Machine learning articles within Nature Communications

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  • Article
    | Open Access

    High quality labels are important for model performance, evaluation and selection in medical imaging. As manual labelling is time-consuming and costly, the authors explore and benchmark various resource-effective methods for improving dataset quality.

    • Mélanie Bernhardt
    • , Daniel C. Castro
    •  & Ozan Oktay

  • Article
    | Open Access

    Randomized clinical trials are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding factors. Here, the authors develop a framework based on natural language processing to uncover interpretable potential confounders from text.

    • Jiaming Zeng
    • , Michael F. Gensheimer
    •  & Ross D. Shachter

  • Article
    | Open Access

    While artificial intelligence (AI) is quickly becoming ubiquitous, biology still suffers from the lack of interfaces connecting biological structures and modern AI methods. Here, the authors report PyUUL, a library to translate biological structures into 3D differentiable tensorial representations.

    • Gabriele Orlando
    • , Daniele Raimondi
    •  & Frederic Rousseau

  • Article
    | Open Access

    To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from both molecule structure and biomedical text information, the authors propose a machine reading system that bridges both types of information.

    • Zheni Zeng
    • , Yuan Yao
    •  & Maosong Sun

  • Article
    | Open Access

    Single-cell genomic technologies present unique data integration challenges. Here the authors introduce an integrative nonnegative matrix factorization algorithm that incorporates features unshared between datasets when performing dataset integrations, improving integration results for spatial transcriptomic, cross-modality, and cross-species data.

    • April R. Kriebel
    •  & Joshua D. Welch

  • Article
    | Open Access

    Rational protein design to achieve a given protein backbone conformation is needed to engineer specific functions. Here Anand et al. describe a machine learning method using a learned neural network potential for fixed-backbone protein design.

    • Namrata Anand
    • , Raphael Eguchi
    •  & Po-Ssu Huang

  • Article
    | Open Access

    Genetic associations can be biased by conditioning on a phenotype. This study presents ‘Slope-Hunter’, a method which uses model-based clustering to correct this bias, even in the presence of genetic correlation, assuming the class of SNPs affecting only the collider explains more variation in the collider than any other class of SNPs.

    • Osama Mahmoud
    • , Frank Dudbridge
    •  & Kate Tilling

  • Article
    | Open Access

    Growth limitation caused by mutual shading and the high harvest cost hamper algal biofuel production. Here, the authors overcome these two problems by designing a semi-continuous algal cultivation system and an aggregation-based sedimentation strategy to achieve high levels production of biomass and limonene.

    • Bin Long
    • , Bart Fischer
    •  & Joshua S. Yuan

  • Article
    | Open Access

    The late-stage functionalization of unactivated carbon–hydrogen bonds is a difficult but important task, which has been met with promising but limited success through synthetic organic chemistry. Here the authors use machine learning to engineer WelO5* halogenase variants, which led to regioselective chlorination of inert C–H bonds on a representative polyketide that is a non-natural substrate for the enzyme.

    • Johannes Büchler
    • , Sumire Honda Malca
    •  & Rebecca Buller

  • Article
    | Open Access

    AlphaFold2 has originally been developed to provide highly accurate predictions of protein monomer structures. Here, the authors present a simple adaptation of AlphaFold2 that enables structural modeling of peptide–protein complexes, and explore the underlying mechanisms and limitations of this approach.

    • Tomer Tsaban
    • , Julia K. Varga
    •  & Ora Schueler-Furman

  • Article
    | Open Access

    Ordinary differential equation (ODE) models are widely used to understand multiple processes. Here the authors show how the concept of mini-batch optimization can be transferred from the field of Deep Learning to ODE modelling.

    • Paul Stapor
    • , Leonard Schmiester
    •  & Jan Hasenauer

  • Article
    | Open Access

    The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models.

    • Nicolas Renaud
    • , Cunliang Geng
    •  & Li C. Xue

  • Article
    | Open Access

    Prediction of drug-target interactions (DTI) plays a vital role in drug development through applications in various areas, such as virtual screening for lead discovery, drug repurposing and identification of potential drug side effects. Here, the authors develop a unified framework for DTI prediction by combining a knowledge graph and a recommendation system.

