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To better understand DNA's 3D folding in cell nuclei, researchers developed chromosome capture methods such as Hi-C that measure the contact frequencies between all DNA segment pairs across the genome. As Hi-C data sets often are massive,…

Biological Physics · Physics 2023-11-14 Anton Holmgren , Dolores Bernenko , Ludvig Lizana

The principles underlying the architectural landscape of chromatin beyond the nucleosome level in living cells remains largely unknown despite its potential to play a role in mammalian gene regulation. We investigated the 3-dimensional…

Genomics · Quantitative Biology 2012-06-27 Ivan Junier , Ryan Dale , Chunhui Hou , François Képès , Ann Dean

Investigating the dynamics of chromatin and the factors that are affecting it, has provided valuable insights into the organization and functionality of the genome in the cell nucleus. We control the expression of Lamin-A, an important…

Biological Physics · Physics 2024-07-15 M. Hidalgo-Soria , Y. Haddad , E. Barkai , Y. Garini , S. Burov

Chromatin organization and dynamics is studied in this work at scales ranging from single nucleosome to nucleosomal array by using a unique combination of biochemical assays, single molecule imaging technique and numerical modeling. We…

Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works…

Machine Learning · Computer Science 2022-04-04 Kamilia Mullakaeva , Luca Cosmo , Anees Kazi , Seyed-Ahmad Ahmadi , Nassir Navab , Michael M. Bronstein

Epigenetic cell memory, the inheritance of gene expression patterns across subsequent cell divisions, is a critical property of multi-cellular organisms. In recent work [10], a subset of the authors observed in a simulation study how the…

Probability · Mathematics 2024-05-17 Simone Bruno , Felipe A. Campos , Yi Fu , Domitilla Del Vecchio , Ruth J. Williams

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data, due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of…

Machine Learning · Computer Science 2023-10-11 Manuel Madeira , Dorina Thanou , Pascal Frossard

Understanding the three-dimensional (3D) structure of the genome is essential for elucidating vital biological processes and their links to human disease. To determine how the genome folds within the nucleus, chromosome conformation capture…

Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accurately capturing the geometric information of biomolecules at…

Quantitative Methods · Quantitative Biology 2023-04-07 Xinye Xiong , Bingxin Zhou , Yu Guang Wang

We report on simultaneous recordings from cells in all layers of visual cortex and models developed to capture the higher order structure of population spiking activity. Specifically, we use Ising, Restricted Boltzmann Machine (RBM) and…

Neurons and Cognition · Quantitative Biology 2013-01-03 Urs Köster , Jascha Sohl-Dickstein , Charles M. Gray , Bruno A. Olshausen

Cis-regulatory modules (CRMs) composed of multiple transcription factor binding sites (TFBSs) control gene expression in eukaryotic genomes. Comparative genomic studies have shown that these regulatory elements are more conserved across…

Applications · Statistics 2007-09-14 Qing Zhou , Wing Hung Wong

A Restricted Boltzmann Machine (RBM) is an unsupervised machine-learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. As such, RBM were recently…

Machine Learning · Computer Science 2019-02-19 Jérôme Tubiana , Simona Cocco , Rémi Monasson

Focusing on the DNA-bridging nucleoid proteins Fis and H-NS, and integrating several independent experimental and bioinformatic data sources, we investigate the links between chromosomal spatial organization and global transcriptional…

Genomics · Quantitative Biology 2010-12-17 Vittore F. Scolari , Bruno Bassetti , Bianca Sclavi , Marco Cosentino Lagomarsino

As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular…

Machine Learning · Computer Science 2020-09-29 Zeren Shui , George Karypis

Bipartite networks are widely used to encode the ecological interactions. Being able to compare the organization of bipartite networks is a first step toward a better understanding of how environmental factors shape community structure and…

Machine Learning · Statistics 2025-12-02 Louis Lacoste , Pierre Barbillon , Sophie Donnet

The microstructure is an essential part of materials, storing the genes of materials and having a decisive influence on materials' physical and chemical properties. The material genetic engineering program aims to establish the relationship…

Machine Learning · Computer Science 2021-09-30 Chao Shu , Zhuoran Xin , Cheng Xie

Epigenetic landscapes, represented by patterns of chemical modifications on histone tails, are essential for maintaining cell identity and tissue homeostasis. These landscapes are shaped by multiple factors, including local biochemical…

Quantitative Methods · Quantitative Biology 2025-08-25 Daria Stepanova , Helen M. Byrne , Tomás Alarcón

Understanding the spatial organisation of the genome in the cell nucleus is one of the current grand challenges in biophysics. Certain biochemical -- or epigenetic -- marks that are deposited along the genome are thought to play an…

Soft Condensed Matter · Physics 2020-01-08 Davide Michieletto , Davide Colí , Davide Marenduzzo , Enzo Orlandini

Transcriptional activity has been shown to relate to the organization of chromosomes in the eukaryotic nucleus and in the bacterial nucleoid. In particular, highly transcribed genes, RNA polymerases and transcription factors gather into…

Biological Physics · Physics 2010-05-11 Ivan Junier , Olivier Martin , François Képès

It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions which might be dormant in a single source analysis. Moreover, different…

Methodology · Statistics 2021-12-08 Arnab Kumar Maity , Sang Chan Lee , Bani K. Mallick , Tapasree Roy Sarkar
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