Bioinformatics

1. Algorithms in Bioinformatics
   1. Dynamic programming
      1. Needleman-Wunsch algorithm
         1. Overview and purpose
         2. Global sequence alignment
         3. Matrix filling technique
         4. Traceback method
         5. Applications and limitations
      2. Smith-Waterman algorithm
         1. Overview and purpose
         2. Local sequence alignment
         3. Scoring systems
         4. Traceback method
         5. Applications and efficiency improvements
   2. Hidden Markov Models (HMMs)
      1. Fundamentals of HMMs
         1. States and transitions
         2. Emission probabilities
         3. Transition probabilities
      2. Gene prediction
         1. Applications in microbial genomics
         2. Exon and intron recognition
         3. Implementation and challenges
      3. Protein domain identification
         1. Pfam and other domain databases
         2. Statistical significance and validation
         3. Integration with sequence data
   3. Graph theory applications
      1. Basics of graph theory
         1. Nodes and edges
         2. Directed vs. undirected graphs
         3. Graph traversal algorithms
      2. Pathway analysis
         1. Metabolic networks
         2. Signal transduction pathways
         3. Shortest path algorithms and their applications
      3. Network biology
         1. Protein-protein interaction networks
         2. Gene regulatory networks
         3. Community detection and modularity
   4. Machine learning algorithms
      1. Clustering
         1. k-means clustering
            1. Distance metrics
            2. Initialization techniques
            3. Applications in gene expression analysis
         2. Hierarchical clustering
            1. Methods of linkage
            2. Dendrogram construction
            3. Use cases in phylogenetics
      2. Classification
         1. Support Vector Machines (SVM)
            1. Kernel functions
            2. Hyperplane classification
            3. Applications in phenotype prediction
         2. Neural networks
            1. Deep learning architectures
            2. Backpropagation and optimization
            3. Bioinformatics applications in structural prediction
      3. Regression models
         1. Linear regression
            1. Assumptions and applications in genomics
            2. Interpretation and significance testing
         2. Logistic regression
            1. Binary and multinomial outcomes
            2. Applications in disease classification
         3. Advanced regression techniques
            1. Ridge and Lasso regression
            2. Applications in multi-omics data analysis
            3. Integrative analysis across datasets
   5. Other computational algorithms
      1. String matching algorithms
         1. Boyer-Moore and Knuth-Morris-Pratt
         2. Applications in sequence motif identification
      2. Bayesian methods
         1. Bayesian networks
         2. Applications in probabilistic gene prediction
         3. Comparative approaches to other probabilistic models
      3. Genetic algorithms
         1. Basics of genetic algorithms
         2. Applications in sequence alignment and motif discovery
         3. Evolutionary models and adaptation in bioinformatics
   6. Practical considerations
      1. Algorithm performance and complexity
         1. Time and space complexity analysis
         2. Parallelization and optimization strategies
      2. Software tools and frameworks
         1. Bioinformatics programming libraries
         2. Integration with high-performance computing platforms
      3. Case studies and real-world applications
         1. Genome-wide association studies (GWAS)
         2. Cancer genomics and precision medicine applications
         3. Novel insights from algorithm-driven research initiatives