Effect of asymptomatic transmission and emergence time on multi-strain viral disease severity.

In a viral epidemic, the emergence of a novel strain with increased transmissibility (larger value of basic reproduction number R0) sparks the fear that the increase in transmissibility is likely to lead to an increase in disease severity. It is required to investigate if a new, more contagious strain will be necessarily dominant in the population and resulting in more disease severity. In this paper, the impact of the asymptomatic transmission and the emergence time of a more transmissible variant of a multi-strain viral disease on the disease prevalence, disease severity, and the dominant variant in an epidemic was investigated by a proposed 2-strain epidemic model.

False Negative Mitigation in Group Testing for COVID-19 Screening.

After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected.

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.

Background: Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data.

Ambiguity in logic-based models of gene regulatory networks: An integrative multi-perturbation analysis.

Most studies of gene regulatory network (GRN) inference have focused extensively on identifying the interaction map of the GRNs. However, in order to predict the cellular behavior, modeling the GRN in terms of logic circuits, i.e.

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment.