Motivation
Prediction of protein complexes from protein–protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction.
Results
To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks.
Availability and implementation
https://github.com/SaraOmranian/PC2P.
Supplementary information
Supplementary data are available at Bioinformatics online.
The increasing use of genetically modified (GM) foods and feeds attracts the interest of media and public, causing great concern among consumers about the consequences of their consumption. The issues of concern are mainly focused on the impact on consumer health and the repercussions on the environment. The biggest fears are the possible negative consequences on human and animal health, which encompass allergic reactions, side effects such as toxicity, damage to individual organs, gene transfer and differences in nutritional value. Consumers are unsure and confused as to whether consuming GM foods is harmful to their health or not. According to a Pew Research Center survey conducted between October 2019 and March 2020, 48% of respondents said GM foods are harmful, 13% responded GM foods are safe, while 37% of respondents could not express their opinion due to lack of knowledge about it. Numerous studies have been undertaken to examine the effects that GM foods and feeds exert on humans and animals. The results differ in many ways that issue numerous questions. In this paper, we will try addressing questions that concern the public, as well as the activities and measures that science and competent institutions are taking to confront them.
Background: Acrylamide (AA) is an important food contaminant resulted from Maillard reaction during thermal processing of carbohydrate rich food commodities. The present paper reports the data for the AA content in some types of thermally processed starch rich food, and assessment of dietary exposure for the population in North Macedonia.
Methods: The AA level was determined employing modified and validated ultra high performance liquid chromatography with tandem quadrupole detector. A total of 160 samples divided in seven most frequently consumed commodity groups were collected for determination of their AA content. Finally, chronic exposure of AA in the population was estimated. Statistical analysis was performed applying OriginPro 8 SR4 v8.0951 software package
Results: The average AA levels varied from 126.9±122.4 μg/kg for bread samples to 494.5±127.1 μg/kg for French fries samples. The dietary exposure of the population from North Macedonia for the tested food commodities was estimated at 0.643±0.171 μgAA/kgbw/day. The main contributor to the total AA intake was bread, with estimated value at 0.394±0.150 μgAA/kgbw/day. The margin of exposure values were 528 and 264, respectively for neurotoxicity and non-plastic effect calculated on average intake.
Conclusion: The risk assessment analysis revealed increased concern for human health regarding the neoplastic effects, especially for infants, toddlers, and adolescents. This is the first study related to AA presence in different food commodities in North Macedonia, and implies that monitoring programs and mitigation strategies must be implemented.
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