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Local 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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Charting 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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The 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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Structure-based prediction and characterization of photo-crosslinking in native protein–RNA complexes
Feng et al. developed a computational method PxR3D-map to jointly analyze crosslinked nucleotides and amino acids in protein-RNA complexes, which revealed key structural features underlying photocrosslinking of protein and RNA in cells.
- Huijuan Feng
- , Xiang-Jun Lu
- & Chaolin Zhang
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Article
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Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology
Deconvolution of cfDNA fragmentation benefits from cell type-specific reference data. Here, the authors create a disease agnostic cfDNA cell type of origin analysis and show it can successfully predict cell types of origin from plasma samples.
- Kate E. Stanley
- , Tatjana Jatsenko
- & Joris Robert Vermeesch
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Systematic review and meta-analysis for a Global Patient co-Owned Cloud (GPOC)
Use of cloud-based personal health records are increasing globally. Here, authors introduce the Global Patient co-Owned Cloud (GPOC) concept. The systematic review and meta-analysis examine factors like data security, efficiency, privacy, and cost. It aims to establish a scientific basis for a GPOC, which may disseminate global artificial intelligence for healthcare.
- Niklas Lidströmer
- , Joe Davids
- & Eric Herlenius
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BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis
Binning is an essential step in genome-resolved metagenomic analysis in which assembled contigs originating from the same source population are clustered. However it is challenging, especially for low abundance microbial species. Here the authors introduce a toolkit that integrates multiple prominent binning tools and AI for efficient and high-resolution recovery of non-redundant bins from short- and long-read metagenomic sequencing datasets.
- Zhiguang Qiu
- , Li Yuan
- & Ke Yu
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Enabling large-scale screening of Barrett’s esophagus using weakly supervised deep learning in histopathology
Diagnosis of Barrett’s esophagus depends on pathologist assessment of stained slides. Here, the authors utilise a deep learning approach to prioritize potential cases using diagnostic labels in two datasets, with the aim to improve Barrett’s screening capacity.
- Kenza Bouzid
- , Harshita Sharma
- & Javier Alvarez-Valle
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AlphaPept: a modern and open framework for MS-based proteomics
Mass spectrometry-based proteomics faces the challenge of processing vast data amounts. Here, the authors introduce AlphaPept, an open-source, Python-based framework that offers high speed analysis and easy integration for large-scale proteome analysis.
- Maximilian T. Strauss
- , Isabell Bludau
- & Matthias Mann
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Drivers 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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Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance
RNA splicing serves as a critical layer of gene expression regulation. Here, authors introduce SCASL for investigating the heterogeneity of RNA splicing landscapes at single-cell resolution, offering a novel scheme for classifying cell identities with physiological relevance.
- Xianke Xiang
- , Yao He
- & Xuerui Yang
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The defensome of complex bacterial communities
Bacteria have evolved numerous innate and adaptive defence mechanisms. Here, Beavogui et al characterise the impact of biogeography, genetic mobility, and clustering in defense islands, on the defence systems of soil, marine, and human gut bacterial populations genomes.
- Angelina Beavogui
- , Auriane Lacroix
- & Pedro H. Oliveira
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Viscosity-dependent control of protein synthesis and degradation
Xenopus egg extracts constitute a cell-like system for studying biochemical reactions. Here Chen and co-workers show that extract protein synthesis and degradation are differently affected by cytoplasmic concentration and viscosity.
- Yuping Chen
- , Jo-Hsi Huang
- & James E. Ferrell Jr.
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Machine 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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DeepETPicker: 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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bacLIFE: a user-friendly computational workflow for genome analysis and prediction of lifestyle-associated genes in bacteria
Many bacteria live in close association with eukaryotic hosts, exhibiting detrimental, neutral or beneficial effects on host growth and health. Here, the authors present a streamlined computational workflow for bacterial genome annotation, large-scale comparative genomics, and prediction of genes potentially involved in niche adaptation.
- Guillermo Guerrero-Egido
- , Adrian Pintado
- & Víctor J. Carrión
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Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme
Amaryllidaceae alkaloids, such as the Alzheimer’s medication galantamine, are currently extracted from low-yielding daffodils. Here, authors pair biosensor-assisted screening with machine learning-guided protein design to rapidly engineer an improved Amaryllidaceae enzyme in a microbial host.
