Alzheimer's disease has an increasing prevalence in the population
world-wide, yet current diagnostic methods based on recommended biomarkers are
only available in specialized clinics. Due to these circumstances, Alzheimer's
disease is usually diagnosed late, which contrasts with the currently available
treatment options that are only effective for patients at an early stage.
Blood-based biomarkers could fill in the gap of easily accessible and low-cost
methods for early diagnosis of the disease. In particular, immune-based
blood-biomarkers might be a promising option, given the recently discovered
cross-talk of immune cells of the central nervous system with those in the
peripheral immune system. With the help of machine learning algorithms and
mechanistic modeling approaches, such as agent-based modeling, an in-depth
analysis of the simulation of cell dynamics is possible as well as of
high-dimensional omics resources indicative of pathway signaling changes. Here,
we give a background on advances in research on brain-immune system cross-talk
in Alzheimer's disease and review recent machine learning and mechanistic
modeling approaches which leverage modern omics technologies for blood-based
immune system-related biomarker discovery.

阿尔茨海默病在全球范围内的患病率不断增加，然而目前基于推荐生物标记的诊断方法只在专科诊所中提供。基于血液的生物标记物可以作为早期诊断该疾病的易获得、低成本的方法。本文回顾了阿尔茨海默病中脑免疫系统相互作用的研究进展，概述了利用现代组学技术探索血液免疫系统相关生物标记物的机器学习和机械建模方法。

早期诊断阿尔茨海默病：免疫相关血液生物标志物和计算建模方法的进展

Towards early diagnosis of Alzheimer's disease: Advances in  immune-related blood biomarkers and computational modeling approaches

A good theory of mathematical beauty is more practical than any current
observation, as new predictions of physical reality can be verified
self-consistently. This belief applies to the current status of understanding
deep neural networks including large language models and even the biological
intelligence. Toy models provide a metaphor of physical reality, allowing
mathematically formulating that reality (i.e., the so-called theory), which can
be updated as more conjectures are justified or refuted. One does not need to
pack all details into a model, but rather, more abstract models are
constructed, as complex systems like brains or deep networks have many sloppy
dimensions but much less stiff dimensions that strongly impact macroscopic
observables. This kind of bottom-up mechanistic modeling is still promising in
the modern era of understanding the natural or artificial intelligence. Here,
we shed light on eight challenges in developing theory of intelligence
following this theoretical paradigm.

通过理论模型的建立，我们可以更好地理解大型神经网络和人工智能等领域的现象和行为，依此探讨人工智能理论中的八大挑战。

智能理论研发中的八大挑战

Eight challenges in developing theory of intelligence

This report documents the programme and the outcomes of Dagstuhl Seminar
22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic
Modelling". Today's scientific challenges are characterised by complexity.
Interconnected natural, technological, and human systems are influenced by
forces acting across time- and spatial-scales, resulting in complex
interactions and emergent behaviours. Understanding these phenomena -- and
leveraging scientific advances to deliver innovative solutions to improve
society's health, wealth, and well-being -- requires new ways of analysing
complex systems. The transformative potential of AI stems from its widespread
applicability across disciplines, and will only be achieved through integration
across research domains. AI for science is a rendezvous point. It brings
together expertise from $\mathrm{AI}$ and application domains; combines
modelling knowledge with engineering know-how; and relies on collaboration
across disciplines and between humans and machines. Alongside technical
advances, the next wave of progress in the field will come from building a
community of machine learning researchers, domain experts, citizen scientists,
and engineers working together to design and deploy effective AI tools. This
report summarises the discussions from the seminar and provides a roadmap to
suggest how different communities can collaborate to deliver a new wave of
progress in AI and its application for scientific discovery.

本报告总结了 Dagstuhl Seminar 22382“科学中的机器学习：桥接数据驱动和机理建模” 的讨论并提出了如何协作来推动人工智能和科学发现的新一波进展的路线图。

科学中的人工智能：一个新兴议程

AI for Science: An Emerging Agenda

Breiman's classic paper casts data analysis as a choice between two cultures:
data modelers and algorithmic modelers. Stated broadly, data modelers use
simple, interpretable models with well-understood theoretical properties to
analyze data. Algorithmic modelers prioritize predictive accuracy and use more
flexible function approximations to analyze data. This dichotomy overlooks a
third set of models $-$ mechanistic models derived from scientific theories
(e.g., ODE/SDE simulators). Mechanistic models encode application-specific
scientific knowledge about the data. And while these categories represent
extreme points in model space, modern computational and algorithmic tools
enable us to interpolate between these points, producing flexible,
interpretable, and scientifically-informed hybrids that can enjoy accurate and
robust predictions, and resolve issues with data analysis that Breiman
describes, such as the Rashomon effect and Occam's dilemma. Challenges still
remain in finding an appropriate point in model space, with many choices on how
to compose model components and the degree to which each component informs
inferences.

该论文提出数据分析涉及数据建模和算法建模两种文化之间的选择，而机械模型则提供了一种有效的解决方案，可以生产灵活、可解释、科学的混合模型，以获得准确和稳健的预测，并且可以解决数据分析中的一些挑战。