприятия Добавить мероприятие Спикеры Доклады Коллекции
 
Продолжительность 43:48
16+
Видео

Lightning Talks, Session #2

Kristyna Kupkova
Doctoral Student в University of Virginia
+ 5 докладчиков
  • Видео
  • Тезисы
  • Видео
BioC2020
30 июля 2020, Онлайн, USA
BioC2020
Запросить Q&A
BioC2020
Из видеозаписей конференции
BioC2020
Запросить Q&A
Видеозапись
Lightning Talks, Session #2
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
В избранное
111
Мне понравилось 0
Мне не понравилось 0
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
  • Описание
  • Расшифровка
  • Обсуждение

О докладе

Kristyna Kupkova (University of Virginia) PhD student

Sangram Keshari Sahu (Independent Bioinformatician)

Nathan Sheffield (University of Virginia) Assistant Professor

Antonio Colaprico (University of Miami Miller School of Medicine) Associate Scientist

Iguaracy Pinheiro-de-Sousa (University of São Paulo/EMBL-EBI) PhD Student

David Burton (University of Rochester, Dept. of Biostatistics) Doctoral Student

Aaron Chevalier (Boston University) Phd Candidate

1:00 PM - 1:55 PM EDT on Thursday, 30 July

TALK

Kristyna Kupkova: GenomicDistributions: Fast and convenient analysis of genomic intervals

Sangram Sahu: SigBio-Shiny: A standalone interactive application for detecting biological signifcance on a GeneSet

Nathan Sheffield: Refgenie: a reference genome resource manager

Antonio Colaprico: Interpreting pathways to discover cancer driver genes with Moonlight

Iguaracy Pinheiro-de-Sousa: Common Molecular Signature For Human Endothelial Dysfunction Associated With Abnormalities In Blood Flow, Lipids, Inflammation And Hypoxia

David Burton: Mixture Modeling for Genetic Regulatory Networks from Single Perturbation Gene Expression Experiments

Aaron Chevalier: Enhanced Deconvolution of Mutational Signatures

Moderator: Simone Bell

О спикерах

Kristyna Kupkova
Doctoral Student в University of Virginia
Sangram Keshari Sahu
Bioinformatics Solution Architect в Lifebit
Nathan Sheffield
Assistant Professor в University of Virginia
Antonio Colaprico
Senior Associate Scientist в University of Miami, Miller School of Medicine
David Burton
Biostatistics PhD. Student в University of Rochester School of Medicine and Dentistry
Aaron Chevalier
PhD. Student в Boston University

I am a computational biologist using data science approaches to study epigenomics of chronic child undernutrition.

Перейти в профиль

I work on statistical models for genomics data. Currently I am specializing in network models for genetic regulatory networks which includes software development in the Bioconductor environment for R.

Перейти в профиль
Поделиться

Hi everyone, my name is Christina. And I'm going to introduce you to our new package called General Distribution, which is basically this music tool box of Annalise visualize and to compare gentleman, gentleman and control. So that you might have we can go to the next. Why should you be any one? Should you be interested and gentleman distributions and Washington? Why should you use it? I can give you for a reason. First, as I said, it's a really rich tuel twang shins to

to describe genomic regions that. We really put it in any descriptive function that we could. We could think off into the us into this package. So everything is is one place, it's really easy to use and you don't have to go anywhere else. Second Weaver the package and a modular way where all the functions are separate. Into Coke functions that every person has is your data and plug functions that the does applauding. So once you have your input, your first running through the cult function and you you just distribution or your statistics inform us a table or a

list, what you can, then, easily, easily, pledge ourselves. This kind of offers you more flexibility, or if you want to do kind of quick and dirty analysis. First, you can plug in the plug in the output into our blood function, which then provides you a GP put objects, what you can of course in and I did also. So just kind of gives you this like double degree of flexibility of the more we all of the functions are taking hiders Luxury, Inn in form of genomic, ranges list, or if you want to purchase multiple, sure. He's at

one time. You just provides a jammock ranges. To the to the function and everything purchase purses for you in one single step. So you don't have to worry about writing for loose or values, apply functions etcetera. So it's a really easy for the more we wrote that we really put in effort into writing the code and everything is up to my first baby. So all of the calculations are done really fast so if you have large datasets I really do encourage you to use genomic distributions if we can cook go to the

next line here, I have to get examples of what gentleman distributions can do on. So if he has your agents of interest you can you can plug their distributions across different chromosomes. Do you can you can find if your regions are in an average for open chromatin signals in a given sell pipe. You can Find out a distance is from certain features. For example, transcription start side, or any future that you, that you might provide all you can find out if your Regents or in