- Simon d’Oelsnitz
- , Daniel J. Diaz
- & Andrew D. Ellington
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Evolving copy number gains promote tumor expansion and bolster mutational diversification
Understanding the timing and fitness of somatic copy number alterations (SCNAs) in cancer would shed light on cancer progression and evolution. Here, the authors develop Butte, a computational framework to estimate the timing of clonal SCNAs that encompass multiple gains, and apply it on whole-genome sequencing data from 184 samples.
- Zicheng Wang
- , Yunong Xia
- & Ruping Sun
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Article
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Deep 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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Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks
People will likely use ChatGPT to seek health advice. Here, the authors show promising performance of ChatGPT and open source models, but a lack of high accuracy considering medical question answering. Improvements are expected over time via domain-specific finetuning and integration of regulations.
- Sarah Sandmann
- , Sarah Riepenhausen
- & Julian Varghese
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Riboformer: a deep learning framework for predicting context-dependent translation dynamics
Riboformer is a deep learning-based framework that predicts changes in translation dynamics with codon-level precision. It corrects experimental artifacts in ribosome profiling data and identifies sequences causing ribosome stalling.
- Bin Shao
- , Jiawei Yan
- & Allen R. Buskirk
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Machine learning-aided design and screening of an emergent protein function in synthetic cells
Here, the authors introduce a pipeline to screen machine learning generated variants of a protein that forms intracellular spatiotemporal patterns in E. coli, demonstrating the best variants can substitute the wildtype gene.
- Shunshi Kohyama
- , Béla P. Frohn
- & Petra Schwille
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Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population
Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. Here, the authors show that the Quasi-Pareto Improvement approach is widely applicable to improving AI models among less-prevalent subgroups, promoting equitable healthcare outcomes.
- Siqiong Yao
- , Fang Dai
- & Hui Lu
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Article
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A 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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Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics
Efficient and accurate annotation of malignant cells is crucial for single-cell and spatial transcriptomics in cancer. Here, the authors develop Cancer-Finder, a deep-learning algorithm that can identify malignant cells in cancer single-cell and spatial transcriptomics data with speed and precision.
- Zhixing Zhong
- , Junchen Hou
- & Jia Song
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Widespread stable noncanonical peptides identified by integrated analyses of ribosome profiling and ORF features
By developing computational algorithms, the authors annotated translated open reading frames in five eukaryotes and found many stable peptides are encoded by putative ‘noncoding’ regions of genomes.
- Haiwang Yang
- , Qianru Li
- & Zhe Ji
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Article
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Empirical 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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Drug target prediction through deep learning functional representation of gene signatures
Large-scale OMICs investigations of biological systems can be used to predict functional relationships between compounds, genes and proteins. Here, the authors develop a deep learning-based approach that significantly increases the number of high-quality compound-target predictions relative to existing methods.
- Hao Chen
- , Frederick J. King
- & Yingyao Zhou
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A framework for evaluating clinical artificial intelligence systems without ground-truth annotations
Estimating the performance of clinical AI systems on data in the wild is complicated by distribution shift and the absence of ground-truth annotations. Here, we introduce SUDO, a framework for more reliably evaluating AI systems on data in the wild.
- Dani Kiyasseh
- , Aaron Cohen
- & Nicholas Altieri
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Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer
Metastatic castration-resistant prostate cancer is a highly aggressive disease, with a variable response to treatment. Here, the authors validate ctDNA fraction as a poor prognostic factor and develop a model to predict whether patients harbor sufficient ctDNA for informative blood-based genotyping.
- Nicolette M. Fonseca
- , Corinne Maurice-Dror
- & Alexander W. Wyatt
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Functional regulation of aquaporin dynamics by lipid bilayer composition
Membrane proteins depend on their lipid environments. Using aquaporin as a model, the authors show that the choice of lipid bilayer fundamentally affects membrane protein structure, thermodynamics, kinetic, and function, even to the point of lipid-based inhibition.
- Anh T. P. Nguyen
- , Austin T. Weigle
- & Diwakar Shukla
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Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes
Congenital myasthenic syndromes are rare inherited neuromuscular disorders. Here, the authors attempt to explain diverse disease severity seen in 20 patients with shared CHRNE gene mutations with a multilayer network analysis that identifies individual-level impairments at the neuromuscular junction.
- Iker Núñez-Carpintero
- , Maria Rigau
- & Alfonso Valencia
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Automatic data-driven design and 3D printing of custom ocular prostheses
Manual processes to produce ocular prostheses are time-consuming and yield varying quality. Here, authors present an automatic digital end-to-end process for custom ocular prostheses. It creates shape and appearance from image data of an OCT device and produces them using a full-colour 3D printer.
