Volume 2 Issue 6, June 2022

Gender inequality has been the unspoken truth, rampant for centuries. Although a deep-rooted cultural mindset, the inequality has reverse-translated from society into the way we study and practice science, and more currently, into the computational modeling world.

Dr Sean Gibbons, assistant professor at the Institute for Systems Biology and a Washington Research Foundation Distinguished Investigator, discusses with Nature Computational Science how he uses computational science to gain insights into the gut microbiome and to address the major challenges of this field, as well as his advice to young LGBTQIA+ scientists.

A graph neural network-based tool is introduced to perform unsupervised cell clustering using spatially resolved transcriptomics data that can uncover cell identities, interactions, and spatial organization in tissues and organs.

Aptamers are expected to be next-generation drugs, but identifying candidate aptamers is a challenging task given the large search space. Now, an artificial intelligence (AI)-powered tool called RaptGen is proposed for improving the successful identification of aptamer sequences.

Machine learning has been used to accelerate the simulation of fluid dynamics. However, despite the recent developments in this field, there are still challenges to be addressed by the community, a fact that creates research opportunities.

A deep neural network method is developed to learn the mapping function from atomic structure to density functional theory (DFT) Hamiltonian, which helps address the accuracy–efficiency dilemma of DFT and is useful for studying large-scale materials.

A probabilistic generative model for aptamers called RaptGen is introduced, which accelerates the process of aptamer development by generating new aptamer sequences.

ActiveSVM is introduced as a feature selection procedure for discovering minimal gene sets in large single-cell mRNA-seq datasets.

A graph neural network-based cell clustering model for spatial transcripts obtains cell embeddings from global cell interactions across tissue samples and identifies cell types and subpopulations.