Using deep reinforcement learning approach for solving the multiple sequence alignment problem

Jafari, Reza; Javidi, Mohammad Masoud; Rafsanjani, Marjan Kuchaki

doi:10.1007/s42452-019-0611-4

Cited by 22 publications

(15 citation statements)

References 28 publications

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“…Yet many scoring-scheme agnostic pairwise and multiple alignment heuristics have been proposed recently, e.g. using reinforcement learning ( Jafari et al , 2019 ; Mircea et al , 2018 ; Ramakrishnan et al , 2018 ). This paves the way for the possible adoption of complex mutation models such as EvoLSTM.…”

Section: Discussionmentioning

confidence: 99%

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Lim

Blanchette

2020

Bioinformatics

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Motivation Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood. Results We introduce EvoLSTM, a recurrent neural network-based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence-to-sequence long short-term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long-range context dependencies in mutation probabilities. EvoLSTM brings modern machine-learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes. Availability and implementation Code and dataset are available at https://github.com/DongjoonLim/EvoLSTM. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Lim

Blanchette

2020

Bioinformatics

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“…The training process used the Q-learning algorithm. Another solution for this problem uses a deep RL algorithm and a long short-term memory network, introduced in [8]. Their experiments show, that this version not only outperforms canonical multiple sequence aligner tools but other RL approaches too.…”

Section: Related Workmentioning

confidence: 99%

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

Lipták

Kiss

2021

Studia UBB Informatica

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With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

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“…In recent years, ML methods have been developed to address the challenges faced by molecular evolution research, in particular, by overcoming the difficulties of analyzing increasingly massive sets of sequence and other omics data. Examples of such applications include the use of autoencoders to impute incomplete data for phylogenetic tree construction [ 7 ], application of random forest for phylogenetic model selection [ 8 ], harnessing convolutional neural networks (CNNs) to infer tree topologies [ 9 ] and tumor phylogeny [ 10 ], and utilization of deep reinforcement learning for the construction of robust alignments of many sequences [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Machine Learning into Established Bioinformatics Frameworks

Auslander

Gussow

Koonin

2021

IJMS

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The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

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Using deep reinforcement learning approach for solving the multiple sequence alignment problem

Cited by 22 publications

References 28 publications

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM

Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

Incorporating Machine Learning into Established Bioinformatics Frameworks

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