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Motivation. Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and…

Quantitative Methods · Quantitative Biology 2020-05-19 Nova F. Smedley , Suzie El-Saden , William Hsu

During evolution of microorganisms genomes underwork have different changes in their lengths, gene orders, and gene contents. Investigating these structural rearrangements helps to understand how genomes have been modified over time. Some…

Genomics · Quantitative Biology 2017-06-27 Huda Al-Nayyef , Christophe Guyeux , Marie Petitjean , Didier Hocquet , Jacques M. Bahi

How natural selection acts to limit the proliferation of transposable elements (TEs) in genomes has been of interest to evolutionary biologists for many years. To describe TE dynamics in populations, many previous studies have used models…

Populations and Evolution · Quantitative Biology 2014-03-04 Justin P. Blumenstiel , Xi Chen , Miaomiao He , Casey M. Bergman

Tumor is heterogeneous - a tumor sample usually consists of a set of subclones with distinct transcriptional profiles and potentially different degrees of aggressiveness and responses to drugs. Understanding tumor heterogeneity is therefore…

Applications · Statistics 2017-02-28 Fangzheng Xie , Mingyuan Zhou , Yanxun Xu

Background: Transposable elements (TEs) in eukaryote genomes are quantitatively the main components affecting genome size, structure and expression. The dynamics of their insertion and deletion depend on diverse factors varying in strength…

Genomics · Quantitative Biology 2013-11-26 Anna-Sophie Fiston-Lavier , Charles E. Vejnar , Hadi Quesneville

Understanding non-genetic determinants of cell fate is critical for developing and improving cancer therapies, as genetically identical cells can exhibit divergent outcomes under the same treatment conditions. In this work, we present a…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Florian Bürger , Martim Dias Gomes , Adrián E. Granada , Noémie Moreau , Katarzyna Bozek

In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voronoi-tessellated materials. We provide a concise overview of…

Materials Science · Physics 2023-03-17 Adam M. Krajewski , Jonathan W. Siegel , Jinchao Xu , Zi-Kui Liu

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. OED grading is subject to large inter/intra-rater variability, resulting in the under/over-treatment of patients. We…

Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external…

Machine Learning · Computer Science 2025-01-16 Hui Liu , Shikai Jin

Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric…

Materials Science · Physics 2021-08-11 Nicole Creange , Ondrej Dyck , Rama K. Vasudevan , Maxim Ziatdinov , Sergei V. Kalinin

In this work, we propose two novel methodologies to study temporal and morphological phenotypic effects caused by different experimental conditions using imaging data. As a proof of concept, we apply them to analyze drug effects in 2D…

Quantitative Methods · Quantitative Biology 2022-03-10 Andrei Dmitrenko , Mauro M. Masiero , Nicola Zamboni

The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application…

Materials Science · Physics 2022-08-12 Marcin Abram , Keith Burghardt , Greg Ver Steeg , Aram Galstyan , Remi Dingreville

We consider the problem of predicting gene expressions from DNA sequences. A key challenge of this task is to find the regulatory elements that control gene expressions. Here, we introduce Seq2Exp, a Sequence to Expression network…

Genomics · Quantitative Biology 2025-02-21 Xingyu Su , Haiyang Yu , Degui Zhi , Shuiwang Ji

The genes of eukaryotes are characterized by protein coding fragments, the exons, interrupted by introns, i.e. stretches of DNA which do not carry any useful information for the protein synthesis. We have analyzed the melting behavior of…

Biomolecules · Quantitative Biology 2009-11-10 Enrico Carlon , Mehdi Lejard Malki , Ralf Blossey

We propose a methodology for the identification of transcription factors involved in the deregulation of genes in tumoral cells. This strategy is based on the inference of a reference gene regulatory network that connects transcription…

Molecular Networks · Quantitative Biology 2020-04-20 Magali Champion , Julien Chiquet , Pierre Neuvial , Mohamed Elati , François Radvanyi , Etienne Birmelé

Motivation: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such…

Machine Learning · Computer Science 2024-02-12 Markus Wenzel , Erik Grüner , Nils Strodthoff

From a visual perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus. While existing…

Computer Vision and Pattern Recognition · Computer Science 2024-04-23 Yue Jiang , Zixin Guo , Hamed Rezazadegan Tavakoli , Luis A. Leiva , Antti Oulasvirta

Alternative cassette exons are known to originate from two processes exonization of intronic sequences and exon shuffling. Herein, we suggest an additional mechanism by which constitutively spliced exons become alternative cassette exons…

The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. Here, we propose using the Euler Characteristic Transform…

Machine Learning · Computer Science 2025-07-08 Victor Toscano-Duran , Florian Rottach , Bastian Rieck

This study presents an innovative approach to predicting VCSEL emission characteristics using transformer neural networks. We demonstrate how to modify the transformer neural network for applications in physics. Our model achieved high…

Disordered Systems and Neural Networks · Physics 2025-09-17 Aleksei V. Belonovskii , Elizaveta I. Girshova , Erkki Lähderanta , Mikhail Kaliteevski