Tvareta

MBA, Amrita School of Business

[tvareta]

Artificial Intelligence and Machine learning in Biotechnology:

Biotechnology has been a pillar for several activities but only since the pandemic its importance has grown to a much greater level. Biotechnology is generally the integration of biology and technology and is the process of using the biological organisms or substances or any biological agents which can be used in the preparation of any biological product or process which can be used for the benefit of the lives of the people, their health, environment, and their ecosystems. There are several streams of biotechnology such as drug delivery, plant, animal, food, chemical, etc., Even though the biotechnologists or the scientist's research and come up with any process or product, more amount of time is required by the people to derive it. In order to analyse, process, and interpret the product within a short time and with more efficiency, people began integrating with artificial intelligence and machine learning. On the pretext of COVID’19, the biotech industry has become popular and is also witnessing great demand on many fronts. AI is opening the way for a new generation of medical and biotech solutions that can outperform the best human minds in terms of analytical abilities. By combing both biotechnology and artificial intelligence we will be able to implement several methods in a very innovative way. On the other hand, they lead this field to several new possibilities. AI-based tools and applications are the major support system in the development of molecular structures based on the target market. Machine learning, a subset of artificial intelligence, aids in the calculation of permutations and combinations of bioactive components in order to determine the best

combination without the need for physical experimentation in the lab. Artificial intelligence is bringing improvements to biotechnology that can aid in predictive analysis to estimate the requirements of a particular drug or chemical on the market. Through the use of cloud computing, AI in Biotech can also assist in managing the allocation of raw materials required by the biotechnology industry.

Functions of Artificial Intelligence in the biotech industry:

•              Drugs and Vaccines Innovation

•              Faster Analysis of Healthcare data

•              Plant genome Studies

•              Global connection

Artificial intelligence and machine learning can be widely used in the field of drug discovery processes which comes under medicinal biotechnology. In the biotech industry, artificial intelligence and machine learning are widely used in fields such as clinical data management, illness prediction and prevention automation, and biomarker identification. Drugs and antibiotics are developed using living cells to improve human health. ML researches DNA and manipulates cells genetically to promote the production of critical and desirable traits. It helps in identifying the molecules which are known as the target component and also helps in diagnosing the diseases through visualizing the components. It can be used in radiation therapy which prevents further effects on the patients. Artificial Intelligence can be widely used in DNA sequencing from the data pool by grouping them into biological functions, and gene expressions and analyzing the protein structure, and the binding of the components, and predicting the protein structure which can be visualized virtually and then processed as a drug.

[tvareta]

Multiple Regression using R

Introduction:

Multiple regression is a branch of linear regression which can be used to analyse more than two variables. In multiple regression there is one response and more than one predictor variables whereas in linear regression where one response variable and one predictor variable. The predicator variables are the dependent variables and the response variable are the independent variables. Considering the equation for multiple regression,

Y=mx1+mx2+mx3=b

Where Y is the response variable

m1, m2, m3 are predictor variables

Let us discuss two problems regarding multiple regression

Analysis using R:

Multiple regression using R is one of the widely and often used method which is easy to use and handle.

DATA SET USED:

· https://github.com/grantgasser/Complete-Multiple-Regression

Using this dataset, we study of the relation between degree of brand liking (Y) and moisture content (X1) and sweetness (X2) of the product, the following results were obtained from the experiment based on a completely randomized design.

Some of the steps which we has to be followed are

1. Load and view the dataset

2. Identifying the data linearity in R

3. Plotting the graph

4. Implementation of Multiple Regression

5. Prediction and Interpretation

Brand Preference:

In a small-scale experimental study of the relation between degree of the brand liking (Y) and moisture content (XI) and sweetness (X2) of the product, the following results were obtained from the experiment based on a completely randomized design (data are coded:)

Analyzation of the data:

Scatter plot:

The diagnostic aids show that firstly, there are no outliers and the distribution for each variable is normal. Additionally, looking at the correlation matrix, Y and X1 have significant positive correlation, Y and X2 are positively correlated, but less so than Y and X1 and there’s no correlation between X1 and X2.

Correlation Matrix:

The correlation matrix of the variables is plotted to check the correlation between the variables.

Summary:

The value of multiple R- squared is 0.9521 and the adjusted R- squared value is 0.9447. When the variable X2, is added to X1 we get a p-value of about 2.01e-05. F- statistic variable is larger than 1. Y= 37.65 + 4.425X1 + 4.375X2 is the result of the regression model. Holding the other variables constant, increasing one unit of X1 results in a 4.425 rise in brand liking degree, while increasing one unit of X2 results in a 4.375 increase in brand liking degree. Because the P values for each variable are less than 0.05, both X1 and X2 are significant.

QQ plot:

In this QQ- plot the points plotted all fall in the same line which clearly determines that the residuals follow normal distribution. There are no outliers and errors .

Shapiro test:

Shapiro Wilk test is a statistic normality test for a random data set. It can be used to analyse if the data set is normally distributed. By analysing the values, we get,

Model validation:

Regression vs Residual Plot:

The above given plot is a residual plot which indicates a pattern in the residuals and the fitted plot. Although the distribution appears to be pretty normal, there are outliers on both sides of the median, with more outliers to the right.

Breusch-Pagan test:

This is test can be used to determine whether the heteroscedasticity is present in a regression analysis

Prediction and Confidence level:

newX = data. frame (X1=newX1, X2 = newX2)

#Confidence interval (95%)

predict (fit, newX, interval="confidence")

#Prediction Interval (95%)

predict (fit, newX, interval="prediction"

Output:

Interpretation:

From the above analysis, we get that the R- square is about 95% is very good and the results are accurate and the overall relationship is significant.