arXiv:1103.4347
공식 코드 · Papers with Code
Phylogenetic automata, pruning, and multiple alignment
원문 초록
We present an extension of Felsenstein's algorithm to indel models defined on entire sequences, without the need to condition on one multiple alignment. The algorithm makes use of a generalization from probabilistic substitution matrices to weighted finite-state transducers. Our approach may equivalently be viewed as a probabilistic formulation of progressive multiple sequence alignment, using partial-order graphs to represent ensemble profiles of ancestral sequences. We present a hierarchical stochastic approximation technique which makes this algorithm tractable for alignment analyses of reasonable size.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
PEvoLM: Protein Sequence Evolutionary Information Language Model
PoET: A generative model of protein families as sequences-of-sequences
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.