GC, GC Richards, four regions of a genome. You can find the the distances between individual and triples, or if they are agents are enraged and certain partition. So are there, mostly in excellence for insurance or or sets around with this, I would like to encourage you to use genomic distributions. I provided her, a link to our GitHub repository, who miss miss that. I provide the link in the poster that I presented yesterday called genomic distribution. So you can take that. And if we can go to the next slide with this, I would like to thank all the people who

contributed to making to making this tool, making this package and who supported me through my page. Thank you for your attention and I guess this would be all for me. Thanks very much Christina. Again, how this is working, so we are playing, does light year. Each of the speakers in the session has 4 minutes to present. You can post your question to the conference platform under the poles and at the end of the session will have a Q&A together with all speakers. All right. So

now the next speaker and I hope that you pronounce your name, so I guess. My name is singing. This is how so, I'm sort of Bangalore India and just to go sign is it kind of like my pet project? I can say, I do out of my free time. So shiny application, you can do most of the stuff with it with a set of genes surgically. What genes that means and what are the available resources? And some of the ideas and we'll see cybelle's overview, which teacher and like, how I'm trying to collaborate with the time.

Neglecting. Yeah. So basically it since it is like, suppose for a typical transfer, can you get a set of differential, equations opened on Allegiant before they're taken into a different kind of punishment taste like, KFC gaoranger, a few meaningful biology at going for the dosage options. Correct. So I'm trying to do Knicks, latest. So there are some great resources available like you're empty BJP or enrich. Rog profiler a debit but they have like flight

problems with them. In some sense into your next light, please. Yeah, so if you can today, I just know you. So, basically, for any kind of thing we need an update resources rate, like adopter database, which of all the IDS mapping and everything, great, in some time, with passing time with requests for normal organisms as well. So most of the human mind of the recruiters waiting and that can be achieved in your database version of Fame and statistics sessions number of vibrations, you do while doing the engagement rings are giving a log of them and apart from Lake

View, wild story about David and everything you need in, which plot straight. So, yeah, I have some of the components but not all. So yeah, that's a drink that you were here. Mixlr please. So this is my sign is like completely depend upon by conductors open infrastructure resources. So it's basically using an addition have to getting the different ideas from different databases and then using some of the balcony tour packages on top of those resources to do that Alexis and

everything would think in Baytown goes so initially this can be launched from or directly from China server or it can be used to launch from your Arkansas with if you have a set of Gene Gene list or else along with JLS and your fortune, you can. He lost that? So and you need to provide a organism organism that you can make see how to get to getting updated and it converts different kind of Engagement. Looks like they didn't know what to think. Like it currently doing downloading database. Doing Gene mapping, ontology, enrichment, and part when

Richmond and few things are in working progress like getting ready, participle of different makes you like you did everything. Saying all the things that whatever you're doing done previously, through that, you ate those facing so it has to be a next time. I'm okay. I don't have much at 3. I think you want to do you want like 1 minute or so or got some fine woodwork? I can show them. This is how it can be loaded one, mostly I can throw the d. G y chocolate, how this work, and everything are you? So can you go to the last night?

I just want to forward the links. Yasso currently developing independently from your like my ideas too. If I can get some someone on board, I can grab it. Then we can make it in this table multiplication. Thank you very much syndrome or just a very interesting talk. Yes, it would be wonderful if you can, I post this links to the platform so people can join you in and collaborate together. Thanks very much to the speakers on the conference platform on their poles and it helps us all. So, if you, if you had the name of the speaker to say home, is this, your question address to

because we have seven speakers, so it is easier than for the Q. A Q & A session is a ride. The next speaker is Nathan, okay? So this is a tool which is designed to, to try to solve this problem that many of us have faced for for many years. And that is that many tools require Jean. Related assets, like a bowtie indexes are bwa indexes or annotations that the relate to a genome but we typically store these assets differently. So, I mean, we have a different folder structure, a different Labs, different individuals,