- Johann Reinhard
- , Philipp Urban
- & Mandeep S. Sagoo
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Article
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A release of local subunit conformational heterogeneity underlies gating in a muscle nicotinic acetylcholine receptor
Authors show that agonist binding to the muscle acetylcholine receptor releases local conformational heterogeneity transitioning all subunits into a symmetric open state. A release of conformational heterogeneity underlies allosteric communication.
- Mackenzie J. Thompson
- , Farid Mansoub Bekarkhanechi
- & John E. Baenziger
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Statistical 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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A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study
Alternative polyadenylation (APA) can play a key role in cancer initiation and progression. Here, the authors conducted a comprehensive pan-cancer APA TWAS analysis and discovered a distinct class of APA-mediated cancer susceptibility genes across 22 cancer types.
- Hui Chen
- , Zeyang Wang
- & Lei Li
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Article
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SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis
There is a lack of universal tools to analyse protein assemblies and quantify underlying structures in single-molecule localization microscopy. Here, the authors present SEMORE, a semi-automatic machine learning framework for system- and input-dependent analysis of super-resolution data.
- Steen W. B. Bender
- , Marcus W. Dreisler
- & Nikos S. Hatzakis
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Article
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Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
Modern molecular discovery processes generate millions of measurements at different quality levels. Here, the authors develop a new deep learning method for transfer learning from low-cost and abundant data to enhance the efficiency of drug discovery.
- David Buterez
- , Jon Paul Janet
- & Pietro Lió
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Rapid deep learning-assisted predictive diagnostics for point-of-care testing
A key aim in the development of diagnostic assays is improving diagnostic speed while maintaining sensitivity. Here the authors report an approach for the rapid and accurate analysis of lateral flow tests, which integrates time-series deep learning and AI verification, achieving a diagnostic time of 1-2 minutes.
- Seungmin Lee
- , Jeong Soo Park
- & Jeong Hoon Lee
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Article
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Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer
Gastric cancer detection by endoscopy is intrusive and time-consuming, and early detection is key to improving survival. Here, the authors propose a metabolite-based model to enable early detection.
- Yangzi Chen
- , Bohong Wang
- & Zeping Hu
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Article
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Complex regulatory networks influence pluripotent cell state transitions in human iPSCs
Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Here, authors show that pluripotency and self-renewal processes have a high level of regulatory complexity and suggest that genetic factors contribute to cell state transitions in human iPSC lines.
- Timothy D. Arthur
- , Jennifer P. Nguyen
- & Kelly A. Frazer
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Article
| Open Access
scCASE: 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
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Protein 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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Article
| Open Access
Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
Here the authors report a computational approach which integrates deep learning and structural modelling to design target-specific peptides. They apply this to β-catenin and NF-κB essential modulator, resulting in improved binding, highlighting the efficacy of this strategy.
- Sijie Chen
- , Tong Lin
- & Xiaolin Cheng
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Article
| Open Access
Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features
Density map alignment is a fundamental step in Cryo-EM data postprocessing. Here, authors propose an accurate global and local density map alignment method using local density features.
- Bintao He
- , Fa Zhang
- & Renmin Han
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Article
| Open Access
Learning representations for image-based profiling of perturbations
Assessing cell phenotypes in image-based assays requires solid computational methods for transforming images into quantitative data. Here, the authors present a strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation.
- Nikita Moshkov
- , Michael Bornholdt
- & Juan C. Caicedo
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Article
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Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn
Profiling antibody responses to vast antigenic spaces has been challenging using programmable phage display (PhIP-Seq). Here, authors develop a methodology for compressing large proteomic spaces and have discovered human antibodies targeting gut bacteria-infecting phages.
- Anna-Maria Liebhoff
- , Thiagarajan Venkataraman
- & H. Benjamin Larman
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Article
| Open Access
Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci
Ultraconserved non-coding elements (UCNEs) can regulate developmental gene expression. Retinal multi-omics data integration revealed UCNEs to be candidate cis-regulatory elements during retinal development, which may be implicated in rare eye diseases.
- Victor Lopez Soriano
- , Alfredo Dueñas Rey
- & Elfride De Baere
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Article
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Large language models streamline automated machine learning for clinical studies
A knowledge gap persists between machine learning developers and clinicians. Here, the authors show that the Advanced Data Analysis extension of ChatGPT could bridge this gap and simplify complex data analyses, making them more accessible to clinicians.
- Soroosh Tayebi Arasteh
- , Tianyu Han
- & Sven Nebelung
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