1. Discuss the flow of information stored in the DNA to protein.
2. Discuss Homology identification in biological sequence alignment. Differentiate
between Orthologous and Paralogous genes.
3. Descibe the procedure of protein s
tructure prediction through homology modeling.
4. Define Genome annotation. Explain its importance. Explain sequence
5. Explain similarity based approach to Gene prediction.
6. Briefly discuss the various methods of prote
in structure prediction
7. Discuss the method of ORF finding in genome sequence.
8. What is microarray gene expression and discuss the clustering of microarray data.
9. What do you mean by inference problems in molecular biology? Explain with a
10. Enumerate few key inference problems in molecular biology.
11. Describe any one method for protein structure prediction.
12. What do you mean by protein function prediction? How is it done, explain with a
13. What is drug disco
very? Explain various steps of drug discovery.
14. What is network identification, explain with suitable examples.
15. Briefly enumerate the various methods of protein structure prediction.
16. What do you understand by gene regulatory network? Explain wit
h a suitable
17. Discuss the current methods for genome sequencing and importance of genomic
1. Describe the advantage of clustering techniques in computational molecular biology.
2. Discuss the various application of regress
ion analysis in bioinformatics.
3. Explain the concept of dimensionality reduction and its utility in solving the molecular
4. Describe Regression and statistical inference methods applied in bioinformatics
5. What is Baye’s rule
? Explain Baye’s theorem applicable in biological system.
7. Explain applications of statistics in Bioinformatics.
8. Explain applications of probability and statistics in Bioinformatics.
9. Describe Regression and Significance testing methods applied in b
10. Explain the Baye’s rule application in biological sequence analysis.
11. Discuss the regression and significance testing, statistical inference methods applied
in bioinformatics analysis.
13. Define Computational induction tech
nique for density evaluation.
14. Differentiate between clustering and discrimination process.
15. What do you mean by dimensionality reduction?
1. What is artificial neural network? Discuss the application of feed forward back
propagation method in prote
in structure prediction
2. Discuss the application SVM in subunit vaccine design.
3. Discuss a machine learning algorihm used for optimal pairwise sequence alignment.
4. Descriobe the application and limitations of machine learning approaches.
5. Explain a
pplication of GA in Bioinformatics.
6. Discuss the concept of neural networks and their applications in computational
7. Briefly explain parametric tests, cross validation and empirical significance testing.
8. Enumerate various machine
learning approaches used in computational biology.
9. What is GA? Explain with suitable example.
10. What is artificial neural network? Explain the application of back propagation method
with suitable example.
11. What do you mean by supervised learning an
d AND DISCUSS the training of back
propagation ANN for classification task
12. What is GA? How it is applied to solve biological problems.
13. Define the evaluation parameter sensitivity, specificity, correlation coefficient and
14. Describe the
machine learning process with diagrammatic representation.
15. Discuss the neural network and SVM with example.
16. What do you mean by machine learning process and discuss the HMM model for
17. Discuss the application of SVM for classi
fication task with example.
18. Explain the ROC curve for cross
validation and significance testing.
1. What is biological bibliography database? Give an example of such a database.
2. Briefly discuss Chou
Fasman algorithm for secondary structure predicti
on of proteins
3. What do you understand by sensitivity analysis of a bioinformatics software tool.
4. What are differential equation simulators, explain with suitable examples
5. Explain Discrete and Hybrid simulation and application in molecular biology.
6. Give the name two most common computational operations performed in continuous
simulation with suitable examples.
7. Discuss how large biological documents are managed.
8. Explain different data mining methods.
9. What is Data mining? Explain data mini
ng applications in genomic sequences.
10. Describe the general simulation techniques applicable to biological problems
11. Explain continuous simulation in biological processes.
12. How management information is applied in Bioinformatics.
13. Describe the
simulation processes used in Bioinformatics.
14. Describe the retrieval system for a biological database.
15. Discuss the RDBMS techniques for biological database management.
16. Discuss the general modeling and simulation process applicable to most proble
Bioinformatics with flow chart.
17. Explain continuous simulation, discrete simulation and hybrid simulation.
18. Give the name of two most common computational operations performed in
continuous simulation and explain in briefly.
19. Define Data min
ing. Explain data mining operations in context of a larger knowledge
20. Describe the Monte Carlo process of simulation.
1. Discuss the procedure for microarray data analysis and various software tools used for
2. How Bioin
formatics can help in novel drug design? What is QSAR analysis?
3. What is Pharmacogenomics? Mention their applications in drug discovery.
4. Explain any one method for protein
ligand docking in drug designing.
5. Discuss the recent trends in computationa
l drug design.
6. Devise an algorithm to compute the number of distinct optimal alignments between a
pair of strings.
7. Suggest a computational strategy for Homology based 3
D protein structure modeling.
8. What do you mean by protein
ligand docking? Expl
ain with suitable example.
9. Discuss the recent trends in computational vaccine designing.
10. Discuss any one computational method for 3
D structure modeling.
11. Describe the importance of literature databases in Bioinformatics.
12. Define RMSD and disc
uss the structural alignment process in protein
13. Derive an algorithm to compute the number of distinct optimal alignment between a
pair of strings.