within a lab-based or these differently or different labs and they store them differently which leads to challenges with interoperability. Because if we're writing software that needs these kinds of assets, then we can't get to it. Exactly because they don't follow the same structure. So next time, please, so if we had instead of organization, then you could pass through a pipeline, just the name of the genome and it would sort of know where to find the different things that needed instead of the top live here instead of the second one, where we would need to pass

each thing independently. Because they sort of might be a different places and different peoples in Bayern. Doctor value of a standard organization. So next time please, so one solution to this is the eye genomes project. If you're not familiar with that, you can go check it out. And lumina has basically produced standard resources than at our ball that you can download. And then they all follow the same format. And now we can sort of all use one structure and you just say give me the eye genomes and then my stuff will work next time. But the problem with that is that it's not scripted. So

if you want to use a genome that that they don't have, well, you're out of luck, you could try to recreate it but that would be kind of tough. It's not modular. So you've got to download the entire thing, which is everything. They provide, you can just get through the one thing you need. All I needed was the bow tie. I'm at, it's not programmatic. There's no way to sort of explore the used by an API or anything out this place could just too far ball if you download the next time, Stretching his attempts to solve these limitations, with the command line tool. That provides two

ways to retrieve these assets. You can build the asset for any other interest. So, everything's scripted unlike IG. So that means I can recreate this environment for my Vikings, you no more, my non model organism, or whatever it is. If it also provides better discoverability + 9 / from this wreck wreck wreck in your server instance, which provides a plausible web interface and API to look at the different assets. And then the client allows us to obtain the local pass the asset really easily with this speak function. Next likeliest. So this is a simple example of

how it would work on a command line, you just would type R, S T pool and now you have the fast a file for a cheap. Or you could build something with Refugee build, once you got an ass that locally because you either pulled it or built it, then you use Refugee seek and that provides you the path to the objects. The rest in the middle interface where you now no longer have to worry about pads, all you have to do is have hg38 and then rescue team will be able to identify those paths on any system next light. So this is my last time. How would you do this from? Within our so, the rest you need

client package is written in Python and you can access it through our with the reticulate package. So, this is something that I'm sort of just getting into and trying to figure out how it works and what's the best way to wrap this up but essentially, you can just Call vscode like this and from, within our, you could pull assets from the server, or you can see can identify local. Pastor asked that all, from within our through this particular package. So I'm trying to explain the possibility of wrapping this up as a separate package and maybe making the interface a little bit

easier, from within our and I'd be interested in talking to anyone else if there. Last night was just a few Lakes. If you want to check it out please do so or contact me later. I'm done. Thank you very much. And miss Nathan, do you like to read this links? Also to one of these links you can get all the other things from one of them. The next speaker is Antonio I hope this is the correct pronunciation. So all right thanks I'm good. Thank you for this opportunity so I'm not going to

force the Vault. So yeah. So we made $1,019 If you know, my pulse is J bar links, like the package is. So what we want to do is to discover driving in Sri Lanka jeans and some espresso. So we can complete using a network of where Gene said, the increase the 35 degrees above and beyond country, where is Greece? State of Alaska division of Uncle Gene. And I also said you suck at cooking hamburger landscaper like to give weight of those Russell, so I'm just, at least on this

messaging. so what's with the score on the findings that so we discovered that ankle jeans, Espresso in instrumentation and when we find paper so we found Uncle jeans that are associated with open a chromatin. And instead we found associated with the open, we miss you. I'm a low as it goes like okay, the last life is for Rockin my Paw Patrol. We will. You said the same thing to serve papers identify station and also and Spring Station 2024, what color should thank you. Thanks very much Antonio, it was very good, thanks a lot.

