Featured
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| Open AccessDevelopmental progression of DNA double-strand break repair deciphered by a single-allele resolution mutation classifier
DNA double-strand breaks (DSBs) are repaired by a hierarchically regulated network of pathways. Here, authors develop ICP for deciphering somatic DSB repair patterns in multicellular organisms and discover developmental regulation in flies and mosquitoes, enabling tracking of mutant alleles and interhomolog copying of gene cassettes.
- Zhiqian Li
- , Lang You
- & Ethan Bier
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Article
| Open AccessAllele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells
Quantifying the impact of copy-number alterations (CNAs) on gene expression at the subclone level in cancer remains a challenge. Here, the authors develop TreeAlign, a method that integrates sample-matched single-cell DNA and RNA sequencing data to infer the impact of CNAs on subclonal gene expression.
- Hongyu Shi
- , Marc J. Williams
- & Sohrab P. Shah
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Article
| Open AccessBalancing competing effects of tissue growth and cytoskeletal regulation during Drosophila wing disc development
The authors integrate computational and quantitative approaches to elucidate how organ shape arises through the interplay between multiple growth pathways through regulation of both proliferation and the cytoskeleton.
- Nilay Kumar
- , Jennifer Rangel Ambriz
- & Mark Alber
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Article
| Open AccessThe bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease
Here, the authors develop a hybrid agent-based model to quantify the contributions of intrinsic cellular mechanisms and bone ecosystem factors to therapy resistance in multiple myeloma. They show that intrinsic mechanisms are essential for resistance, and that the bone microenvironment provides a protective niche that increases the likelihood.
- Ryan T. Bishop
- , Anna K. Miller
- & David Basanta
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Article
| Open AccessLocal prediction-learning in high-dimensional spaces enables neural networks to plan
The task of planning a sequence of actions, and dynamically adjusting the plan in dependence of unforeseen circumstances, remains challenging for artificial intelligence frameworks. The authors introduce a learning approach inspired by cognitive functions, that demonstrates high flexibility and generalization capability in planning tasks, suitable for on-chip learning.
- Christoph Stöckl
- , Yukun Yang
- & Wolfgang Maass
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| Open AccessThe decline of the 2022 Italian mpox epidemic: Role of behavior changes and control strategies
Mpox cases in Italy rapidly declined following a peak in summer 2022. Here, the authors investigate potential reasons for the decline in cases using an individual-based model of a sexual contact network of men who have sex with men.
- Giorgio Guzzetta
- , Valentina Marziano
- & Stefano Merler
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Article
| Open AccessCharting cellular differentiation trajectories with Ricci flow
When stem cells develop into tissues intracellular signalling is rewired, errors in this process lead to cancer. Here, authors applied tools from differential geometry made by Albert Einstein’s General Relativity to understand and predict biological network rewiring in health and disease.
- Anthony Baptista
- , Ben D. MacArthur
- & Christopher R. S. Banerji
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Article
| Open AccessDrivers and impact of the early silent invasion of SARS-CoV-2 Alpha
The SARS-CoV-2 Alpha variant of concern emerged in the UK in late 2020 but spread internationally before it was detected. Here, the authors reconstruct the dynamics of dissemination of this variant out of the UK by combining extent of genomic sequencing, travel volume, and local epidemic dynamics in a Bayesian model.
- Benjamin Faucher
- , Chiara E. Sabbatini
- & Chiara Poletto
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Article
| Open AccessMachine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions
Here the authors report a machine learning model, PSPire, which integrates both residue-level and structure-level features and outperforms tools in identifying phase-separating proteins lacking intrinsically disordered regions.
- Shuang Hou
- , Jiaojiao Hu
- & Yong Zhang
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Article
| Open AccessDeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning
Picking particles of biological macromolecules is critical for solving their structures in situ using cryo-electron tomograms. Here, authors develop DeepETPicker, a deep learning-based tool for fast, accurate, and automated picking of three-dimensional particles.
- Guole Liu
- , Tongxin Niu
- & Ge Yang
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Article
| Open AccessDeep learning model for personalized prediction of positive MRSA culture using time-series electronic health records
Identification of patients at high risk of methicillin-resistant Staphylococcus aureus (MRSA) infection could improve treatment outcomes by optimising antimicrobial therapy. Here the authors develop a deep learning model that uses electronic health record data from the United States to predict MRSA culture positivity.
- Masayuki Nigo
- , Laila Rasmy
- & Degui Zhi
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Article
| Open AccessA coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits
Competition for the host cell’s resources influences synthetic biology circuit behavior. Here the authors present an E. coli cell model that combines insights into bacterial resource allocation with a simplified model of competition, facilitating resource-aware circuit design.
