MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

What the public needs to see and say: An easier guide to early detection of armed assailants

In the wake of active shooting, the commonly cited “See Something, Say Something” campaign is, by and large, ineffective, not because it lacks good intent. Rather, it fails insofar as it does not give the public clear criteria of what to see, what to say, to whom to say it and when. Terrorists and other types of armed assailants wishing to use violence, wreaking out death and destruction for political or personal ends, do not suddenly launch an attack. Prior to each attack, the could-be armed assailant does extensive research, And, even prior to deciding on becoming an armed assailant, the could-be armed assailant broadcasts clear and recognizable signals. While not predictable, active shootings are foreseeable. After debunking four popular myths, the article identifies a pyramid of five phases which the could-be armed assailant ascends and the respective indicators. The article concludes by discussing a proposed risk model and means by which to reduce that risk, not the least of which is an effective awareness and reporting program incorporating among others human resource personnel, psychologists, social workers and naturally law enforcement officials designed to mitigate the risk a could-be armed assailant ascend the pyramid. It ultimately challenges scholars in the fields of Psychology, Sociology, and Political Science to explore the underlying reasons which explain and thereby confront the underlying triggers which inspire could-be armed assailants to move toward the apex of the pyramid. But more it calls for raising the awareness of those who witness the broadcasts

Disproportionate burden of violence: Explaining racial and ethnic disparities in potential years of life lost among homicide victims, suicide decedents, and homicide-suicide perpetrators.

Research indicates that the burden of violent death in the United States is disproportionate across racial and ethnic groups. Yet documented disparities in rates of violent death do not capture the full extent of this inequity. Recent studies examining race-specific rates of potential years of life lost-a summary measure of premature mortality-indicate that persons of color may die at younger ages than their counterparts, leading to increased trauma among surviving family members, friends, and communities. This study examines racial and ethnic disparities in potential years of life lost among people who died by homicide and suicide. We calculated potential years of life lost using life expectancy values specific to each racial and ethnic group, thereby isolating racial differences in potential years of life lost due to violence. Findings indicated that persons of color were disproportionately impacted by violence. Non-Hispanic African American homicide victims, suicide decedents, and homicide-suicide perpetrators died eleven or more years earlier than their non-Hispanic White counterparts. Similar disparities were observed for non-Hispanic Asian or Pacific Islander decedents. Less pronounced differences were observed for Hispanic and non-Hispanic American Indian or Alaska Native decedents. These racial and ethnic disparities were partly accounted for by a broad array of individual differences, incident characteristics, and contextual factors. The results suggest that homicide and suicide exact a high societal cost, and the burden of that cost is disproportionately high among persons of color

Descriptive statistics for suicide decedents, by race and ethnicity.

Descriptive statistics for suicide decedents, by race and ethnicity.</p

Descriptive statistics for homicide-suicide perpetrators, by race and ethnicity.

Descriptive statistics for homicide-suicide perpetrators, by race and ethnicity.</p

Availability of data for the national violent death reporting system, by state and year.

Availability of data for the national violent death reporting system, by state and year.</p

Hierarchical linear regression models predicting potential years of life lost among homicide victims (<i>N</i> = 98,617 persons, 93,629 incidents, 7,056 places).

Hierarchical linear regression models predicting potential years of life lost among homicide victims (N = 98,617 persons, 93,629 incidents, 7,056 places).</p

Life expectancy at birth, 2019, by race, ethnicity, and sex.

Life expectancy at birth, 2019, by race, ethnicity, and sex.</p

Hierarchical linear regression models predicting potential years of life lost among homicide-suicide perpetrators (<i>N</i> = 3,962 persons, 2,057 places).

Hierarchical linear regression models predicting potential years of life lost among homicide-suicide perpetrators (N = 3,962 persons, 2,057 places).</p