So there, I am sure there will be questions and on the platform. Alright, the next speaker is you go to sleeping and you just Dozer from some Paulo. Here we go cuz I will present a bit more biology in bioinformatics but they like to get started and I'm going to present a part of my ADHD and thank you for the invitation what I'm trying to do, what's for supper, what is in the theater? This fact basically it's a condition that several of cardiovascular disease and some of the least a lot of respect for scam calls in with the description. So

basement that I went you to your database. Do you think part of the experiment that had this? Main influence factors that influence of the cheerleading sweater and we end up with 24. The basis that you can see here, the main side abdominal inflammation. So what we did was to do Chicken Shack specializes in and then in which one of the doctors pets and then we do the mathematics which one of this condition and we kept the genes that word Financial Express in all conditions

validation him. Come in here and can go next. You can go next week. Supposed to be realized that as we thought because they changed according to the confusion. But fortunately one of those change that's past 3 to Gene find the TV to Spanish. There was a problem in the geodatabase hypoxia inflammation. Most of these experiments were done in stock the conditions and then defeated cells are always on the floor condition under the blood flow to the penis sells. The blood flow to stimulate.

We set up a special on the couch is trash do this which is try to be like a good blood flow and they're still talks. She starts would be there and we combined here. If you can see it in all of them together, make shapes that success go to We're more open related to respiratory distress, around in the oldest Jack Canford. I weigh a more expressive. When you have faith, I will when Pizza is to my wife. And when we thought about The Human Condition, you can text everything

is happening at the same time. So we got a human tradition for $30. And we divided into three categories of lesions going for a minor obstruction to the major abstraction and we can see that some of those genius would actually increase expression according to the The degree of the address for the legion, so yes, we will make your database and we are able to, but the nicest thing is that some of those jeans were never associated with him first. So, yeah. Thank you. That necklace sets. I was

afraid of running out of time, but thank you. Thank you very much. You got to see. This was very nice, and also thanks for staying on time. I hope you're there or two questions for you later on. No, and it's the district's behind an update. We're making for the Turner. Net package. Some calling this mixture modeling for genetic regulatory networks from single perturbation gene, expression experiments slide, please make. So I'm super motivation here is that cancer genetics researchers, I believe that genes work together to

grow tumors. I one of our collaborators. Holy MacMurray was lead author on a paper where they felt they had sufficient evidence that the jeans were working so strongly together, that they should just call those jeans, their own group, or give him a new classification. That the gym class was working so strongly that it was affecting how they Define cancer. The next year, another group started looking at G networks from a systems perspective. As if the gym at work went into in the tractor and stayed there and that's what was causing cancer was part of

cancer existing in the state was that the gene Network just couldn't get out of there. So the thing is if we want to look at G networks as a network or using gene expression data, we kind of have to have some kind of a device or simplification to make that jump. Sometimes it's like, please make sure model overlaid on a some Delta. Delta, CT values from single perturbation gene, expression experiments and so does simplification are sumption? We want to make is that when the jeans are over, Express in response to the experiment, then we are presented with a uniform distribution in the

positive space. When they're under expressed, we represent them with a uniform distribution. Negative space is a normal distribution, the middle for genes, which have not been affected by the experiment. So if we use the simplification that open some new tools up to us And what that does is it allows us to then calculate probabilities of those gene expression values based on which network State we say that they're in, whether they're up down or if their Baseline and then we can calculate a likelihood for any possible Network fit. Given that data set, using

that tool than of the likelihood. We have a parallel tempering scheme, set up, that samples, possible networks from the space of all possible, networks for the 20 jeans that were looking at and then we can compare those Network fits the one another based on which ones score of the best likelihood slide please. What that allows us to do then is look at the top scoring networks and see how many times or What, proportion of those networks. Have the same gene relationships, the crane them until we get very strong as well. Sometimes, we're virtually all of the networks that were

scored. Well, show relationship. And sometimes not so strong at this graphic here. Just come Illustrate. Some of the output we're trying to get out with the new scoring method, and it's pretty much, it's like, please. So I just like to think my advisor. Matt McCall, the undergrads to work with me. Daniel Munoz. Worked, Michael Lansford, our collaborators and biomedical. Jen actually McMurray Heartland and our computers support got here Eastern. The thank you guys for your speaker is I don't really like

talking about it. I knew our package for Education, signature analysis and I'm going to talk really really briefly do some background about the package 7 walkthrough. I'm kind of an example or close you speak. So unlike someone else to use that are interested in the Cosmic dancer Princeton mutational, signatures are looking at the causes of mutation so whether their environmental exposures or normal human body and I'm suffering from smoking. Next five, benzoate Irene is a component of cigarette smoke that weed, that

