ResearchPad - clustering-coefficients Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Familial assimilation in transmission of raw-freshwater fish-eating practice leading to clonorchiasis]]> Clonorchiasis is caused by the ingestion of raw freshwater fish, which contains the infective larvae of Clonorchis sinensis. It is highly endemic in Asia, where about 15 million people are afflicted. To establish sustainable control strategy, the transmission of raw-eating practice needs to be illuminated. In this study, we conducted a survey in school students from four clonorchiasis endemic provinces in China, covering 23,222 students aged 9–18. The characteristics of raw-eating practice, impact of parents’ raw-eating practice on children, interaction of spouses’ practice was explored. It is demonstrated that raw-eating practice presents familial clustering, which is higher in those families with older children and with boys. Raw-eating practice in children is highly influenced by their parents’ raw-eating practice especially when both parents do. Additionally, there exists interaction between spouses’ raw-eating practice. The impact of husband’s raw-eating practice on his wife is higher than that of wife’s raw-eating practice on her husband. Familial assimilation dominates the transmission of raw-freshwater eating-practice, including the assimilation from parents to their children and that between spouses. This finding indicates the adoption of sustainable strategy against clonorchiasis through blocking raw-freshwater fish-eating practice.

<![CDATA[Normalization enhances brain network features that predict individual intelligence in children with epilepsy]]>

Background and purpose

Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function.

Materials and methods

Patients with epilepsy and resting state fMRI were retrospectively identified. Brain network nodes were defined by anatomic parcellation, first in patient space (nodes defined for each patient) and again in template space (same nodes for all patients). Whole-brain weighted graphs were constructed according to pair-wise correlation of BOLD-signal time courses between nodes. The following metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Metrics computed on graphs in patient space were normalized to the same metric computed on a random network of identical size. A machine learning algorithm was used to predict patient IQ given access to only the network metrics.


Twenty-seven patients (8–18 years) comprised the final study group. All brain networks demonstrated expected small world properties. Accounting for intrinsic population heterogeneity had a significant effect on prediction accuracy. Specifically, transformation of all patients into a common standard space as well as normalization of metrics to those computed on a random network both substantially outperformed the use of non-normalized metrics.


Normalization contributed significantly to accurate subject-level prediction of cognitive function in children with epilepsy. These findings support the potential for quantitative network approaches to contribute clinically meaningful information in children with neurological disorders.

<![CDATA[Hierarchical patient-centric caregiver network method for clinical outcomes study]]>

In clinical outcome studies, analysis has traditionally been performed using patient-level factors, with minor attention given to provider-level features. However, the nature of care coordination and collaboration between caregivers (providers) may also be important in determining patient outcomes. Using data from patients admitted to intensive care units at a large tertiary care hospital, we modeled the caregivers that provided medical service to a specific patient as patient-centric subnetwork embedded within larger caregiver networks of the institute. The caregiver networks were composed of caregivers who treated either a cohort of patients with particular disease or any patient regardless of disease. Our model can generate patient-specific caregiver network features at multiple levels, and we demonstrate that these multilevel network features, in addition to patient-level features, are significant predictors of length of hospital stay and in-hospital mortality.

<![CDATA[Network-based features enable prediction of essential genes across diverse organisms]]>

Machine learning approaches to predict essential genes have gained a lot of traction in recent years. These approaches predominantly make use of sequence and network-based features to predict essential genes. However, the scope of network-based features used by the existing approaches is very narrow. Further, many of these studies focus on predicting essential genes within the same organism, which cannot be readily used to predict essential genes across organisms. Therefore, there is clearly a need for a method that is able to predict essential genes across organisms, by leveraging network-based features. In this study, we extract several sets of network-based features from protein–protein association networks available from the STRING database. Our network features include some common measures of centrality, and also some novel recursive measures recently proposed in social network literature. We extract hundreds of network-based features from networks of 27 diverse organisms to predict the essentiality of 87000+ genes. Our results show that network-based features are statistically significantly better at classifying essential genes across diverse bacterial species, compared to the current state-of-the-art methods, which use mostly sequence and a few ‘conventional’ network-based features. Our diverse set of network properties gave an AUROC of 0.847 and a precision of 0.320 across 27 organisms. When we augmented the complete set of network features with sequence-derived features, we achieved an improved AUROC of 0.857 and a precision of 0.335. We also constructed a reduced set of 100 sequence and network features, which gave a comparable performance. Further, we show that our features are useful for predicting essential genes in new organisms by using leave-one-species-out validation. Our network features capture the local, global and neighbourhood properties of the network and are hence effective for prediction of essential genes across diverse organisms, even in the absence of other complex biological knowledge. Our approach can be readily exploited to predict essentiality for organisms in interactome databases such as the STRING, where both network and sequence are readily available. All codes are available at