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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Minimum length RNA folding trajectories
Amir H. Bayegan, Peter Clote · 2018-02-18 · via cs.DS updates on arXiv.org

The Kinfold and KFOLD programs for RNA folding kinetics implement the Gillespie algorithm to generate stochastic folding trajectories from an initial structure s to a target structure t, in which each intermediate secondary structure is obtained from its predecessor by the addition, removal or shift of a single base pair. Define MS2 distance between secondary structures s and t to be the minimum path length to refold s to t, where a move from MS2 is applied in each step. We describe algorithms to compute the shortest MS2 folding trajectory between any two given RNA secondary structures. These algorithms include an optimal integer programming (IP) algorithm, an accurate and efficient near-optimal algorithm, a greedy algorithm, a branch-and-bound algorithm, and an optimal algorithm if one allows intermediate structures to contain pseudoknots. Our optimal IP [resp. near-optimal IP] algorithm maximizes [resp. approximately maximizes] the number of shifts and minimizes [resp. approximately minimizes] the number of base pair additions and removals by applying integer programming to (essentially) solve the minimum feedback vertex set (FVS) problem for the RNA conflict digraph, then applies topological sort to tether subtrajectories into the final optimal folding trajectory. We prove NP-hardness of the problem to determine the minimum barrier energy over all possible MS2 folding pathways, and conjecture that computing the MS2 distance between arbitrary secondary structures is NP-hard. Since our optimal IP algorithm relies on the FVS, known to be NP-complete for arbitrary digraphs, we compare the family of RNA conflict digraphs with the following classes of digraphs (planar, reducible flow graph, Eulerian, and tournament) for which FVS is known to be either polynomial time computable or NP-hard. Source code available at http://bioinformatics.bc.edu/clotelab/MS2distance/.