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Manhattan Tourist Algorithm. (V-U)
- Course:Advanced Computing Approaches (V-U)
- Discipline:Basic and Health Sciences
- Institute:Virtual University
- Instructor(s): Dr. Muhammad Nauman Aftab
- Level:Graduate
Advanced Computing Approaches (V-U)
- 4×4 Chess Board (V-U)
- A greedy approach to motif search (V-U)
- Agorithm (V-U)
- Algorithm and complexity-The Change Problem-I (V-U)
- Algorithm Design Techniques II (V-U)
- Algorithm Design Techniques I (V-U)
- Algorithm for Dynamic Programming (V-U)
- Algorithm for Search Trees I (V-U)
- Algorithm for Search Trees II (V-U)
- Algorithum Introducation (V-U)
- Algorithm-Introduction (V-U)
- Alignment. (V-U)
- Analysis using a linear model (V-U)
- Applications of Bioinformatics-I (V-U)
- Approximation Algorithms (V-U)
- Applications of Bioinformatics-II (V-U)
- Approximation Algorithms-II (V-U)
- Approximation Algorithms-III (V-U)
- Basics of R language. (V-U)
- Better Change Problem (V-U)
- Biological Algorithms versus Compute Algorithms-I (V-U)
- Big O notation (V-U)
- Biological Algorithms versus Computer Algorithms-II (V-U)
- Branch and bound algorithm. (V-U)
- Breakpoint Reversal Sort Algorithm. (V-U)
- Brute Force change (V-U)
- Brute Force Median search. (V-U)
- Brute Force VS. Greedy Algorithm (V-U)
- ByPass Algorithm (V-U)
- Calculation of weights-II (V-U)
- Calculation of weights-I (V-U)
- Calculations with vectors (V-U)
- Cell organelles-I (V-U)
- Central Dogma (V-U)
- Cell organelles-II (V-U)
- Change Problem Revisited (V-U)
- Changing colors and symbols (V-U)
- Checking Agilent data (V-U)
- Checking Affymetrix data (V-U)
- Checking Illumina data (V-U)
- Commands. (V-U)
- Correct vs. Incorrect Algorithm (V-U)
- Computational Protein Sequencing (V-U)
- DAG in daily life. (V-U)
- Data input and output (V-U)
- Differential expression and p-values (V-U)
- DNA Array (V-U)
- Directed Acyclic Graphs (V-U)
- DNA Sequencing I (V-U)
- DNA Sequencing II (V-U)
- DNA Structure (V-U)
- Dynamic Programming (V-U)
- DNA Transcription (V-U)
- Dynamic Programming Algorithm (V-U)
- Dynamic Programming I (V-U)
- Dynamic Programming II (V-U)
- Dynamic Programming-II (V-U)
- Dynamic Programming-I (V-U)
- Dynamic Programming1 (V-U)
- Dynamic Programming2 (V-U)
- Edit Distance. (V-U)
- Edit graph-II (V-U)
- Edit Graph. (V-U)
- Eulerian Cycle Problem (V-U)
- Exon Changing Algorithm (V-U)
- Exon chaining problem (V-U)
- Finding motif (V-U)
- Finding Optimal Number of Clustering (V-U)
- Fragment Assembly in DNA Sequencing (V-U)
- First Algorithm-Multiplication of integers (V-U)
- Gene Prediction I (V-U)
- Gene Prediction II (V-U)
- Gene set enrichment analysis for GO categories. (V-U)
- Genomic rearrangements (V-U)
- Gene set enrichment analysis for KEGG (V-U)
- Getting the reports (V-U)
- GO categories (V-U)
- Global sequence alignment (V-U)
- Graph (V-U)
- Graph Algorithm (V-U)
- Graph Theory (V-U)
- Graph Terminology (V-U)
- Graph Theory in Chemistry (V-U)
- Graphical settings (V-U)
- Graphs and Genetics (V-U)
- Graphical user Interfaces (V-U)
- Hamiltonian Cycle Problem (V-U)
- Heat map. (V-U)
- Histogram (V-U)
- History of Bioinformatics-II (After 2000) (V-U)
- History of Bioinformatics-I (Till 2000) (V-U)
- Illumina data (V-U)
- Improved breakpoint reversal sort algorithm (V-U)
- Installing extra packages (V-U)
- Interval Graphs (V-U)
- Introduction to Advanced Computing Approaches (V-U)
