Opening the paper…
Algorithmic Thinking in Bioinformatics, Quiz 2
Consider a univariate Gaussian Mixture Model (GMM) with 3 components. Suppose for component 2, you are given the following data points and corresponding responsibilities. Which of the following options correctly represents the updated mean ?
| 3 | 0.1 |
| 7 | 0.7 |
| 10 | 0.6 |
| 5 | 0.4 |
| 8 | 0.8 |
Consider a univariate Gaussian Mixture Model (GMM) with 3 components. Suppose for component 2, you are given the following data points and corresponding responsibilities. Which of the following options correctly represents the updated mean $\mu_2$? | $x_i$ | $r_{i,2}$ | |---|---| | 3 | 0.1 | | 7 | 0.7 | | 10 | 0.6 | | 5 | 0.4 | | 8 | 0.8 | Figure from the original question paper Given below are three algorithms for the motif finding problem. Identify which figure belongs to which algorithm. ████████ randomly select k-mers Motifs = (Motif_1, ..., Motif_t) in each string from Dna BestMotifs ← Motifs for j ← 1 to N i ← RANDOM(t) Profile ← profile matrix formed from all strings in Motifs except for Motif_i Motif_i ← Profile-randomly generated k-mer in the i-th sequence if SCORE(Motifs) < SCORE(BestMotifs) BestMotifs ← Motifs return BestMotifs (a) Figure I ████████ BestMotifs ← motif matrix formed by first k-mers in each string from Dna for each k-mer Motif in the first string from Dna Motif_1 ← Motif for i = 2 to t form Profile from motifs Motif_1, ..., Motif_{i-1} Motif_i ← Profile-most probable k-mer in the i-th string in Dna Motifs ← (Motif_1, ..., Motif_t) if SCORE(Motifs) < SCORE(BestMotifs) BestMotifs ← Motifs return BestMotifs (b) Figure II ████████ randomly select k-mers Motifs = (Motif_1, ..., Motif_t) in each string from Dna BestMotifs ← Motifs while forever Profile ← PROFILE(Motifs) Motifs ← MOTIFS(Profile, Dna) if SCORE(Motifs) < SCORE(BestMotifs) BestMotifs ← Motifs else return BestMotifs (c) Figure III