    • Qing Ye
    • , Chang-Yu Hsieh
    •  & Tingjun Hou

  • Article
    | Open Access

    The coverage and throughput of data-independent acquisition (DIA)-based phosphoproteomics is limited by its dependence on experimental spectral libraries. Here the authors develop a DIA workflow based on in silico spectral libraries generated by a novel deep neural network to expand phosphoproteome coverage.

    • Ronghui Lou
    • , Weizhen Liu
    •  & Wenqing Shui

  • Article
    | Open Access

    With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes.

    • Doron Stupp
    • , Elad Sharon
    •  & Yuval Tabach

  • Article
    | Open Access

    Deep learning (DL) can be used to automatically extract complex features from dynamic systems. Here, the authors combine high-content imaging, DL and mechanistic models to extract and explain drug-induced morphological changes in the growth of the fungus responsible for Asian soybean rust.

    • Henry Cavanagh
    • , Andreas Mosbach
    •  & Robert G. Endres

  • Article
    | Open Access

    The exact protein features that control passage through the eukaryotic secretory system remain largely unknown. Here the authors report SECRiFY which they use to evaluate the secretory potential of polypeptides on a proteome-wide scale in yeast, revealing a role for flexibility and intrinsic disorder.

    • Morgane Boone
    • , Pathmanaban Ramasamy
    •  & Nico Callewaert

  • Article
    | Open Access

    scATAC-Seq yields data that is extremely sparse. Here, the authors present a computationally efficient imputation method called scOpen that improves the downstream analyses of scATAC-Seq data and use it to identify transcriptional regulators of kidney fibrosis.

    • Zhijian Li
    • , Christoph Kuppe
    •  & Ivan G. Costa

  • Article
    | Open Access

    Generative models have become increasingly popular in protein design, yet rigorous metrics that allow the comparison of these models are lacking. Here, the authors propose a set of such metrics and use them to compare three popular models.

    • Francisco McGee
    • , Sandro Hauri
    •  & Allan Haldane

  • Article
    | Open Access

    Machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive amounts of labeled data. Here, the authors propose a semi-supervised model based on the mean teacher architecture that provides pathological predictions at both patch- and patient-levels.

    • Gang Yu
    • , Kai Sun
    •  & Hong-Wen Deng

  • Article
    | Open Access

    Although much effort has been devoted to determine the 3D structure of chromatin, there is a need for new experimental and computational methods. Here the authors present GP-FBM to extract chromatin diffusion parameters with high precision and apply it to live-imaging of embryonic stem cells, revealing that the diffusive properties of mitotic and interphase chromatin do not differ significantly.

    • Guilherme M. Oliveira
    • , Attila Oravecz
    •  & Nacho Molina

  • Article
    | Open Access

    Existing approaches to sharing of distributed medical data either provide only limited protection of patients’ privacy or sacrifice the accuracy of results. Here, the authors propose a federated analytics system, based on multiparty homomorphic encryption (MHE), to overcome these issues.

    • David Froelicher
    • , Juan R. Troncoso-Pastoriza
    •  & Jean-Pierre Hubaux

  • Article
    | Open Access

    Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.

    • Shanshan Wang
    • , Cheng Li
    •  & Hairong Zheng

  • Article
    | Open Access

    Fatty acyl reductases (FARs) are critical enzymes in the biosynthesis of fatty alcohols and have the ability to directly acces acyl-ACP substrates. Here, authors couple machine learning-based protein engineering framework with gene shuffling to optimize a FAR for the activity on acyl-ACP and improve fatty alcohol production.

    • Jonathan C. Greenhalgh
    • , Sarah A. Fahlberg
    •  & Philip A. Romero

  • Article
    | Open Access

    In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such domain shifts occurring at unknown time points.

    • Matthias Perkonigg
    • , Johannes Hofmanninger
    •  & Georg Langs

  • Article
    | Open Access

    Developing interpretable models is a major challenge in single cell deep learning. Here we show that the VEGA variational autoencoder model, whose decoder wiring mirrors gene modules, can provide direct interpretability to the latent space further enabling the inference of biological module activity.

