A multi-agent system with reinforcement learning agents for biomedical text mining

Camara, Michael; Bonham-Carter, Oliver; Jumadinova, Janyl

doi:10.1145/2808719.2812596

Cited by 16 publications

(3 citation statements)

References 16 publications

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“…To estimate the number of NCBI articles, we applied the text mining analysis implemented in [21,22], which served to locate all article abstracts containing relevant keywords: protein names, PTM types and other words for syntax.…”

Section: Organismal Protein Samplesmentioning

confidence: 99%

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

Bonham-Carter

Thapa²,

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et al. 2016

Brief Bioinform

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Post-translational modifications (PTMs) are important steps in the biosynthesis of proteins. Aside from their integral contributions to protein development, i.e. perform specialized proteolytic cleavage of regulatory subunits, the covalent addition of functional groups of proteins or the degradation of entire proteins, PTMs are also involved in enabling proteins to withstand and recover from temporary environmental stresses (heat shock, microgravity and many others). The literature supports evidence of thousands of recently discovered PTMs, many of which may likely contribute similarly (perhaps, even, interchangeably) to protein stress response. Although there are many PTM actors on the biological stage, our study determines that these PTMs are generally cast into organism-specific, preferential roles. In this work, we study the PTM compositions across the mitochondrial (Mt) and non-Mt proteomes of 11 diverse organisms to illustrate that each organism appears to have a unique list of PTMs, and an equally unique list of PTM-associated residue reaction sites (RSs), where PTMs interact with protein.Despite the present limitation of available PTM data across different species, we apply existing and current protein data to illustrate particular organismal biases. We explore the relative frequencies of observed PTMs, the RSs and general amino-acid compositions of Mt and non-Mt proteomes. We apply these data to create networks and heatmaps to illustrate the evidence of bias. We show that the number of PTMs and RSs appears to grow along with organismal complexity, which may imply that environmental stress could play a role in this bias.

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Section: Organismal Protein Samplesmentioning

confidence: 99%

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

Bonham-Carter

Thapa²,

From

et al. 2016

Brief Bioinform

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show abstract

“…Перспективным решением в создании современных интеллектуальных информационных систем являются системы с мультиагентной архитектурой. Многие распределенные информационные системы интеллектуального анализа данных строятся с использованием архитектуры на базе мультиагентных систем [2][3][4][5]. Мультиагентная система -система, состоящая из двух и более агентов, которые взаимодействуют друг с другом для достижения поставленных перед ними целей [6].…”

Section: Introductionunclassified

Development of a multi-agent intelligent system for solving problems of classification and ranking of materials on the Internet

et al. 2021

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The article proposes an architectural solution for intelligent information systems based on a multi-agent approach and microservice architecture. An example of using the described solution in the development of an automated intelligent search system that solves the problem of thematic classification and ranking of various sources of information on the Internet is given. The advantages and disadvantages of the developed architectural solution are described.

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“…Multi-Agent Reinforcement Learning (MARL) is a useful approach for executing multi-agent cooperation tasks, such as multi-robot cooperation and traffic signal control [1][2][3][4][5][6]. However, MARL has difficulty with deriving good performance because the behavior of the agents are too complex to allow for cooperation with other agents.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Reinforcement Learning Methods with Different Goal Selections in Multi-Agent Cooperation

Uwano

Takadama

2017

JACIII

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This study discusses important factors for zero communication, multi-agent cooperation by comparing different modified reinforcement learning methods. The two learning methods used for comparison were assigned different goal selections for multi-agent cooperation tasks. The first method is called Profit Minimizing Reinforcement Learning (PMRL); it forces agents to learn how to reach the farthest goal, and then the agent closest to the goal is directed to the goal. The second method is called Yielding Action Reinforcement Learning (YARL); it forces agents to learn through a Q-learning process, and if the agents have a conflict, the agent that is closest to the goal learns to reach the next closest goal. To compare the two methods, we designed experiments by adjusting the following maze factors: (1) the location of the start point and goal; (2) the number of agents; and (3) the size of maze. The intensive simulations performed on the maze problem for the agent cooperation task revealed that the two methods successfully enabled the agents to exhibit cooperative behavior, even if the size of the maze and the number of agents change. The PMRL mechanism always enables the agents to learn cooperative behavior, whereas the YARL mechanism makes the agents learn cooperative behavior over a small number of learning iterations. In zero communication, multi-agent cooperation, it is important that only agents that have a conflict cooperate with each other.

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A multi-agent system with reinforcement learning agents for biomedical text mining

Cited by 16 publications

References 16 publications

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

A study of bias and increasing organismal complexity from their post-translational modifications and reaction site interplays

Development of a multi-agent intelligent system for solving problems of classification and ranking of materials on the Internet

Comparison Between Reinforcement Learning Methods with Different Goal Selections in Multi-Agent Cooperation

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