knocks not random, mutations that specifically, ceeday transversions Moschino. That's specific signature, next slide. So, when we think about the smoke, we can actually do this as a probability distribution, across different types of mutations. So we see those c2a transversion about Bratayley but also within that different probabilities for different upstream and downstream mutations which is what that's broken into next week. So our package takes GPS map. Time tables

and count these mutations for sample. And from there we can we can be convolute these accounts into signatures and exposures the current we provide. I'm in a mess and I'll be a for doing their deconvolution. So in there, the middle last, we have the signatures and all right, we have her sample, we have the proportion of the exposure to different types of mutations. And then, we have these for comparing, two known signature sound. Cosmic Princeton. We can also

break these exposures down by annotation, for tumor type, and we can also. So, use the posterior implementation for predicting, exposures and new samples of this can be on a level or single sample. So, Support Solutions and also any custom annotation, you may have on a region in the, in the genome on a mutation. And we can also perform deconvolution for any kind of custom analysis in my want to do next. Please here's our kind of example analysis. So we've got our own is probably some patterns in here, but we can't really see what's going on and we have our

signatures on the right that we can go. Maybe guess what these are based on our prior knowledge is but you know as well as of this point, there's not much we know about it next time, please. So using one of our, one of our analysis plus, we can see that there's something strange. Happening here where our signatures one, two and four, have a tumor types of Highly represent on the signature of three is really just a Liars next life. Next time, if you want to wrap up, okay? So

there's just a lot of analyses that we can we can do for you man up as well. Just go to that the last line so we can see that there's just a few mutations that have very high mutation account next slide and we can see if we compare the signatures that the signature at 3 that we're seeing here. If you do, if you get the cosmic signature 10 which is is known for being a hyper mutator and found them, they were just refinanced things. We already know at this point so

if you could go to that the next slide, This is why I get up for the package. So we're working on a manuscript now and then Thank you so much dr. Pepper time do. That's fine. Thank you very much. You are very welcome to copy this link into the platform, so the people can also find it later today and tomorrow would be super nice for example. All right, so thanks very much to all speakers and the now we have time for questions. Thanks, Caleb. Let's have a look at the questions.

Your package by the facility to intersect multiple bed files to calculate the fraction of genomic. Feature overlaps, thank you for the question. Unfortunately. At this point, our package does not offer this feature but it is it is definitely an interesting suggestion, so we might actually consider consider including. So thank you. Thank you for the suggestion. Next question for Nathan, did you look at the bass basilisk package for interfacing with python? Yes, I just

was actually looking at it a couple of days ago and I sort of wreck and I'm resting up in both basilisk and reticulate. It seems to be like, from my just initial. Look at it, it seems like what basilisk is doing is essentially just wrapping reticulate so that you have a python that sort of embedded. So, it's really reticulate under the hood, is what it would be using with either basketball or reticulate. So, but so sure it would work of what bats looks good at is a self-contained

version of python, which you know, it just depends on if there's a And also for your niece and nephew need to use to pull from ncbi. Our ensembles gencode Illumina does not appear to have updated human gene models. In IG known since 2015, the lack of updates and I do know this was one of the reasons why we wanted to produce red genie on the lack of updates and also just didn't have the particular in a spike in control. Do you know, sweetie, do you think that any way to

answer the question directly in the chat now a link to the actual resources? Are you go browse? What's actually on there? I'm weakened if it fit, whatever I put on to the server is what you would be able to download and their hosted on S3 and an Amazon cassette program right now. But I can add, I guess whatever. So if there's resources on the other people think would be useful for the community that I haven't put in there. I can add them right now. The human genome, I think I'm an ncbi genome and we also have some

gencode and Ensemble annotations already on there. An idea for something I will, just let me know. Thanks for the question. The next question is for Ignacio. How did you score the severity of the lesions? Well, if you haven't yelled the looming of the pastures, and when you have a high address for, at least you, you probably reduced this. Looming of the vessel that we have the best tomorrow, I'll be strict. So we have a less degree. So you have like the whole

passel of Destruction two more? I'm Strasse. Probably have asked if you're at the restaurant eating, that's how we traded. Thanks for the couch. 7x questions for David. How do you infer the directionality of the arrows isn't the likelihood of both directions equivalent? Actually, the director of the area's comes from the network topology that we sample. I'm so I mentioned is very briefly, but there's a parallel tempering algorithm, we run that samples networks

