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Volume 2 Issue 5, May 2022

Evaluation of brain connectomes at scale

Identifying structural brain connectivity, also known as the connectome, is imperative for elucidating how neurons and neural networks process information. Existing algorithms for pruning big connectome data, however, have limited speed and memory performance for connectome-wide association studies. In this issue, Sreenivasan et al. propose a GPU-based implementation for connectome pruning called ReAl-LiFE and demonstrate its computational efficiency and utility by applying it to a wide range of datasets.

See Sreenivasan et al. and Zuo

Image: Sherbrooke Connectivity Imaging Lab (SCIL) / Image Source / Getty Images. Cover Design: Thomas Phillips.

Editorial

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Comment & Opinion

  • Normative modeling is considered one of the most promising avenues towards personalized medicine. The integration of multimodal, mechanistic and lifespan modeling will play an essential role, but significant challenges need to be overcome before this promise can be turned into reality.

    • Thilo Rieg
    • Emanuel Schwarz
    Comment
  • Dr Srijan Kumar, assistant professor at Georgia Institute of Technology and a Forbes 30 Under 30 honoree in science, discusses with Nature Computational Science how he uses machine learning and data science to identify and mitigate malicious activities on online platforms, including misinformation and anti-Asian hate speech.

    • Fernando Chirigati
    Q&A
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Research Highlights

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News & Views

  • Characterizing the brain’s connectome at multiple scales is essential for unraveling fundamental principles of cortical information processing and how it impacts behavior. A GPU-based implementation for connectome pruning is proposed, achieving greater than 100-fold speedups over previous CPU-based implementations.

    • Xi-Nian Zuo
    News & Views
  • The identification of robust and generalizable biomarkers based on microbial abundance data is a challenging task. An algorithm shows an enhanced classification performance by quantifying shifts in microbial co-abundances.

    • Leo Lahti
    News & Views
  • Variational Monte Carlo is one of the most accurate methods to solve the many-electron Schrödinger equation, but suffers from high computational cost. A recent study uses a weight-sharing technique to accelerate the neural network-based variational Monte Carlo method, allowing accurate and effective simulations of molecules.

    • Huan Tran
    News & Views
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Research Briefings

  • Biomimetic nanoparticles can form complexes with proteins. Structural descriptors have been identified to predict nanoparticle–protein complex formation and their interaction sites. These descriptors include geometrical and graph-theoretical molecular features that are universally applicable to all nanoscale macromolecules of both organic and inorganic chemistries.

    Research Briefing
  • Determining the origin of engineered DNA can help to foster responsible innovation within the biotechnology community. A convolutional neural network approach that learns distances between engineered DNA sequences and various labs that could have created them is used to accurately predict the lab-of-origin.

    Research Briefing
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