- Kirill Sechkar
- , Harrison Steel
- & Guy-Bart Stan
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Article
| Open AccessEmpirical data drift detection experiments on real-world medical imaging data
Data drift is the systematic change in the underlying distribution of input features in prediction models, and can cause deterioration in model performance. Here, the authors highlight the importance of detecting data drift in clinical settings and evaluate methods for detecting drift in medical image data.
- Ali Kore
- , Elyar Abbasi Bavil
- & Mohamed Abdalla
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Article
| Open AccessStatistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters
2D visualisation of single-cell data is highly impacted by the hyperparameter setting of the 2D embedding method, such as t-SNE and UMAP. Here, authors develop a statistical method scDEED to detect dubious cell embeddings and optimise the hyperparameter setting for trustworthy visualisation.
- Lucy Xia
- , Christy Lee
- & Jingyi Jessica Li
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Article
| Open AccessProtein design using structure-based residue preferences
Recent protein design methods rely on large neural networks, yet it is unclear which dependencies are critical for determining function. Here, authors show that learning the per residue mutation preferences, without considering interactions, enables design of functional and diverse protein variants.
- David Ding
- , Ada Y. Shaw
- & Debora S. Marks
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| Open AccessscCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data
Single-cell chromatin accessibility sequencing (scCAS) data suffers from high sparsity and dimensionality. Here, authors propose an accurate and interpretable computational framework for enhancing scCAS data that considers cell-to-cell similarity.
- Songming Tang
- , Xuejian Cui
- & Shengquan Chen
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Article
| Open AccessIterative design of training data to control intricate enzymatic reaction networks
Kinetic modeling of in vitro enzymatic reaction networks (ERNs) is severely hampered by the lack of training data. Here, authors introduce a methodology that combines an active learning-like approach and flow chemistry to create optimized datasets for an intricate ERN.
- Bob van Sluijs
- , Tao Zhou
- & Wilhelm T. S. Huck
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Article
| Open AccessA signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing
The authors present DeepMod2, a deep-learning based computational method that allows fast and accurate detection of DNA methylation and epihaplotypes from Oxford Nanopore sequencing data.
- Mian Umair Ahsan
- , Anagha Gouru
- & Kai Wang
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| Open AccessTFvelo: gene regulation inspired RNA velocity estimation
Most RNA velocity models extract dynamics from the phase delay between unspliced and spliced mRNA for each gene. Here, authors propose TFvelo, broadening RNA velocity beyond splicing information to include gene regulation. TFvelo accurately models genes dynamics and infers cell pseudo-time from RNA abundance data.
- Jiachen Li
- , Xiaoyong Pan
- & Hong-Bin Shen
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Article
| Open AccessMAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production
Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses. Here, authors introduce MAIVeSS, a machine learning assisted framework to streamline vaccine seed virus selection using genomic sequence, expediting seasonal flu vaccine production and supply.
- Cheng Gao
- , Feng Wen
- & Xiu-Feng Wan
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| Open AccessProtein structure generation via folding diffusion
The ability to engineer novel protein structures has tremendous scientific and therapeutic impact. Here, authors develop a generative model acting upon an angular representation of protein structures to create high quality protein backbones.
- Kevin E. Wu
- , Kevin K. Yang
- & Ava P. Amini
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| Open AccessThe impacts of active and self-supervised learning on efficient annotation of single-cell expression data
Cell type annotation for single-cell data is challenging. Here, authors explore active and self-supervised learning and introduce adaptive reweighting as a tailored heuristic, demonstrating competitive performance and showing that incorporating prior knowledge enhances cell type annotation accuracy.
- Michael J. Geuenich
- , Dae-won Gong
- & Kieran R. Campbell
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Article
| Open AccessCongenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts
Congenital heart disease is life threatening, and its screening is complex and costly. Here, authors use AI to detect the disease based on pediatric electrocardiogram, suggesting superior performance over cardiologists.
- Jintai Chen
- , Shuai Huang
- & Huiying Liang
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Article
| Open AccessscDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data
Here the authors propose a deep learning model that integrates multi-condition, multi-batch single-cell RNA-sequencing datasets. The model disentangles biological variation (condition effect) from technical confounders (batch effect) and overcomes some limitations of existing approaches.
- Ziqi Zhang
- , Xinye Zhao
- & Xiuwei Zhang
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Article
| Open AccessSemi-supervised integration of single-cell transcriptomics data
Batch effects hinder multi-sample single-cell data analyses. Here, authors present STACAS, a scalable single-cell RNA-seq data integration tool that uses prior cell type knowledge to preserve biological variability, demonstrating robustness to noisy input cell type labels.
