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Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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    作为一种新的强化学习框架,CodonRL优化了mRNA序列,以提高翻译效率和稳定性. 它通过学习结构前置和实现用户控制的多目标权衡,优于现有方法.

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    科学领域:

    • 计算生物学是一种计算生物学.
    • 合成生物学 合成生物学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 优化mRNA序列的翻译效率,RNA稳定性和组合性质是复杂的,因为巨大的搜索空间和交互的目标.
    • 现有的方法,如动态编程,难以扩展,而深度生成模型需要广泛的训练数据,强化学习面临延迟奖励和大行动空间的挑战.

    研究的目的:

    • 开发一个强化学习框架,CodonRL,以实现高效和可定制的mRNA序列优化.
    • 解决现有方法的局限性,将结构先验纳入,并在推理过程中实现多目标权衡.

    主要方法:

    • 科登RL利用强化学习与高效的折叠反和示范引导的重复来学习mRNA设计的结构先验.
    • 它使用LinearFold在训练期间快速计算中间奖励,并使用ViennaRNA进行最终评估.
    • 基于里程碑的中间奖励和专家序列热身加速了融合,并在长期优化中处理延迟反.

    主要成果:

    • 与GEMORNA相比,CodonRL在55种人类蛋白质上表现优越.
    • 平均达到9.5%更高的编码子适应指数 (CAI),25.4千卡/mol更有利的最小自由能量 (MFE) 和3.4%更低的尿素含量.
    • 在匹配约束下,超过90%的基准蛋白质中改善了子稳定系数 (CSC).

    结论:

    • CodonRL提供了一个强大的框架来设计mRNA序列,以提高翻译效率,结构稳定性和降低免疫性.
    • 该方法允许在推断时进行持续的客观重量调整,在mRNA设计中提供灵活性.
    • CodonRL代表了计算mRNA设计的重大进步,特别是在训练数据稀缺的场景中.