Machine learning articles within Nature Communications

Featured

  • Article
    | Open Access

    Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide range of imaging tasks.

    • Audrey Durand
    • , Theresa Wiesner
    •  & Flavie Lavoie-Cardinal

  • Article
    | Open Access

    The diagnosis of sleep disorders such as narcolepsy and insomnia currently requires experts to interpret sleep recordings (polysomnography). Here, the authors introduce a neural network analysis method for polysomnography that could reduce time spent in sleep clinics and automate narcolepsy diagnosis.

    • Jens B. Stephansen
    • , Alexander N. Olesen
    •  & Emmanuel Mignot

  • Article
    | Open Access

    Publicly available single cell RNA-seq datasets represent valuable resources for comparative and meta-analysis. Here, the authors develop scQuery, a web server integrating over 500 different studies with over 300 unique cell types for comparative analysis of existing and new scRNA-seq data.

    • Amir Alavi
    • , Matthew Ruffalo
    •  & Ziv Bar-Joseph

  • Article
    | Open Access

    Gene synthesis has expanded the ability to modify and create DNA sequences, with implications for biosurveillance. The authors use machine learning and codon theory to identify synthetic genes in Addgene data, and show that synthesis accelerates human-directed gene transfer across the tree of life.

    • Aditya M. Kunjapur
    • , Philipp Pfingstag
    •  & Neil C. Thompson

  • Article
    | Open Access

    Approximately 30% of psoriasis patients develop psoriatic arthritis (PsA) and early diagnosis is crucial for the management of PsA. Here, Patrick et al. develop a computational pipeline involving statistical and machine-learning methods that can assess the risk of progression to PsA based on genetic markers.

    • Matthew T. Patrick
    • , Philip E. Stuart
    •  & Lam C. Tsoi

  • Article
    | Open Access

    Clinical trials for the CYD-TDV dengue vaccine showed that vaccine efficacy varies with prior dengue exposure, but baseline serostatus is only known for 12% of subjects. Here, Dorigatti et al. use machine learning to impute baseline serostatus and determine vaccine efficacy by baseline serostatus, age and dengue serotype.

    • I. Dorigatti
    • , C. A. Donnelly
    •  & N. M. Ferguson

  • Article
    | Open Access

    AGO-PAR-CLIP is widely used for high-throughput miRNA target characterization. Here, the authors show that the previously neglected non-T-to-C clusters denote functional miRNA binding events, and develop microCLIP, a super learning framework that accurately detects miRNA interactions.

    • Maria D. Paraskevopoulou
    • , Dimitra Karagkouni
    •  & Artemis G. Hatzigeorgiou

  • Article
    | Open Access

    In this study the authors build lncRNA-drug response models for 265 anti-cancer agents across 27 cancer types. They report their cancer cell line based lncRNA EN-models are able to effectively predict therapeutic outcome for breast cancer, ovarian cancer, endometrial cancer and stomach cancer patients.

    • Yue Wang
    • , Zehua Wang
    •  & Da Yang

  • Article
    | Open Access

    The synthetic biology era has seen a rapidly growing number of engineered DNA sequences. Here, the authors develop a deep learning method to predict the lab-of-origin of a DNA sequence based on hidden design signatures.

    • Alec A. K. Nielsen
    •  & Christopher A. Voigt

  • Article
    | Open Access

    Technical noise in experiments is unavoidable, but it introduces inaccuracies into the biological networks we infer from the data. Here, the authors introduce a diffusion-based method for denoising undirected, weighted networks, and show that it improves the performances of downstream analyses.

    • Bo Wang
    • , Armin Pourshafeie
    •  & Jure Leskovec

  • Article
    | Open Access

    Serotonin (5-HT) plays many important roles in reward, punishment, patience and beyond, and optogenetic stimulation of 5-HT neurons has not crisply parsed them. The authors report a novel analysis of a reward-based decision-making experiment, and show that 5-HT stimulation increases the learning rate, but only on a select subset of choices.

    • Kiyohito Iigaya
    • , Madalena S. Fonseca
    •  & Peter Dayan

  • Article
    | Open Access

    Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

    • Decebal Constantin Mocanu
    • , Elena Mocanu
    •  & Antonio Liotta

  • Article
    | Open Access

    Dimensionality reduction and visualization methods lack a principled way of comparing multiple datasets. Here, Abid et al. introduce contrastive PCA, which identifies low-dimensional structures enriched in one dataset compared to another and enables visualization of dataset-specific patterns.