- Massimo Andreatta
- , Léonard Hérault
- & Santiago J. Carmona
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Article
| Open AccessCompositional and temporal division of labor modulates mixed sugar fermentation by an engineered yeast consortium
Synthetic microbial communities are suitable for mixed substrates fermentation and long metabolic pathway engineering. Here, the authors combine fermentation experiments with mathematical modeling to reveal the effect of compositional and temporal changes on division of labor in cellulosic ethanol production using two yeast strains.
- Jonghyeok Shin
- , Siqi Liao
- & Yong-Su Jin
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Article
| Open AccessSiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data
Cells simultaneously encode multiple signals, some harder to recover. Here, authors introduce SiFT (Signal FilTering), a kernel-based projection method, revealing underlying biological processes in single-cell data.
- Zoe Piran
- & Mor Nitzan
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Article
| Open AccessEmergence of periodic circumferential actin cables from the anisotropic fusion of actin nanoclusters during tubulogenesis
Periodic circumferential cytoskeletons support biological tube formation. Here, the authors show that self-assembled actin nanoclusters undergo biased fusion and develop into periodic cables in response to the membrane anisotropy of the expanding Drosophila tracheal tube.
- Sayaka Sekine
- , Mitsusuke Tarama
- & Shigeo Hayashi
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Article
| Open AccessRational strain design with minimal phenotype perturbation
No consensus exists on the computationally tractable use of dynamic models for strain design. To tackle this, the authors report a framework, nonlinear-dynamic-model-assisted rational metabolic engineering design, for efficiently designing robust, artificially engineered cellular organisms.
- Bharath Narayanan
- , Daniel Weilandt
- & Vassily Hatzimanikatis
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Article
| Open AccessShifting patterns of dengue three years after Zika virus emergence in Brazil
Dengue virus circulation was unusually low in Brazil in 2015-2018 following the emergence of Zika virus, but subsequently resurged causing large outbreaks with a lower mean age of infection. Here, the authors use mathematical modelling to investigate the links between dengue dynamics and prior Zika infection.
- Francesco Pinotti
- , Marta Giovanetti
- & José Lourenço
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| Open AccessDeterminants of epidemic size and the impacts of lulls in seasonal influenza virus circulation
Seasonal influenza levels were unusually low when non-pharmaceutical interventions for COVID-19 were in place. Here, the authors analyse serological and epidemiological evidence for the hypothesis that such lulls in influenza transmission lead to reduced immunity and therefore larger epidemics in subsequent seasons.
- Simon P. J. de Jong
- , Zandra C. Felix Garza
- & Colin A. Russell
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| Open AccessPROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
Understanding biological mechanisms requires a thorough exploration of spatiotemporal transcriptional patterns in complex tissues. Here, authors present PROST to quantify spatial gene expression patterns and detect spatial domains using spatial transcriptomics data of varying resolutions.
- Yuchen Liang
- , Guowei Shi
- & Zhonghui Tang
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| Open AccessBIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data
Subcellular in situ spatial transcriptomics offers the promise to address biological problems that were previously inaccessible but requires accurate cell segmentation to uncover insights. Here, authors present BIDCell, a biologically informed, deep learning-based cell segmentation framework.
- Xiaohang Fu
- , Yingxin Lin
- & Jean Y. H. Yang
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| Open AccessCryo-EM structure and B-factor refinement with ensemble representation
Cryo-EM is the go-to method for visualizing large, flexible biomolecules. Here, authors introduce a new Gaussian mixture modelling method for cryo-EM modelling tasks, including refinement, composite map generation and ensemble representation.
- Joseph G. Beton
- , Thomas Mulvaney
- & Maya Topf
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Article
| Open AccessMechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC
Heterogeneous response to Enzalutamide remains a critical issue in castration-resistant prostate cancer (CRPC). Here, the authors reconstruct a CRPC-specific mechanism-centric regulatory network to identify signatures of Enzalutamide response and predict patients at risk of Enzalutamide resistance.
- Sukanya Panja
- , Mihai Ioan Truica
- & Antonina Mitrofanova
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| Open AccessMarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
Identifying rare cell populations is key to understanding cancer progression and response to therapy. Here, authors introduce MarsGT, an end-to-end deep learning model for rare cell population identification from scMulti-omics data.
- Xiaoying Wang
- , Maoteng Duan
- & Qin Ma
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| Open AccessEnhancing geometric representations for molecules with equivariant vector-scalar interactive message passing
Utilising geometric information and reducing computational costs are key challenges in the molecular modelling field. Here, authors propose ViSNet, which efficiently extracts geometric features, accurately predicts molecular properties, and drives simulations with interpretability.
