25 papers · ranked by Valyu relevance
Fabrizio Riguzzi, Kristian Kersting, Marco Lippi, Sriraam Natarajan
Statistical Relational Artificial Intelligence (StarAI) aims at integrating logical (or relational) AI with probabilistic (or statistical) AI (De Raedt et al., ; Riguzzi, ). Relational AI achieved impressive results in structured machine learning and data mining, especially in bio- and chemo-informatics. Statistical AI…
Seyed Mehran Kazemi, David Poole
The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation…
Vaishak Belle
In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order ) logic to represent (probabilistic) knowledge in a non-technical fashion. Our motivation is three fold. First, for machine learning researchers unaware of why the research community cares about…
Chengchun Shi, Wenbin Lu, Rui Song
Statistical relational learning is primarily concerned with learning and inferring relationships between entities in large-scale knowledge graphs. [19] proposed a RESCAL tensor factorization model for statistical relational learning, which achieves better or at least comparable results on common benchmark data sets…
Bahare Fatemi, Seyed Mehran Kazemi, David Poole
Relational logistic regression (RLR) is a representation of conditional probability in terms of weighted formulae for modelling multi-relational data. In this paper, we develop a learning algorithm for RLR models. Learning an RLR model from data consists of two steps: 1- learning the set of formulae to be used in the…
David C. Poole
Artificial intelligence seems to be taking over the world with systems that model pixels, words, and phonemes. The world is arguably made up, not of pixels, words, and phonemes but of entities (objects, things, including events) with properties and relations among them. Surely we should model these, not the perception…
Ernest Fokoué
The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theoretical and methodological core of AI is, and has always been, statistical. This paper systematically argues that the field of…
Robert Johansson
Arbitrarily Applicable Relational Responding (AARR) is a cornerstone of human language and reasoning, referring to the learned ability to relate symbols in flexible, context-dependent ways. In this paper, we present a novel theoretical approach for modeling AARR within an artificial intelligence framework using the…
Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational…
Rupsa Saha, Ole‐Christoffer Granmo, Vladimir Zadorozhny, Morten Goodwin
'Morten Goodwin'] Abstract—TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by…
Akihiro Nomura, Masahiro Noguchi, Mitsuhiro Kometani, Kenji Furukawa + 1 more
'Takashi Yoneda'] Purpose of Review Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied…
Peifeng Ruan, Ismael Villanueva-Miranda, Jialiang Liu, Donghan M. Yang + 3 more
Sample size and power analysis are essential in biomedical research and investigations, particularly in clinical trial design, as they ensure sufficient statistical power to detect meaningful effects. However, the complexity of these calculations often requires specialized statistical expertise, making the process…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Yikai Zheng, Wanquan Liu, Bi Zeng, Yichun Feng + 3 more
Biomedical interactions are inherently dynamic, often shifting or even reversing under specific physiological states. However, existing extraction methods simplify these complex mechanisms into context-agnostic binary associations, resulting in semantic loss and contradictory evidence. Here, we present AutoBioKG, an…
Yichun Feng, Lu Zhou, Yikai Zheng, Ruikun He + 2 more
In recent years, Large Language Models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. We developed the Knowledge Graph-based Thought (KGT) framework, an innovative solution…
Yili Hong, Jiayi Lian, Li Xu, Jie Min + 3 more
'Xinwei Deng'] Artificial intelligence (AI) systems have become increasingly popular in many areas. Nevertheless, AI technologies are still in their developing stages, and many issues need to be addressed. Among those, the reliability of AI systems needs to be demonstrated so that the AI systems can be used with…
Livia Faes, Dawn A. Sim, Maarten van Smeden, Ulrike Held + 2 more
We are witnessing a tremendous increase in scientific studies in the medical literature using Artificial Intelligence (AI) and its branch Machine Learning (ML) methods in particular. A recent systematic review comparing the classification performance of healthcare professionals vs. AI retrieved over 20,000 records of…
Joshua J. Levy, A. James O’Malley
Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Trang T. Lê, Ryan J. Urbanowicz, Jason H. Moore, Brett A. McKinney
Relief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a…
Authors not listed
This article proposes a three-level classification of artificial intelligence (AI) application in chemical sciences, reflecting the increasing degree of technology involvement in scientific and production processes: from automation of routine tasks (the level of "AI Assistant"), to the creation of specialized…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Ricardo Stefani
The use of data science, artificial intelligence, and big data in the field of chemistry has recently grown to speed up the discovery of new materials, drugs, and synthetic substances and the identification of automated compounds. Machine learning and data science are commonly used in organic chemistry to predict…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…