    • Lucas Seninge
    • , Ioannis Anastopoulos
    •  & Joshua Stuart

  • Article
    | Open Access

    Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new folds.

    • Rahil Taujale
    • , Zhongliang Zhou
    •  & Natarajan Kannan

  • Article
    | Open Access

    Computer-assisted diagnosis is key for scaling up cervical cancer screening, but current algorithms perform poorly on whole slide image analysis and generalization. Here, the authors present a WSI classification and top lesion cell recommendation system using deep learning, and achieve comparable results with cytologists.

    • Shenghua Cheng
    • , Sibo Liu
    •  & Xiuli Liu

  • Article
    | Open Access

    How to infer transient cells and cell-fate transitions from snap-shot single cell transcriptome dataset remains a major challenge. Here the authors present a multiscale approach to construct single-cell dynamical manifold, quantify cell stability, and compute transition trajectory and probability between cell states.

    • Peijie Zhou
    • , Shuxiong Wang
    •  & Qing Nie

  • Article
    | Open Access

    Classifying cells into unseen cell types remains challenging in scRNA-seq analysis. Here we show that Cell Ontology enables an accurate classification of unseen cell types through considering the cell type relationships in the Cell Ontology graph.

    • Sheng Wang
    • , Angela Oliveira Pisco
    •  & Russ B. Altman

  • Article
    | Open Access

    The high dimensional and complex nature of mass spectrometry imaging (MSI) data poses challenges to downstream analyses. Here the authors show an application of artificial intelligence in mining MSI data revealing biologically relevant metabolomic and proteomic information from data acquired on different mass spectrometry platforms.

    • Walid M. Abdelmoula
    • , Begona Gimenez-Cassina Lopez
    •  & Nathalie Y. R. Agar

  • Article
    | Open Access

    Comparing changes in behaviour across various species is not always trivial, especial across significantly divergent species. Here, the authors develop a deep learning framework that allows them to map changes in locomotion demonstrated on dopamine-deficient humans, mice and worms.

    • Takuya Maekawa
    • , Daiki Higashide
    •  & Susumu Takahashi

  • Article
    | Open Access

    Dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool are recommended tools for osteoporotic fracture risk evaluation, but are underutilized. Here, the authors present an opportunistic tool to identify fractures, predict bone mineral density and evaluate fracture risk using plain pelvis and lumbar spine radiographs.

    • Chen-I Hsieh
    • , Kang Zheng
    •  & Chang-Fu Kuo

  • Article
    | Open Access

    Peptide-protein interactions play fundamental roles in cellular processes and are crucial for designing peptide therapeutics. Here, the authors present a deep learning framework for simultaneously predicting peptide-protein interactions and identifying peptide binding residues involved in the interactions.

    • Yipin Lei
    • , Shuya Li
    •  & Jianyang Zeng

  • Article
    | Open Access

    The molecular basis of Alzheimer’s Disease has been obscured by heterogeneity and scarcity of brain gene expression data, which limit effectiveness in complex models. Here, the authors introduce a multi-task deep learning framework to learn generalizable and nuanced relationships between gene expression and neuropathology.

    • Nicasia Beebe-Wang
    • , Safiye Celik
    •  & Su-In Lee

  • Article
    | Open Access

    Intratumour heterogeneity (ITH) and mutational signatures are typically analysed separately, even though they are not necessarily independent. Here, the authors present CloneSig, a tool for the joint estimation of ITH and mutational signatures, with which they analyse the TCGA and PCAWG datasets.

    • Judith Abécassis
    • , Fabien Reyal
    •  & Jean-Philippe Vert

  • Article
    | Open Access

    Disambiguating abbreviations is important for automated clinical note processing; however, deploying machine learning for this task is restricted by lack of good training data. Here, the authors show novel data augmentation methods that use biomedical ontologies to improve abbreviation disambiguation in many datasets.

    • Marta Skreta
    • , Aryan Arbabi
    •  & Michael Brudno

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