Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201

Similarity based learning method for drug target interaction prediction

In silico prediction of drug target interactions has gained its popularity with the growth of publicly available information in chemical and biological sciences. The old paradigm of \u27one drug-one target\u27 is quickly becoming outdated. It was smart way of understanding the drug-protein interactions but the biological systems we are dealing with are made up of myriad of proteins exhibiting multiple functions. To analyze and understand these systems as a whole, we require efficient computational models. In this work we have improved a machine learning method by integrating more correlated information about the drug compounds and extend this method to weighted profile method in order to infer novel interactions for drugs and targets with no prior interaction information, which was not possible with the current model. We have evaluated our method using area under the ROC curve and the results obtained show that the proposed model can predict drug target interactions accurately

Globalized bipartite local model for drug-target interaction prediction

In pharmacology, it is essential to identify the interactions between drug and targets to understand its effects. Supervised learning with Bipartite Local Model (BLM) recently has been shown to be effective for prediction of drug-target interactions by first predicting target proteins of a given known drug, then predicting drugs targeting a known protein. However, this pure "local" model is inapplicable to new drug or target candidates that currently have no known interactions. In this paper, we extend the existing BLM method by integrating a strategy for handling new drug and target candidates. Based on the assumption that similar drugs and targets have similar interaction profiles, we present a simple neighbor-based training data inferring procedure and integrate it into the frame work of BLM. This globalized BLM called bipartite local model with neighborbased inferring (BLMN) then has an extended functionality for prediction interactions between new drug candidates and new target candidates. Good performance of BLMN has been observed in the experiment of predicting interactions between drugs and four important categories of targets. For the Nuclear Receptors dataset, where there are more chances for the presented strategy to be applied, 20% improvement in terms of AUPR was achieved. This demonstrates the effectiveness of BLMN and its potential in prediction of drugtarget interactions