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Related papers: Toxicity Prediction by Multimodal Deep Learning

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Learning deep representations to solve complex machine learning tasks has become the prominent trend in the past few years. Indeed, Deep Neural Networks are now the golden standard in domains as various as computer vision, natural language…

Machine Learning · Computer Science 2020-12-04 Vincent Gripon , Carlos Lassance , Ghouthi Boukli Hacene

One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have…

Machine Learning · Computer Science 2020-01-08 Işıksu Ekşioğlu , Mehmet Tan

We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is predominantly used to just predict a single number. This…

Machine Learning · Statistics 2020-04-02 Beate Sick , Torsten Hothorn , Oliver Dürr

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density…

Computational Physics · Physics 2019-08-20 Chengqiang Lu , Qi Liu , Chao Wang , Zhenya Huang , Peize Lin , Lixin He

Therapeutic antibody development has become an increasingly popular approach for drug development. To date, antibody therapeutics are largely developed using large scale experimental screens of antibody libraries containing hundreds of…

Quantitative Methods · Quantitative Biology 2022-10-07 Lin Li , Esther Gupta , John Spaeth , Leslie Shing , Tristan Bepler , Rajmonda Sulo Caceres

The quantitative structure-activity relationship (QSAR) regression model is a commonly used technique for predicting biological activities of compounds using their molecular descriptors. Predictions from QSAR models can help, for example,…

Biomolecules · Quantitative Biology 2023-04-04 Yuting Xu , Andy Liaw , Robert P. Sheridan , Vladimir Svetnik

Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistance. Therefore, the classification of MT images is of great…

Image and Video Processing · Electrical Eng. & Systems 2020-12-23 Aleksei Shpilman , Dmitry Boikiy , Marina Polyakova , Daniel Kudenko , Anton Burakov , Elena Nadezhdina

Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning is to create models that can process and link information using various modalities. Despite…

Computer Vision and Pattern Recognition · Computer Science 2021-05-25 Jabeen Summaira , Xi Li , Amin Muhammad Shoib , Songyuan Li , Jabbar Abdul

The optimal design of compounds through manipulating properties at the molecular level is often the key to considerable scientific advances and improved process systems performance. This paper highlights key trends, challenges, and…

Biomolecules · Quantitative Biology 2020-07-13 Abdulelah S. Alshehri , Rafiqul Gani , Fengqi You

For preventing youth suicide, social media platforms have received much attention from researchers. A few researches apply machine learning, or deep learning-based text classification approaches to classify social media posts containing…

Computation and Language · Computer Science 2022-06-20 Siqu Long , Rina Cabral , Josiah Poon , Soyeon Caren Han

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between…

Computational Physics · Physics 2021-01-29 Massimiliano Lupo Pasini , Ying Wai Li , Junqi Yin , Jiaxin Zhang , Kipton Barros , Markus Eisenbach

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian…

Molecular Networks · Quantitative Biology 2018-01-17 Narsis A. Kiani , Ming-Mei Shang , Hector Zenil , Jesper Tegnér

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the…

Accurately predicting chemical reactions is essential for driving innovation in synthetic chemistry, with broad applications in medicine, manufacturing, and agriculture. At the same time, reaction prediction is a complex problem which can…

Machine Learning · Computer Science 2025-04-23 Ryan J. Miller , Alexander E. Dashuta , Brayden Rudisill , David Van Vranken , Pierre Baldi

Dimensionality reduction is an effective method for learning high-dimensional data, which can provide better understanding of decision boundaries in human-readable low-dimensional subspace. Linear methods, such as principal component…

Machine Learning · Computer Science 2020-07-09 Koji Maruhashi , Heewon Park , Rui Yamaguchi , Satoru Miyano

The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have…

Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propose a novel and straightforward approach to estimate…

Machine Learning · Computer Science 2019-10-04 Tiago Ramalho , Miguel Miranda

Fusion-graded plasmas are one of the physically complex systems, resulting in continuous establishment of plasma theories for unclarified physical phenomena in order to thoroughly control nuclear fusion reactors. Deep learning has drawn…

Plasma Physics · Physics 2023-01-30 Semin Joung

Prediction of molecular properties, including physico-chemical properties, is a challenging task in chemistry. Herein we present a new state-of-the-art multitask prediction method based on existing graph neural network models. We have used…

Machine Learning · Computer Science 2019-10-31 Fabio Capela , Vincent Nouchi , Ruud Van Deursen , Igor V. Tetko , Guillaume Godin

In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions. Despite the rapid increase in the use of…

Chemical Physics · Physics 2019-05-17 Seongok Ryu , Yongchan Kwon , Woo Youn Kim
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