    • Abubakar Abid
    • , Martin J. Zhang
    •  & James Zou

  • Article
    | Open Access

    Cell protrusion dynamics are heterogeneous at the subcellular level, but current analyses operate at the cellular or ensemble level. Here the authors develop a computational framework to quantify subcellular protrusion phenotypes and reveal the underlying actin regulator dynamics at the leading edge.

    • Chuangqi Wang
    • , Hee June Choi
    •  & Kwonmoo Lee

  • Article
    | Open Access

    Functional characterisation of single cells is crucial for uncovering the true extent of cellular heterogeneity. Here the authors offer an approach to infer functional identities of cells from their transcriptomes, identify their dominant function, and reconstruct the underlying regulatory networks.

    • Shahin Mohammadi
    • , Vikram Ravindra
    •  & Ananth Grama

  • Article
    | Open Access

    Assays to characterize the epigenome and interrogate chromatin state genome wide have so far been performed in a selected set of conditions. Here, Durham et al. develop a computational method based on tensor decomposition to impute missing experiments in collections of epigenomics experiments.

    • Timothy J. Durham
    • , Maxwell W. Libbrecht
    •  & William Stafford Noble

  • Article
    | Open Access

    RNA levels in post-mortem tissue can differ greatly from those before death. Studying the effect of post-mortem interval on the transcriptome in 36 human tissues, Ferreira et al. find that the response to death is largely tissue-specific and develop a model to predict time since death based on RNA data.

    • Pedro G. Ferreira
    • , Manuel Muñoz-Aguirre
    •  & Roderic Guigó

  • Article
    | Open Access

    The isolation of single cells while retaining context is important for quantifying cellular heterogeneity but technically challenging. Here, the authors develop a high-throughput, scalable workflow for microscopy-based single cell isolation using machine-learning, high-throughput microscopy and laser capture microdissection.

    • Csilla Brasko
    • , Kevin Smith
    •  & Peter Horvath

  • Article
    | Open Access

    Extracting kinetic models from high-throughput molecular dynamics (MD) simulations is laborious and prone to human error. Here the authors introduce a deep learning framework that automates construction of Markov state models from MD simulation data.

    • Andreas Mardt
    • , Luca Pasquali
    •  & Frank Noé

  • Article
    | Open Access

    Network dynamical systems can represent the interactions involved in the collective dynamics of gene regulatory networks or metabolic circuits. Here Casadiego et al. present a method for inferring these types of interactions directly from observed time series without relying on their model.

    • Jose Casadiego
    • , Mor Nitzan
    •  & Marc Timme

  • Article
    | Open Access

    Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

    • Yunan Luo
    • , Xinbin Zhao
    •  & Jianyang Zeng

  • Article
    | Open Access

    The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

    • Philipp Eulenberg
    • , Niklas Köhler
    •  & F. Alexander Wolf

  • Article
    | Open Access

    While rare cell subpopulations frequently make the difference between health and disease, their detection remains a challenge. Here, the authors devise CellCnn, a representation learning approach to detecting such rare cell populations from high-dimensional single cell data, and, among other examples, demonstrate its capacity for detecting rare leukaemic blasts in minimal residual disease.

    • Eirini Arvaniti
    •  & Manfred Claassen

  • Article
    | Open Access

    Understanding the dynamics of enzyme-substrate complexation provides an insight into potential drugs, but intermediate states are difficult to observe experimentally. Here, the authors use simulations and machine learning to analyse the binding of transition state inhibitors to purine nucleoside phosphorylase.

    • Sergio Decherchi
    • , Anna Berteotti
    •  & Andrea Cavalli

  • Article |

    microRNAs are short non-coding RNAs that post-transcriptionally regulate gene expression for which the identification of promoter and primary transcripts (pri-miRNAs) has been difficult. Here the authors describe microTSS, an algorithm that supports the precise identification of intergenic pri-miRNA transcription start sites.

    • Georgios Georgakilas
    • , Ioannis S. Vlachos
    •  & Artemis G. Hatzigeorgiou

Browse broader subjects

Browse Machine learning across other nature.com journals