Volume 1 Issue 8, August 2021

A model for Drosophila embryonic development is presented by integrating several types of experimental data spanning over several layers of space and time.

Modeling of the multiscale dynamics of new bone formation in tissue scaffolds is still challenging due to the computational complexity in solving the mechanics–material–biology interactions. Recent work proposes a machine learning approach to address this challenge.

Massimino et al. propose the Inflammatory Bowel Disease Transcriptome and Metatranscriptome Meta-Analysis (IBD TaMMA) framework, an open-source platform for expediting the investigation of IBD-specific transcriptomics and metatranscriptomics signatures.

A computational approach to predictive modeling of embryonic development is presented and validated by integrating imaging, biochemical and genetic studies of tissue patterning in Drosophila embryogenesis.

Countries are using hospital admission policies that prioritize patients with COVID-19 during the pandemic. The authors propose an alternative open-source framework to optimally schedule hospital care for all diseases and patients that can save life years overall.

The study develops a machine learning approach for predicting bone regeneration in an additively manufactured bioceramic scaffold, which is correlated with an in vivo sheep model, exhibiting effectiveness for solving such a multiscale modeling problem.

Optical computing promises high-speed computations but presents challenges in nonlinear information processing. This Article demonstrates a scalable and energy-efficient nonlinear optical-computing framework that can perform machine learning tasks.

The study introduces the design and implementation of a parallel computational framework, called HiCOPS, for efficient acceleration of large-scale database peptide search workloads on supercomputers.