数据驱动的饮食配方优化,以提高日本 (Anguilla japonica) 幼虫的生存和生长
Kazuharu Nomura1, Tadao Jinbo2, Hirofumi Furuita1
1Fisheries Technology Institute Nansei Field Station, Japan Fisheries Research and Education Agency, Nakatsuhamaura, Minamiise, Watarai, Mie, Japan.
PloS one
|February 12, 2026
概括
我们使用贝叶斯优化开发了一个人工智能驱动的框架,以创建日本 (Anguilla japonica) 的最佳幼虫饮食. 这种方法显著提高了年轻鱼的生存率和生长率.
科学领域:
- 水产养殖营养水产养殖营养
- 机器学习在生物学中的应用
- 数据驱动的料优化
背景情况:
- 日本 (Anguilla japonica) 的人工种苗生产受到低于最佳的幼虫饮食的阻碍.
- 传统的启发式饮食配方方法缺乏有效性.
- 开发有效的,数据驱动的方法对于水产养殖的进步至关重要.
研究的目的:
- 实施一个人类在循环中的优化框架,以改进日本幼虫的食.
- 使用贝叶斯优化 (BO) 和高斯过程回归 (GPR) 系统地提高生存率和总长度 (TL).
- 通过综合建模方法加速优质幼虫配方的开发.
主要方法:
- 采用一个数据驱动的框架,结合贝叶斯优化 (BO) 和高斯过程回归 (GPR).
- 进行了八次连续养试验,通过专家的意见反复调整了七个关键的饮食成分.
- 利用随机森林和夏普利添加式扩展 (SHAP) 进行饮食-绩效关系的机械建模.
主要成果:
- 优化的饮食实现了显著更高的生存率 (74.9%),相比对照 (30.7%) 在化后100天 (dph).
- 在优化饮食中,平均总长度 (TL) 增加到40.1毫米,而在对照组中为35.2毫米 (p < 0.05).
- 机械模型确定了特定阶段的营养平衡对于增强生存和生长至关重要.
结论:
- 这种以数据为导向,人为循环的框架成功地优化了日本的幼虫饮食.
- 这种方法显著提高了鱼的生存和生长,为水产养殖料创新提供了一个实用的模板.
- 这是BO首次用于优化日本的幼虫料的应用,证明了其广泛的适用性.
相关概念视频
Censoring Survival Data
568
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
568
Optimal Foraging
14.0K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.0K
Survival Curves
745
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
745
Survival Tree
437
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
437
Introduction To Survival Analysis
840
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
840
Comparing the Survival Analysis of Two or More Groups
618
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
618


