A Reinforcement Learning Approach for Solving the Fragment Assembly Problem

Bocicor, Maria-Iuliana; Czibula, Gabriela; Czibula, Istvan Gergely

doi:10.1109/synasc.2011.9

Cited by 19 publications

(29 citation statements)

References 20 publications

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“…Regarding the Q-learning approach presented in Subsection 5.1 and previously introduced in [6] for solving the DNA fragment assembly problem, we remark the following:…”

Section: Discussionmentioning

confidence: 99%

“…As we have shown in [6], a very large number of training episodes is required in order to obtain an accurate solution using the Qlearning approach presented in Subsection 5.1. That is why, in order to speed up the training process, we extend the proposed approach towards a distributed one, in which multiple cooperative agents learn to coordinate in order to find the optimal policy in their environment.…”

Section: Our Distributed Approach Proposalmentioning

confidence: 99%

“…The model proposed in this paper extends toward a distributed approach the reinforcement learning based model that we have previously introduced in [6] for solving the FA problem. To our knowledge, except for the ant [7] based approaches, the DNA Fragment Assembly problem has not been addressed in the literature using distributed reinforcement learning, so far.…”

Section: Introductionmentioning

confidence: 99%

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A Distributed Q-Learning Approach to Fragment Assembly

Bocicor¹,

Czibula²,

Czibula³

2011

SIC

Self Cite

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The process of DNA sequencing has nowadays become of great importance in basic biology research, as well as in various fields such as medicine, biotechnology or forensic biology. The fragment assembly problem is a very complex optimization problem that deals with sequencing of DNA, and many computational techniques including computational intelligence algorithms were designed for finding good solutions for this problem. Since DNA fragment assembly is a crucial part of any sequencing project, researchers are still focusing on developing better assemblers. We are introducing in this paper a distributed reinforcement learning based approach for solving the fragment assembly problem, an NP-complete optimization problem that attempts to reconstruct the original DNA sequence from a large number of fragments, each several hundred base-pairs long. Our model is based on a distributed Q-learning approach. The experimental evaluation of the proposed system has provided encouraging results, indicating the potential of our proposal. The advantages and drawbacks of the proposed approach are also emphasized.

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“…Regarding the Q-learning approach presented in Subsection 5.1 and previously introduced in [6] for solving the DNA fragment assembly problem, we remark the following:…”

Section: Discussionmentioning

confidence: 99%

Section: Our Distributed Approach Proposalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Distributed Q-Learning Approach to Fragment Assembly

Bocicor¹,

Czibula²,

Czibula³

2011

SIC

Self Cite

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“…Reinforcement learning can be applied in collective cell migration ( Hou et al., 2019 ), DNA fragment assembly ( Bocicor et al., 2012 ), and characterizing cell movement ( Wang et al., 2018 ). DNA fragment assembly is a technique that aims to reconstruct the original DNA sequence from a large number of fragments by determining the order in which the fragments have to be assembled back into the original DNA molecule, and it is also an NP-hard optimization problem.…”

Section: Current Trend In Principled DLmentioning

confidence: 99%

“…DNA fragment assembly is a technique that aims to reconstruct the original DNA sequence from a large number of fragments by determining the order in which the fragments have to be assembled back into the original DNA molecule, and it is also an NP-hard optimization problem. Bocicor et al. (2012) proposed a new reinforcement learning-based model for solving this problem.…”

Section: Current Trend In Principled DLmentioning

confidence: 99%

Modern deep learning in bioinformatics

Tian

et al. 2020

Journal of Molecular Cell Biology

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Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

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Deep Reinforcement Learning in Healthcare and Biomedical Research

Agrawal,

Mitra

2024

Deep Reinforcement Learning and Its Industrial Use Cases

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A Reinforcement Learning Approach for Solving the Fragment Assembly Problem

Cited by 19 publications

References 20 publications

A Distributed Q-Learning Approach to Fragment Assembly

A Distributed Q-Learning Approach to Fragment Assembly

Modern deep learning in bioinformatics

Deep Reinforcement Learning in Healthcare and Biomedical Research

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