and apparent notes. In the network that we're sampling are randomly generated. So we don't actually directly infer, which Gene is a parent to which other Jean so much is after we're given a scenario, we didn't use the likelihood to score whether or not it's doing well so to speak. as far as whether the likelihood of both directions are equivalent, Set up and where they're single perturbation experiments, so they jeans that were interested. One of them gets moved either up or down and is kept their of an

experimental condition. And then the other genes react to that Gene being forced to over Express runner express. So I wouldn't say that the likely to both directions is equivalent here. It's not, it's not exactly the same as if you're doing, like a, what is it cool to expression in the, in that case, I think maybe both directions are equivalent, but I think because of the experimental setup, that's not really true here. I hope that was sufficient. Next question is for Aaron. Can you say a bit more on the statistical model that you used to identify the signatures

of Matrix? Factorization, which is standard in the in the field and we also use latent dirichlet allocation which has been widely used for protection. Mining is starting to be starting to be a little more forthcoming in that space and also for predictions overtaking known signatures and predicting exposure using those. We use a l d a posterior and her for all of these you can you can you can get reproducible results. So thank you for the question. I had honestly, no idea about rain it was even ranked rainfall plot is so I was just like looking it up so early. It's

not part of the package but when I looked it up it's going to be like really that hard to implement so like I guess again thank you for the suggestion. I'm going to write it down so we can, we can maybe implemented in the, in the future. But at this point, it does not does not allow this option and the distance between between the features which are both which are both calculated without genomic distributions. So again like you can calculate these and then you can at this

point you can plug it, plug yourself. But thank you for the thank you for the suggestion. Oh wow. Thanks very much to everyone. Again, we still have time for discussions are there more questions from the participants? Yes, please. What I want to know what I'm doing is using any kind of house ology approach, which is that he's fighting I said, good question. We are not using yet. Those are important in my girl's name that they can have Uncle Johnny, but we are planning to do in the future

is female. My questions ideas suggestions, alright? And yeah. So thanks very much to all speakers again. Thanks, Tyler. Thanks for everyone to, for joining the session. We will close a couple of minutes earlier, so you have a longer break and before you join the, the next session I guess, have a nice day and I'll see you later today, and tomorrow, at the conference, Bye. Bye everyone.

Купить этот доклад

Доступ к видеозаписи доклада «Lightning Talks, Session #2»
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно

Ticket

Доступ к записям всех докладов «BioC2020»
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Билет

Интересуетесь тематикой «Наука и исследования»?

Возможно, вас заинтересуют видеозаписи с этого мероприятия

27-31 июля 2020
Онлайн
45
19,14 K
bioc2020, bioconductor , dna methylation, epidemiology, functional enrichment, human rna, probabilistic gene, public data resources, visualizations

Похожие доклады

Anthony Mammoliti
MSc Student в Princess Margaret Cancer Centre
+ 3 докладчика
Shraddha Pai
Data Science, Genomics в Ontario Institute for Cancer Research
+ 3 докладчика
Gabriel Odom
Assistant Professor в Florida International University
+ 3 докладчика
Ruth Schmidt
Data Scientist & Analyst в Nextgem
+ 3 докладчика
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Kayla Interdonato
Programmer/Analyst в Roswell Park Comprehensive Cancer Center
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Levi Waldron
Associate Professor в City University of New York
+ 2 докладчика
Sean Davis
Professor Of Medicine в University of Colorado Anschutz Medical Campus
+ 2 докладчика
Benjamin Haibe-Kains
Senior Scientist в Princess Margaret Cancer Centre, University Health Network
+ 2 докладчика
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно

Купить это видео

Видеозапись
Доступ к видеозаписи доклада «Lightning Talks, Session #2»
Доступно
В корзине
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно
Бесплатно

Conference Cast

ConferenceCast.tv — архив видеозаписей докладов и конференций.
С этим сервисом вы можете найти интересные лекции специально для вас!

Conference Cast
1497 конференций
47700 докладчиков
20185 часов контента
Kristyna Kupkova
Sangram Keshari Sahu
Nathan Sheffield
Antonio Colaprico
David Burton
Aaron Chevalier