29 papers · ranked by Valyu relevance
Alisa J. Hamilton, Alexandra T. Strauss, Diego A. Martinez, Jeremiah S. Hinson + 3 more
'Jeremiah S. Hinson' 'Scott Levin' 'Gary Lin' 'Eili Y. Klein'] Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without…
Nghia Duong‐Trung, Stefan Born, Jong Woo Kim, Marie‐Therese Schermeyer + 8 more
'Marie‐Therese Schermeyer' 'Katharina Paulick' 'M. Borisyak' 'Ernesto Martı́nez' 'Mariano Nicolás Cruz Bournazou' 'Thorben Werner' 'Randolf Scholz' 'Lars Schmidt-Thieme' 'Peter Neubauer'] Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Gammerman, Alexander
This Inaugural Lecture was given at Royal Holloway University of London in 1996. It covers an introduction to machine learning and describes various theoretical advances and practical projects in the field. The Lecture here is presented in its original format, but a few remarks have been added in 2025 to reflect recent…
Jeff Calder, Reed Coil, Annie Melton, Peter J. Olver + 2 more
'Gilbert Tostevin' 'Katrina Yezzi-Woodley'] Abstract—Machine learning (ML), being now widely accessible to the research community at large, has fostered a proliferation of new and striking applications of these emergent mathematical techniques across a wide range of disciplines. In this paper, we will focus on a…
Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch + 25 more
'Marcin Płodzień' 'Kaelan Donatella' 'Kim A. Nicoli' 'Paolo Stornati' 'Rouven Koch' 'Miriam Büttner' 'Robert Okuła' 'Gorka Muñoz-Gil' 'Rodrigo A. Vargas–Hernández' 'Alba Cervera-Lierta' 'Juan Carrasquilla' 'Vedran Dunjko' 'Marylou Gabrié' 'Patrick Huembeli' 'Evert van Nieuwenburg' 'Filippo Vicentini' 'Lei Wang'…
Sandra Eloranta, Magnus Boman
The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted…
Narjice Chafai, Ichrak Hayah, Isidore Houaga, Bouabid Badaoui
The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal…
Yasunari Matsuzaka, Yoshihiro Uesawa, Huiyong Sun, Peichen Pan + 1 more
'Jingyu Zhu'] A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It…
Fang Zhengxin, Yi Yuan, Jingyu Zhang, Yue Liu + 7 more
'Qinghua Lu' 'Xu Xiwei' 'Wang Jeff' 'Wang Chen' 'Shuai Zhang' 'Shiping Chen'] Google AlphaGo's win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education).…
Farzaneh Tajidini, Mohammad-Javad Kheiri
- ML techniques have been widely used in the literature as fast, affordable, and non-invasive approaches for CAD prediction. - This paper conducts a comprehensive review of all relevant studies between 1992 and 2019 for ML-based Diagnostic using machine learning. - The impacts of dataset characteristics and applied ML…
Matthew J. K. Vince, Kristin A. Hughes, Anastasiya Buzuk, Deborah L. Perlstein + 2 more
Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training in fields such as statistics or cheminformatics. Increasingly, however…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…
Cheng Xu, Ling-Yun Zhao, Cun-Si Ye, Ke-Chen Xu + 1 more
With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of…
Alessia Nicosia, Nunzio Cancilla, José David Martín Guerrero, Ilenia Tinnirello + 2 more
Artificial Intelligence (AI) is transforming the healthcare field, offering innovative tools for improving the prediction, detection, and management of diseases. In nephrology, AI holds the potential to improve the diagnosis and treatment of kidney diseases, as well as the optimization of renal replacement therapies.…
Cohoon, Michael, Furman, Debbie
This paper details the machine learning (ML) journey of a group of people focused on software testing. It tells the story of how this group progressed through a ML workflow (similar to the CRISP-DM process). This workflow consists of the following steps and can be used by anyone applying ML techniques to a project…
Zhaoyu Zhai, Zhewei Lin, Qiang Li, Jianbo Pan
The explosive growth of numerical biomedical data poses a challenge in uncovering meaningful insights within from vast omics and clinical data. In recent years, machine learning has emerged as a powerful tool for processing and dissecting numerical biomedical data, making it a popular choice for addressing analytical…
David Kasperek, Michal Podpora, Aleksandra Kawala-Sterniuk, Cristinel Ababei + 2 more
'Cristinel Ababei' 'Henry Medeiros' 'Richard J. Povinelli'] In this paper, the authors have compared all of the currently available Apple MacBook Pro laptops, in terms of their usability for basic machine learning research applications (text-based, vision-based, tabular). The paper presents four tests/benchmarks…
Michal Bozděch
Not only in sports is a neural network the most used type of artificial intelligence. With software development, anyone can create a neural network model, but little is known about how to prepare the data and how to set up the model algorithms to their maximum performance. For these reasons, this study aims to…
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…
Gerardo Ibarra-Vazquez, María Soledad Ramírez-Montoya, Hugo Terashima
'Hugo Terashima'] This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Lucy Moctezuma, Lorena Benitez Rivera, Florentine van Nouhuijs, Faye Orcales + 4 more
This manuscript describes the development of a module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning” https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers…
Jorge Guerra Pires
Introduction: deep learning emerged in 2012 as one of the most important machine learning technologies, reducing image identification error from 25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learning models without coding expertise; 2) to…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Anubhav Jain
The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials…
Marvin van Aalst, Tim Nies, Tobias Pfennig, Anna Matuszyńska
Recent advances in artificial intelligence have accelerated the adoption of ML in biology, enabling powerful predictive models across diverse applications. However, in scientific research, the need for interpretability and mechanistic insight remains crucial. To address this, we introduce MxlPy, a Python package that…
Authors not listed
Here we evaluate the robustness and utility of quantum mechanical descriptors for machine learning with transition metal complexes. We utilize ab initio information from the quantum theory of atoms-in-molecules (QTAIM) for 60k transition metal complexes at multiple levels of theory (LOT), presented here in the tmQM+…