- Introduction to Bioinformatics Algorithm (V-U)
- Introduction- Gene set enrichment data. (V-U)
- Introduction- Annotation and clustering (V-U)
- K-mean Clustering (V-U)
- Karatsuba Algorithm (V-U)
- KEGG pathways (V-U)
- Local Alignment Problem (V-U)
- Local Sequence Alignment (V-U)
- Local Sequence Alignment II (V-U)
- Longest Common Sequences (V-U)
- Longest path in DAG problem. (V-U)
- Longest Common Sequences-II (V-U)
- Loops (V-U)
- Manhattan Tourist Algorithm. (V-U)
- Manhattan Tourist Problem. (V-U)
- Mathematical functions. (V-U)
- Mass Spectrophotometry (V-U)
- Model matrix for a three group comparison (V-U)
- Model matrix for a two group comparison (V-U)
- Modified Protein Identification Problem (V-U)
- Motif Finding Problem II (V-U)
- Motif Finding Problem I (V-U)
- Multiple Sequence Alignment (V-U)
- Next Vertex Algorithm-I (V-U)
- Normalizing Agilent data. (V-U)
- Normalizing Affymetrix data (V-U)
- Normalizing DNA microarray data. (V-U)
- Normalizing one and two color data. (V-U)
- Object types (V-U)
- One and two color data (V-U)
- PAM Matrix (V-U)
- One approach of Gene Prediction (V-U)
- Partial Digest Algorithm I (V-U)
- Partial Digest Algorithm II (V-U)
- Partial Digest Problem (V-U)
- Practical Restriction Mapping Algorithm (V-U)
- Plot (V-U)
- Profiles I (V-U)
- Profiles II (V-U)
- Profiles III (V-U)
- Progressive Multiple Alignment (V-U)
- Protein Sequencing and Identification (V-U)
- Protein Identification via Database Search (V-U)
- Protein Sequencing and Identification2 (V-U)
- Protein Translation (V-U)
- Reading one color data files (V-U)
- Pseudocode with simple example (V-U)
- Recurrence for LCS problem (V-U)
- Recurrence for LCS problem. (V-U)
- Recursive Algorithm-Application (V-U)
- Recursive Change Algorithm (V-U)
- Recursive Algorithm-Theory (V-U)
- Regulatory Motifs (V-U)
- Replication of DNA (V-U)
- Restriction Mapping I (V-U)
- Restriction Mapping II (V-U)
- Reversal distance problem (V-U)
- SBH as an Eulerian Path Problem (V-U)
- SBH as a Hamiltonian Path Problem (V-U)
- Scatter and Panel plot (V-U)
- Scoring Alignments-II (V-U)
- Search Trees-Best Alternative (V-U)
- Searching, merging and transposition (V-U)
- Search Trees-Introduction (V-U)
- Second Approach for Gene Prediction (V-U)
- Selection Sort (V-U)
- Sequence Similarity. (V-U)
- SEQUEST Algorithm (V-U)
- Sequencing by Hybridization (V-U)
- Shortest Path Problem (V-U)
- Shortest Superstring Problem (V-U)
- Similarity-Based Approaches to Gene Prediction (V-U)
- Similarity-Based Approaches to Gene Prediction I (V-U)
- Simple algorithms operations-II (V-U)
- Simple algorithms operations-I (V-U)
- Simple Motif Search Algorithm (V-U)
- Simple reversal sort algorithm (V-U)
- Sorting Algorithms-Classification (V-U)
- Sorting Algorithms-Example (V-U)
- Sorting and ordering (V-U)
- Sorting by reversals (V-U)
- Spectrum Graphs I (V-U)
- Spectrum Graphs (V-U)
- Statistical Analysis (V-U)
- Statistical Approach to Gene Prediction I (V-U)
- Statistical Approach to Gene Prediction II (V-U)
- Strategy for Sequencing (V-U)
- Statistical Approach to Gene Prediction III (V-U)
- Structure of RNA (V-U)
- The Change Problem-II (V-U)
- The Peptide Sequencing Problem (V-U)
- The power of DNA sequence comparison. (V-U)
- The Peptide Sequencing Problem II (V-U)
- Theorem Permutation II (V-U)
- Theorem permutation-I (V-U)
- Tower of Hanoi (V-U)
- Weight of the paths (V-U)
- Types of Graphs (V-U)