Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based. In contrast, industrial applications of these sensors have primarily targeted large wastewater treatment plants (WWTPs), leaving a gap in research for small-scale WWTPs. This paper demonstrates the suitability of using a field-based online UV-Vis sensor combined with advanced data analytics for CWMSs as an early warning for process upset to support sustainable operations. An industry case study is provided to demonstrate the development of surrogate monitoring parameters for total suspended solids (TSSs) and chemical oxygen demand (COD) using the UV-Vis spectral data from an online UV-Vis sensor. Absorbances at a wavelength of 625 nm (UV625) and absorbances at a wavelength of 265 nm (UV265) were identified as surrogate parameters to measure TSSs and COD, respectively. This study contributes to the improvement of WWTP performance with a continuous monitoring system by developing a process monitoring framework and optimization strategy.

Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers.

Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework (https://github.com/zhangjunpeng411/SCOM/) and the resulting web-based database SCOMdb (https://comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers.

Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data.

Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this article, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV-based causal effect estimators.

Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables.

Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of causal effects of treatment on the outcome or generate a unique estimation of the causal effect but making strong assumptions on data and having low efficiency. In this article, we identify a problem setting with the Cause Or Spouse of the treatment Only (COSO) variable assumption and propose an approach to achieving a unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications.

Multilabel Feature Selection: A Local Causal Structure Learning Approach.

Multilabel feature selection plays an essential role in high-dimensional multilabel learning tasks. Existing multilabel feature selection approaches mainly either explore the feature-label and feature-feature correlations or the label-label and feature-feature correlations. A few of them are able to deal with all three types of correlations simultaneously. To address this problem, in this article, we formulate multilabel feature selection as a local causal structure learning problem and propose a novel algorithm, M2LC. By learning the local causal structure of each class label, M2LC considers three types of feature relationships simultaneously and is scalable to high-dimensional datasets as well. To tackle false discoveries caused by the label-label correlations, M2LC consists of two novel error-correction subroutines to correct those false discoveries. Through local causal structure learning, M2LC learns the causal mechanism behind data, and thus, it can select causally informative features and visualize common features shared by class labels and specific features owned by an individual class label using the learned causal structures. Extensive experiments have been conducted to evaluate M2LC in comparison with the state-of-the-art multilabel feature selection algorithms.

Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories.

The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver.

Causal Feature Selection With Dual Correction.

Causal feature selection methods aim to identify a Markov boundary (MB) of a class variable, and almost all the existing causal feature selection algorithms use conditional independence (CI) tests to learn the MB. However, in real-world applications, due to data issues (e.g., noisy or small samples), CI tests can be unreliable; thus, causal feature selection algorithms relying on CI tests encounter two types of errors: false positives (i.e., selecting false MB features) and false negatives (i.e., discarding true MB features). Existing algorithms only tackle either false positives or false negatives, and they cannot deal with both types of errors at the same time, leading to unsatisfactory results. To address this issue, we propose a dual-correction-strategy-based MB learning (DCMB) algorithm to correct the two types of errors simultaneously. Specifically, DCMB selectively removes false positives from the MB features currently selected, while selectively retrieving false negatives from the features currently discarded. To automatically determine the optimal number of selected features for the selective removal and retrieval in the dual correction strategy, we design the simulated-annealing-based DCMB (SA-DCMB) algorithm. Using benchmark Bayesian network (BN) datasets, the experimental results demonstrate that DCMB achieves substantial improvements on the MB learning accuracy compared with the existing MB learning methods. Empirical studies in real-world datasets validate the effectiveness of SA-DCMB for classification against state-of-the-art causal and traditional feature selection algorithms.

Identifying preeclampsia-associated genes using a control theory method.

Preeclampsia is a pregnancy-specific disease that can have serious effects on the health of both mothers and their offspring. Predicting which women will develop preeclampsia in early pregnancy with high accuracy will allow for improved management. The clinical symptoms of preeclampsia are well recognized, however, the precise molecular mechanisms leading to the disorder are poorly understood. This is compounded by the heterogeneous nature of preeclampsia onset, timing and severity. Indeed a multitude of poorly defined causes including genetic components implicates etiologic factors, such as immune maladaptation, placental ischemia and increased oxidative stress. Large datasets generated by microarray and next-generation sequencing have enabled the comprehensive study of preeclampsia at the molecular level. However, computational approaches to simultaneously analyze the preeclampsia transcriptomic and network data and identify clinically relevant information are currently limited. In this paper, we proposed a control theory method to identify potential preeclampsia-associated genes based on both transcriptomic and network data. First, we built a preeclampsia gene regulatory network and analyzed its controllability. We then defined two types of critical preeclampsia-associated genes that play important roles in the constructed preeclampsia-specific network. Benchmarking against differential expression, betweenness centrality and hub analysis we demonstrated that the proposed method may offer novel insights compared with other standard approaches. Next, we investigated subtype specific genes for early and late onset preeclampsia. This control theory approach could contribute to a further understanding of the molecular mechanisms contributing to preeclampsia.

