medical-10

Multiple Sequence Alignment

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in multiple-sequence-alignment-10

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OXBench

BAliBASE v3

SABMark v1.65

Libraries

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Use these libraries to find multiple-sequence-alignment-10 models and implementations

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Most implemented papers

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

Linyong Nan, Iddo Drori, D. Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Wei Fu, Sashank Karri, A. Kannan, A. Moretti, Mohammed Alquraishi, C. Keasar, I. Pe’er•Fri Nov 08 2019

This work demonstrates state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles, and creates a new gold standard dataset of proteins which is comprehensive and easy to use.

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Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation

S. Sun, Le Zhang, Jiayang Chen, Tao Shen, Yu Li•Thu Jun 01 2023

A novel generative language model is introduced, MSA-Augmenter, which leverages protein-specific attention mechanisms and large-scale MSAs to generate useful, novel protein sequences not currently found in databases, which supplement shallow MSAs, enhancing the accuracy of structural property predictions.

8 0

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Phylogenetic automata, pruning, and multiple alignment

Oscar Westesson, G. Lunter, B. Paten, I. Holmes•Mon Mar 21 2011

This work presents an extension of Felsenstein's algorithm to indel models defined on entire sequences, without the need to condition on one multiple alignment, using a hierarchical stochastic approximation technique which makes this algorithm tractable for alignment analyses of reasonable size.

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ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning

Wenwu Zeng, Dafeng Lv, Wenjuan Liu, Shaoliang Peng•Thu Nov 30 2023

Experimental results on benchmark data sets demonstrate that the prediction performance of ESM2 feature representation comprehensively outperforms evolutionary information-based hidden Markov model (HMM) features.

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Ultra-large alignments using phylogeny-aware profiles

Nam-phuong Nguyen, S. Mirarab, Keerthana Kumar, T. Warnow•Sat Apr 04 2015

UPP is presented, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences.

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QuickProbs 2: Towards rapid construction of high-quality alignments of large protein families

Adam Gudyś, Sebastian Deorowicz•Tue Dec 22 2015

The presented algorithm has similar execution times to ClustalΩ, and is orders of magnitude faster than full consistency approaches, like MSAProbs or PicXAA, which make QuickProbs 2 an excellent tool for aligning families ranging from few, to hundreds of proteins.

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Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Sepp Hochreiter, Johannes Brandstetter, Matthias Dorfer, Vihang Patil, M. Hofmarcher, Marius-Constantin Dinu, P. Blies, J. Arjona-Medina•Mon Sep 28 2020

Align-RUDDER is introduced, which is RUDDER with two major modifications, which replaces RUDder's LSTM model by a profile model that is obtained from multiple sequence alignment of demonstrations, which considerably reduces the delay of rewards, thus speeding up learning.

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DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences

Mengmeng Kuang, Yong Liu, Lufei Gao•Wed Nov 18 2020

This paper trained a decision-making model based on the convolutional neural networks and bi-directional long short term memory networks, and progressively aligned the input protein sequences by calculating different posterior probability matrices to improve the accuracy of progressive multiple protein sequence alignment method.

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MSA Transformer

P. Abbeel, Joshua Meier, J. Canny, Roshan Rao, Jason Liu, Robert Verkuil, Tom Sercu, Alexander Rives•Fri Feb 12 2021

A protein language model which takes as input a set of sequences in the form of a multiple sequence alignment and is trained with a variant of the masked language modeling objective across many protein families surpasses current state-of-the-art unsupervised structure learning methods by a wide margin.

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