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Related papers: Toxicity Prediction by Multimodal Deep Learning

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Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accurate, available and inexpensive method to predict the…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Mohaddeseh Chegini , Ali Mahloojifar

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new…

Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of…

Machine Learning · Computer Science 2022-10-21 Meng Liu , Tamal K. Dey , David F. Gleich

Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modality, and complex relationships. While traditional models…

Materials Science · Physics 2025-09-22 Vahid Attari , Raymundo Arroyave

Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over…

Machine Learning · Statistics 2020-12-08 Javier Antorán , James Urquhart Allingham , José Miguel Hernández-Lobato

Deep learning methods are powerful tools in classifying multivariate time series data. Despite their high performance, these methods are hard to interpret, which diminishes their applications in high-risk domains such as healthcare. In this…

Machine Learning · Computer Science 2026-05-11 Bhavesh Kalisetti , Vincent Wang , Gaurav R. Ghosal , Maryam Bijanzadeh , Reza Abbasi-Asl

Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the…

Machine Learning · Computer Science 2021-06-11 Zhiliang Wu , Yinchong Yang , Jindong Gu , Volker Tresp

Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared representations from arbitrary modalities and…

Machine Learning · Computer Science 2022-07-06 Masahiro Suzuki , Yutaka Matsuo

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies…

Risk Management · Quantitative Finance 2019-11-19 Yaodong Yang , Alisa Kolesnikova , Stefan Lessmann , Tiejun Ma , Ming-Chien Sung , Johnnie E. V. Johnson

Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for…

Chemical Physics · Physics 2025-07-03 Daniel Julian , Jesús Pérez-Ríos

Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the…

Molecular Networks · Quantitative Biology 2018-03-20 Hector Zenil , Narsis A. Kiani , Ming-Mei Shang , Jesper Tegnér

The need for analysis of toxicity in new drug candidates and the requirement of doing it fast have asked the consideration of scientists towards the use of artificial intelligence tools to examine toxicity levels and to develop models to a…

Quantitative Methods · Quantitative Biology 2021-01-27 Mriganka Nath , Subhasish Goswami

Qualitative structure-activity relationship (QSAR) is important for drug discovery and offers valuable insights into the biological interactions of potential drug candidates. It has been demonstrated that QSAR can be accurately predicted by…

Quantum Physics · Physics 2025-01-24 Wei-Yin Chiang , Po-Yu Kao , Tzu-Lan Yeh , Ya-Chu Yang , Yen-Chu Lin , Alex Zhavoronkov

Visual scenes are composed of visual concepts and have the property of combinatorial explosion. An important reason for humans to efficiently learn from diverse visual scenes is the ability of compositional perception, and it is desirable…

Machine Learning · Computer Science 2023-06-16 Jinyang Yuan , Tonglin Chen , Bin Li , Xiangyang Xue

Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods suffer from scarcity of labeled data for particular tasks,…

Chemical Physics · Physics 2022-11-29 Xiang Gao , Weihao Gao , Wenzhi Xiao , Zhirui Wang , Chong Wang , Liang Xiang

The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict drug-protein interactions without…

Machine Learning · Computer Science 2020-12-22 QHwan Kim , Joon-Hyuk Ko , Sunghoon Kim , Nojun Park , Wonho Jhe

With recent advancements in the development of artificial intelligence applications using theories and algorithms in machine learning, many accurate models can be created to train and predict on given datasets. With the realization of the…

Machine Learning · Computer Science 2024-03-29 Pei Xi , Lin

We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The…

Artificial Intelligence · Computer Science 2017-09-13 Ivan Olier , Noureddin Sadawi , G. Richard Bickerton , Joaquin Vanschoren , Crina Grosan , Larisa Soldatova , Ross D. King

Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient…

Machine Learning · Computer Science 2023-08-08 Yancheng Liang , Jiajie Zhang , Hui Li , Xiaochen Liu , Yi Hu , Yong Wu , Jinyao Zhang , Yongyan Liu , Yi Wu

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where…

Machine Learning · Statistics 2019-02-06 Hakime Öztürk , Elif Ozkirimli , Arzucan Özgür