Articles

Analysis and interpretation of relationships across different modalities of biomedical datasets is a critical but challenging task. SiMLR is a joint dimensionality reduction algorithm that is designed to perform this task.

Spiking neural network simulations are very memory-intensive, limiting large-scale brain simulations to high-performance computer systems. Knight and Nowotny propose using procedural connectivity to substantially reduce the memory footprint of these models, such that they can run on standard GPUs.

The FDRnet method addresses several issues in cancer pathway analysis, including those related to detecting functionally homogeneous subnetworks, controlling the false discovery rate of the genes and handling the computational complexity.

Multi-fidelity graph networks learn more effective representations for materials from large data sets of low-fidelity properties, which can then be used to make accurate predictions of high-fidelity properties, such as the band gaps of ordered and disordered crystals and energies of molecules.

An approach based on soft Markov state models is capable of constructing interpretable kinetic ensembles of disordered proteins.

Computational simulations show that selection for high gene expression stability can explain the stable maintenance of obsolete phenotypic switching capabilities under natural selection.

An evolutionary-based algorithm enables modeling of complex solid-solution alloys over exponential search spaces in practical time, accelerating materials design of such high-entropy alloys.