- Yusong Wang
- , Tong Wang
- & Tie-Yan Liu
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Article
| Open AccessMENDER: fast and scalable tissue structure identification in spatial omics data
Identifying tissue structure in large-scale spatial omics datasets from multiple slices is challenging. Here, authors present MENDER, an optimisation-free spatial clustering method that can scale to million-level spatial data, enabling efficient analysis of spatial cell atlases.
- Zhiyuan Yuan
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Article
| Open AccessThe juxtamembrane linker of synaptotagmin 1 regulates Ca2+ binding via liquid-liquid phase separation
Synaptotagmin (syt) 1 is a calcium sensor for neuronal exocytosis. Here, the authors show that the juxtamembrane linker of this integral membrane protein negatively regulates its calcium sensing activity by mediating self-association via liquid-liquid phase separation.
- Nikunj Mehta
- , Sayantan Mondal
- & Edwin R. Chapman
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Article
| Open AccessTuning parameters for polygenic risk score methods using GWAS summary statistics from training data
Some polygenic risk score (PRS) methods for predicting genetic risk for common diseases require an external individual-level dataset for parameter tuning, posing privacy-related concerns. Here, the authors present an empirical Bayes method that tunes PRS models using only summary statistics from the training data.
- Wei Jiang
- , Ling Chen
- & Hongyu Zhao
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| Open AccessRevealing hidden patterns in deep neural network feature space continuum via manifold learning
Existing feature visualisation methods are not well-suited for regression tasks. Here, authors introduce a method to learn the manifold topology related to deep neural network output and target labels and provide insightful visualisations of the high-dimensional features while preserving the local geometry.
- Md Tauhidul Islam
- , Zixia Zhou
- & Lei Xing
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Article
| Open AccessPathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity
Clustering-based analysis has limited power in highly dynamic single-cell data, which is a common situation in tumour samples. Here, authors introduce GSDensity, enabling pathway-centric analysis for the direct integration of data with their domain knowledge.
- Qingnan Liang
- , Yuefan Huang
- & Ken Chen
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Article
| Open AccessLinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data
Inferring lineage trees while incorporating gene expressions and lineage barcodes is a challenging task. Here, authors present LinRace, which infers improved cell lineage trees and ancestral cell states using the proposed asymmetric division model.
- Xinhai Pan
- , Hechen Li
- & Xiuwei Zhang
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Article
| Open AccessA computational toolbox for the assembly yield of complex and heterogeneous structures
Predicting the effective assembly of a set of proteins into a desired structure has traditionally been a challenging task. Here, authors demonstrate that advancements in automatic differentiation make it possible to address this problem using classical statistical mechanics.
- Agnese I. Curatolo
- , Ofer Kimchi
- & Michael P. Brenner
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Article
| Open AccessAccurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA
Here, the authors develop MaCroDNA, an algorithm to integrate single-cell DNA and RNA sequencing data from the same tissue. They use MaCroDNA to show—in agreement with previous studies—that copy number changes can predict progression from Barrett’s esophagus to esophageal adenocarcinoma.
- Mohammadamin Edrisi
- , Xiru Huang
- & Luay Nakhleh
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Article
| Open AccessGNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations
Reconstructing transcriptome-wide spatially-resolved gene expressions requires modelling nonlinear patterns and spatial structures in RNA profiling data. Here, authors introduce a graph-guided neural hierarchical tensor decomposition model that incorporates spatial and functional relations for the task.
- Tianci Song
- , Charles Broadbent
- & Rui Kuang
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Article
| Open AccessAn archetype and scaling of developmental tissue dynamics across species
Limb tissue dynamics until basic skeletal pattern establishment exhibit a high degree of conservation between chick and frog after proper rescaling of spacetime, suggesting the presence of a species-independent archetype of morphogenetic dynamics.
- Yoshihiro Morishita
- , Sang-Woo Lee
- & Aiko Kawasumi-Kita
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Article
| Open AccessUniKP: a unified framework for the prediction of enzyme kinetic parameters
Prediction of enzyme kinetic parameters is essential for designing and optimising enzymes for various biotechnological and industrial applications. Here, authors presented a prediction framework (UniKP), which improves the accuracy of predictions for three enzyme kinetic parameters.
- Han Yu
- , Huaxiang Deng
- & Xiaozhou Luo
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Article
| Open AccessSTalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
Spatial transcriptomics (ST) enables gene expression characterisation within tissue sections, but comparing across sections and technologies remains challenging. Here, authors develop STalign to spatially align ST data and demonstrate applications including aligning to common coordinate frameworks.
- Kalen Clifton
- , Manjari Anant
- & Jean Fan