Time to infer miRNA sponge modules.

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

miRspongeR 2.0: an enhanced R package for exploring miRNA sponge regulation.

Summary: MicroRNA (miRNA) sponges influence the capability of miRNA-mediated gene silencing by competing for shared miRNA response elements and play significant roles in many physiological and pathological processes. It has been proved that computational or dry-lab approaches are useful to guide wet-lab experiments for uncovering miRNA sponge regulation. However, all of the existing tools only allow the analysis of miRNA sponge regulation regarding a group of samples, rather than the miRNA sponge regulation unique to individual samples. Furthermore, most existing tools do not allow parallel computing for the fast identification of miRNA sponge regulation. Here, we present an enhanced version of our R/Bioconductor package, miRspongeR 2.0. Compared with the original version introduced in 2019, this package extends the resolution of miRNA sponge regulation from the multi-sample level to the single-sample level. Moreover, it supports the identification of miRNA sponge networks using parallel computing, and the construction of sample-sample correlation networks. It also provides more computational methods to infer miRNA sponge regulation and expands the ground truth for validation. With these new features, we anticipate that miRspongeR 2.0 will further accelerate the research on miRNA sponges with higher resolution and more utilities. Availability and implementation: http://bioconductor.org/packages/miRspongeR/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.

BACKGROUND: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. RESULTS: In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk. CONCLUSIONS: To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis.

BACKGROUND: Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. RESULTS: In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. CONCLUSIONS: The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge.

MOTIVATION: Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. RESULTS: We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

Computational methods for cancer driver discovery: A survey.

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a "one-stop" reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.

Stoichiometry Dependence of Physical and Electrochemical Properties of the SnOx Film Anodes Deposited by Pulse DC Magnetron Sputtering.

A batch of Sn oxides was fabricated by pulse direct current reactive magnetron sputtering (pDC-RMS) using different Ar/O2 flow ratios at 0.3 Pa; the influence of stoichiometry on the physical and electrochemical properties of the films was evaluated by the characterization of scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray reflection (XRR), X-ray photoelectron spectroscopy (XPS) and more. The results were as follows. First, the film surface transitioned from a particle morphology (roughness of 50.0 nm) to a smooth state (roughness of 3.7 nm) when Ar/O2 flow ratios changed from 30/0 to 23/7; second, all SnOx films were in an amorphous state, some samples deposited with low O2 flow ratios (≤2 sccm) still included metallic Sn grains. Therefore, the stoichiometry of SnOx calculated by XPS spectra increased linearly from SnO0.0.08 to SnO1.71 as the O2 flow ratios increased, and the oxidation degree was further calibrated by the average valence method and SnO2 standard material. Finally, the electrochemical performance was confirmed to be improved with the increase in oxidation degree (x) in SnOx, and the SnO1.71 film deposited with Ar/O2 = 23/7 possessed the best cycle performance, reversible capacity of 396.1 mAh/g and a capacity retention ratio of 75.4% after 50 cycles at a constant current density of 44 µA/cm2.

pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers.

MOTIVATION: Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level. RESULTS: We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. AVAILABILITY AND IMPLEMENTATION: pDriver is available at https://github.com/pvvhoang/pDriver. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes.

MOTIVATION: microRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using 'pseudotime' concept have inspired us to develop a pseudotime-based method to infer the miRNA-mRNA relationships characterizing a biological process by taking into account the temporal aspect of the process. RESULTS: We have developed a novel approach, called pseudotime causality, to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition, a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilizing the pseudotemporal information from the data helps reveal the gene regulation in a biological process much better than using the static information. AVAILABILITY AND IMPLEMENTATION: R scripts and datasets can be found at https://github.com/AndresMCB/PTC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DriverGroup: a novel method for identifying driver gene groups.

MOTIVATION: Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. RESULTS: We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. AVAILABILITY AND IMPLEMENTATION: DriverGroup is available at https://github.com/pvvhoang/DriverGroup. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data.

As a medicine safety issue, Drug-Drug Interaction (DDI) may become an unexpected threat for causing Adverse Drug Events (ADEs). There is a growing demand for computational methods to efficiently and effectively analyse large-scale data to detect signals of Adverse Drug-drug Interactions (ADDIs). In this paper, we aim to detect high-quality signals of ADDIs which are non-spurious and non-redundant. We propose a new method which employs the framework of Bayesian network to infer the direct associations between the target ADE and medicines, and uses domain knowledge to facilitate the learning of Bayesian network structures. To improve efficiency and avoid redundancy, we design a level-wise algorithm with pruning strategy to search for high-quality ADDI signals. We have applied the proposed method to the United States Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) data. The result shows that 54.45% of detected signals are verified as known DDIs and 10.89% were evaluated as high-quality ADDI signals, demonstrating that the proposed method could be a promising tool for ADDI signal detection.