Accurate prediction of RNA nucleotide interactions with backbone <i>k</i>-tree model

Ding, Liang; Xue, Xingran; LaMarca, Sal; Mohebbi, Mohammad; Samad, Abdul; Malmberg, Russell L.; Cai, Liming

doi:10.1093/bioinformatics/btv210

Search citation statements

Order By: Relevance

Paper Sections

Select...

Efficient Learning Of Optimal Markov Backbone K-trees1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2018

2022

Publication Types

Select...

Other1

Article1

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recurrence facilitates a dynamic programming algorithm to compute M for all entries of (k + 1)-clique κ and information α. It runs in time O(n k+2 ) and uses O(n k+1 ) memory which can be improved to O(n k+1 )-time and O(n k ) memory with a more careful implementation of the idea, e.g., by considering κ a k-clique instead of a (k + 1)-clique [15,16].…”

Section: Efficient Learning Of Optimal Markov Backbone K-treesmentioning

confidence: 99%

Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling

Ding,

Chang,

Malmberg

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree. Our current paper generalizes the result to Markov networks of tree width ≤ k, for every fixed k ≥ 2. In particular, we prove that approximation of a finite probabilistic system with such Markov networks has the minimum information loss when the network topology is achieved with a maximum spanning k-tree. While constructing a maximum spanning k-tree is intractable for even k = 2, we show that polynomial algorithms can be ensured by a sufficient condition accommodated by many meaningful applications. In particular, we prove an efficient algorithm for learning the optimal topology of higher order correlations among random variables that belong to an underlying linear structure.

show abstract

Section: Efficient Learning Of Optimal Markov Backbone K-treesmentioning

confidence: 99%