DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology.

Background: Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders.

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning.

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine-learning integrative approach, which seeks to delineate associations between patients' clinical profiles and disrupted biological processes, inferred from their copy number variants (CNVs) that span brain genes. Clustering analysis of the relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups.

Thermal unfolding simulations of NBD1 domain variants reveal structural motifs associated with the impaired folding of F508del-CFTR.

We employed high-temperature classical molecular dynamics (MD) simulations to investigate the unfolding process of the wild-type (WT) and F508del-NBD1 domains of CFTR protein, with and without second-site mutations. To rationalize the in vitro behavior of F508del-NBD1, namely its lower folding yield and higher aggregation propensity, we focused our analysis of the MD data on the existence of intermediate states with aggregation potential and/or stabilized by a significant number of non-native interactions (i.e.

Ab initio calculation of the electronic absorption spectrum of liquid water.

The electronic absorption spectrum of liquid water was investigated by coupling a one-body energy decomposition scheme to configurations generated by classical and Born-Oppenheimer Molecular Dynamics (BOMD). A Frenkel exciton Hamiltonian formalism was adopted and the excitation energies in the liquid phase were calculated with the equation of motion coupled cluster with single and double excitations method. Molecular dynamics configurations were generated by different approaches.