00:01First of all, I am going to tell you what we are doing in the workshop.
00:05This is a 5-day workshop. Today is the fourth day.
00:09We have many children from university and other states.
00:15We have candidates from Uttarakhand, Madhya Pradesh,
00:22and other colleges.
00:24We want to give them a facility so that they are the basic experiments of biotechnology.
00:31We want to give them a hands-on training.
00:37What will they do in this training?
00:38They will give exposure to what they can do in this field.
00:44After bioinformatics, we have a whole slot here.
00:49Our biotechnology is not just experimental-based subject.
00:54Bioinformatics is a very important part.
00:56We have genetic screening or gene sequencing.
01:02We know that we have genes in our body.
01:08We can have genes in our body.
01:13We can direct them to the disease.
01:16If we have sequencing, we can know about it.
01:18We can know about it.
01:20We can change our lifestyle like this.
01:23We also have a very important role in bioinformatics.
01:27In plants, there are many diseases.
01:31There are many types of diseases.
01:34Which is beneficial?
01:36Which is harmful?
01:37Which is harmful?
01:38Which is harmful?
01:38Which is harmful?
01:40When you can do a lot of research.
01:41When you process the bioinformatics, you have been prepared for more data.
01:47In order to process that data, we have time for the first time.
02:10biological data use and results from that we are making inferences from that
02:17biological data, like if you are talking about medical science in the field, then patient's data
02:22patient's data, like if he is using x-ray, you will have an image, you will have a picture
02:27now what is the work of artificial intelligence and machine learning?
02:31that he will analyze all the images or pictures and tell the patterns
02:38and he will tell you that this is what I am showing
02:43so now you need to train it, it is a machine learning
02:46so the name is that you will learn before the machine
02:49so there is a data set which is the doctors or experts
02:55that will put the image that if the hand of the hand is broken
03:00then it will go into this category
03:02so it will go into this category
03:04in which you will give the result
03:07so you will put a stack of the picture
03:10and put it in one place and say
03:12this is my result
03:13that if you have this image, this is the result
03:17this is the result
03:17this is the result
03:18and this is the suggestion
03:20suppose that you have 100 patients
03:22which the result has manually checked
03:25and you will tell them
03:26so you will have this
03:27then you will have this
03:27then you will have this
03:28now with that 100 results
03:29and with that 100 patients
03:31the machine will learn from you
03:33that now
03:35there will be some new data
03:36that is
03:37that you have another patient
03:38that has a heart data
03:39that has a heart data
03:39that has a x-ray report
03:40rather than
03:41that you will have
03:42as a technician
03:44so what is the technician
03:45that will do
03:45in the software
03:45load the image
03:48so the machine learning
03:49that we need to follow
03:50and take a look at
03:52that it will be
03:52and take a look at
03:55that it is
03:56that it is similar
03:57as this type of
03:58engineering
03:59which we have to follow
04:00this type of procedure
04:01next we have to follow
04:02this type of procedure
04:02we have to follow
04:03how many times
04:03we have to take a look at
04:047 days
04:04to 12 days
04:05or 2 months
04:05so this is the form of
04:10machine learning
04:11is transforming
04:12such as I have told
04:13that the gene expression
04:14and all these things
04:15So in plants there are problems, such as the uthpadan, because our population is a lot, so the uthpadan should
04:22be more.
04:22As you can see, Russia and Ukraine are running more, so there is a food crisis.
04:29So for the food crisis, we need to build uthpadan.
04:32Now there is a conventional method, such as people cross hybrid seams.
04:38But slowly, slowly, slowly, slowly, slowly, we are not able to reach that level.
04:42We do not know how many players to build uthpadan.
04:48These players can be proteins, genes, repeat elements, epigenetics.
04:57So these are many players.
04:59If you study each layer, in bioinformatics, there is a application for this.
05:05If you study the genome, it is called Genome Sequencing.
05:09If you look at gene expression, it is a transcript.
05:11There is a protein for protein.
05:13There is a metabolite for metabolites.
05:15So these are different layers.
05:16Now, if you sequence all these layers, it is called GBs or TBs.
05:22So these are the results of the data.
05:23Then you will process that data.
05:25So what is machine learning?
05:27It is automatically automatically automatically.
05:29Like you are running 20 or 25 software,
05:33then you will get the raw data from the end result.
05:35So you automatically automatically,
05:37or approach faster data,
05:39and quickly analyze it.
05:40So first, you have to analyze it.
05:42It takes 6 months, 1-1 year to the researchers.
05:44Now it has so much automated,
05:47and it has so much passed to AI,
05:48that you will have the end result in 2 weeks or 3 weeks.
05:51So this is how machine learning and AI
05:53is transforming the field of bioinformatics.
05:56And bioinformatics is such a domain
05:57that is now with every subject.
06:00Like I have given you an image example.
06:03So if you use plant identification,
06:06which is called taxonomy,
06:07which is called a subject,
06:09then it can also be used there.
06:10It can also be used as a subject.
06:11If there is a senior researcher
06:14or a scientist,
06:15who has all these classifieds in their life.
06:19This plant is called this plant.
06:20It has photographs.
06:22So it has a database.
06:24And if you or I have a plant,
06:26or if you have a plant on the roadside,
06:28we have put a photo picture in it,
06:30and it will tell you that
06:32it is your plant.
06:33So bioinformatics is changing
06:36each and every domain of the science.
