Analysis of Genetic Interaction Maps Reveals Functional Pleiotropy

Epistatic or genetic interactions, representing the effects of mutations on the phenotypes caused by other mutations, can be very helpful for uncovering functional relationships between genes. Recently, the Epistasis Miniarray Profile (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. As part of this approach, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles. Here we present an original biclustering algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function, a goal that cannot be achieved with hierarchical clustering. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast S. cerevisiae. In addition to identifying the majority of the functional modules reported in these studies, our algorithm uncovers many recently documented and novel multi-functional relationships between genes and gene groups

An efficiency upper bound for inverse covariance estimation

We derive an upper bound for the efficiency of estimating entries in the inverse covariance matrix of a high dimensional distribution. We show that in order to approximate an off-diagonal entry of the density matrix of a $d$-dimensional Gaussian random vector, one needs at least a number of samples proportional to $d$. Furthermore, we show that with $n \ll d$ samples, the hypothesis that two given coordinates are fully correlated, when all other coordinates are conditioned to be zero, cannot be told apart from the hypothesis that the two are uncorrelated.Comment: 7 Page

Dipolar Bose gases: Many-body versus mean-field description

We characterize zero-temperature dipolar Bose gases under external spherical confinement as a function of the dipole strength using the essentially exact many-body diffusion Monte Carlo (DMC) technique. We show that the DMC energies are reproduced accurately within a mean-field framework if the variation of the s-wave scattering length with the dipole strength is accounted for properly. Our calculations suggest stability diagrams and collapse mechanisms of dipolar Bose gases that differ significantly from those previously proposed in the literature

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and rising incidence rate in the developed world. Molecular profiling techniques have been used to study the variability between tumours as well as cancer models such as cell lines, but their translational value is incomplete with current methods. Moreover, first generation computational methods for subtype classification do not make use of multi-omics data in full scale. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We present a novel subtype classification method based on deep learning and apply it to classify CRC tumors using multi-omics data, and further to measure the similarity between tumors and disease models such as cancer cell lines. Multi-omics Autoencoder Integration (maui) efficiently leverages data sets containing copy number alterations, gene expression, and point mutations, and learns clinically important patterns (latent factors) across these data types. Using these latent factors, we propose a refinement of the gold-standard CRC subtypes, and propose best-matching cell lines for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for drug discovery pipelines, biomarker discovery, and target identification

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning-based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification

Hydrophobic Interactions and Dewetting between Plates with Hydrophobic and Hydrophilic Domains

We study by molecular dynamics simulations the wetting/dewetting transition and the dependence of the free energy on distance between plates that contain both hydrophobic and hydrophilic particles. We show that dewetting and strength of hydrophobic interaction is very sensitive to the distribution of hydrophobic and hydrophilic domains. In particular, we find that plates characterized by a large domain of hydrophobic sites induce a dewetting transition and an attractive solvent-induced interaction. On the other hand, a homogeneous distribution of the hydrophobic and hydrophilic particles on the plates prevents the dewetting transition and produces a repulsive solvent-induced interaction. We also present results for a kind of Janus interface in which one plate consists of hydrophobic particles and the other of hydrophilic particles showing that the inter-plate gap remains wet until steric constraints at small separations eject the water molecules. Our results indicate that the Cassie equation, for the contact angle of a heterogeneous plate, can not be used to predict the critical distance of dewetting. These results indicate that hydrophobic interactions between nanoscale surfaces with strong large length-scale hydrophobicity can be highly cooperative and thus they argue against additivity of the hydrophobic interactions between different surface domains in these cases. These findings are pertinent to certain protein-protein interactions where additivity is commonly assumed.Comment: 28 pages, 6 figure

Assessing banks’ resilience: A complementary approach to stress testing using fair values from banks’ financial statements

For more than a decade, supervisory banking authorities in Europe and the United States have sought to assess the resilience of banks to adverse economic episodes to safeguard the financial system's stability. They rely on regulatory capital measures like Common Equity Tier 1 (CET1) relative to risk-weighted assets in the aftermath of potential economic crises. We propose a new measure of banks' resilience based on financial statements. The fair value margin (FVM) is estimated as the difference between the fair value of assets and the book value of liabilities, scaled by the book value of equity. We find that FVM is positively associated with the surplus or shortfall of CET1 resulting from the stress testing results from 2014, 2016 and 2018. To corroborate the relevance of FVM for supervisory authorities, we compare the ability of the loan component of FVM to predict future credit losses with the capital surplus/shortfall metric derived from the stress test. The findings indicate that the fair value of loans predicts net charge-offs better than stress test outcomes. Therefore, we suggest that FVM could be used as a readily available and relatively low-cost tool to assess bank resilience, thus complementing the stress test exercises