Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

Population analysis reveals the roles of DNA methylation in tomato domestication and metabolic diversity

DNA methylation is an important epigenetic marker, yet its diversity and consequences in tomato breeding at the population level are largely unknown. We performed whole-genome bisulfite sequencing (WGBS), RNA sequencing, and metabolic profiling on a population comprising wild tomatoes, landraces, and cultivars. A total of 8,375 differentially methylated regions (DMRs) were identified, with methylation levels progressively decreasing from domestication to improvement. We found that over 20% of DMRs overlapped with selective sweeps. Moreover, more than 80% of DMRs in tomato were not significantly associated with single-nucleotide polymorphisms (SNPs), and DMRs had strong linkages with adjacent SNPs. We additionally profiled 339 metabolites from 364 diverse accessions and further performed a metabolic association study based on SNPs and DMRs. We detected 971 and 711 large-effect loci via SNP and DMR markers, respectively. Combined with multi-omics, we identified 13 candidate genes and updated the polyphenol biosynthetic pathway. Our results showed that DNA methylation variants could complement SNP profiling of metabolite diversity. Our study thus provides a DNA methylome map across diverse accessions and suggests that DNA methylation variation can be the genetic basis of metabolic diversity in plants.

Integrating multiomics data accelerates elucidation of plant primary and secondary metabolic pathways

Plants are the most important sources of food for humans, as well as supplying many ingredients that are of great importance for human health. Developing an understanding of the functional components of plant metabolism has attracted considerable attention. The rapid development of liquid chromatography and gas chromatography, coupled with mass spectrometry, has allowed the detection and characterization of many thousands of metabolites of plant origin. Nowadays, elucidating the detailed biosynthesis and degradation pathways of these metabolites represents a major bottleneck in our understanding. Recently, the decreased cost of genome and transcriptome sequencing rendered it possible to identify the genes involving in metabolic pathways. Here, we review the recent research which integrates metabolomic with different omics methods, to comprehensively identify structural and regulatory genes of the primary and secondary metabolic pathways. Finally, we discuss other novel methods that can accelerate the process of identification of metabolic pathways and, ultimately, identify metabolite function(s).

Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation‐based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.

Plasma metabolites were associated with spatial working memory in major depressive disorder

Major depressive disorder (MDD) is a common disease with both affective and cognitive disorders. Alterations in metabolic systems of MDD patients have been reported, but the underlying mechanisms still remains unclear. We sought to identify abnormal metabolites in MDD by metabolomics and to explore the association between differential metabolites and neurocognitive dysfunction.Plasma samples from 53 MDD patients and 83 sex-, gender-, BMI-matched healthy controls (HCs) were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was then used to detect metabolites in those samples. Two different algorithms were applied to identify differential metabolites in 2 groups. Of the 136 participants, 35 MDD patients and 48 HCs had completed spatial working memory test. Spearman rank correlation coefficient was applied to explore the relationship between differential metabolites and working memory in these 2 groups.The top 5 metabolites which were found in sparse partial least squares-discriminant analysis (sPLS-DA) model and random forest (RF) model were the same, and significant difference was found in 3 metabolites between MDD and HCs, namely, gamma-glutamyl leucine, leucine-enkephalin, and valeric acid. In addition, MDD patients had higher scores in spatial working memory (SWM) between errors and total errors than HCs. Valeric acid was positively correlated with working memory in MDD group.Gamma-glutamyl leucine, leucine-enkephalin, and valeric acid were preliminarily proven to be decreased in MDD patients. In addition, MDD patients performed worse in working memory than HCs. Dysfunction in working memory of MDD individuals was associated with valeric acid.

Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

BiClusO: A Novel Biclustering Approach and Its Application to Species-VOC Relational Data

In this paper, we propose a novel biclustering approach called BiClusO. Biclustering can be applied to various types of bipartite data such as gene-condition or gene-disease relations. For example, we applied BiClusO to bipartite relations between species and volatile organic compounds (VOCs). VOCs, which are emitted by different species, have huge environmental and ecological impacts. The biosynthesis of VOCs depends on different metabolic pathways which can be used to categorize the species. A previous study related to the KNApSAcK VOC database classified microorganisms based on their VOC profiles, which confirmed the consistency between VOC-based and pathogenicity-based classifications. However, due to limited data, classification of all species in terms of VOC profiles was not performed. In this study, we enriched our database with additional data collected from different online sources and journals. Then, by applying BiClusO to species-VOC relational data, we determined that VOC-based classification is consistent with taxonomy-based classification of the species. We also assessed the diversity of VOC pathways across different kingdoms of species.

Metabolomics Based Comparison on the Biomarkers between Panax Notoginseng and its Counterfeit Gynura Segetum in Rats

Background:

Because of the similar appearance of Gynura segetum and panax notoginseng, the patients often mistakenly use Gynura segetum as Panax notoginseng, which causes serious liver damage. There is no comparative study on the metabolism of Gynura segetum and Panax notoginseng in the literature. This study was conducted to compare the difference between Panax notoginseng and its counterfeit Gynura segetum by using metabolomics method.

Methods:

In this paper, an ultra performance liquid chromatography coupled to quadrupole time-offlight mass spectrometric(UPLC-Q/TOF/MS) were used to detect the type of endogenous metabolites in urine and plasma of three groups (normal group, ethanol extract of panax notoginseng, decoction of Gynura segetum respectively, and different multivariate statistical analysis methods were established.

Results:

In this experiment, main urine biomarkers were L-glutamate, L-methionine, cytidine, and Ltyrosine in the Panax notoginseng group, which are phytosphingosine, creatine and sphinganine in the Gynura segetum group. The plasma biomarkers identified in the Panax notoginseng group were arachidonic acid, L-tyrosine, linoleic acid, alpha-linolenoyl ethanolamide and lysoPC (15:0), and in the Gynura segetum group are L-arginine, L-valine, arachidonic acid and LysoPC(18:2(9Z,12Z)).

Conclusion:

There are significant difference between Panax notoginseng and Gynura segetum in biomarkers from the perspective of metabolomics in the body.

A scalable and effective rough set theory-based approach for big data pre-processing

A big challenge in the knowledge discovery process is to perform data pre-processing, specifically feature selection, on a large amount of data and high dimensional attribute set. A variety of techniques have been proposed in the literature to deal with this challenge with different degrees of success as most of these techniques need further information about the given input data for thresholding, need to specify noise levels or use some feature ranking procedures. To overcome these limitations, rough set theory (RST) can be used to discover the dependency within the data and reduce the number of attributes enclosed in an input data set while using the data alone and requiring no supplementary information. However, when it comes to massive data sets, RST reaches its limits as it is highly computationally expensive. In this paper, we propose a scalable and effective rough set theory-based approach for large-scale data pre-processing, specifically for feature selection, under the Spark framework. In our detailed experiments, data sets with up to 10,000 attributes have been considered, revealing that our proposed solution achieves a good speedup and performs its feature selection task well without sacrificing performance. Thus, making it relevant to big data.

Essential Oil Phytocomplex Activity, a Review with a Focus on Multivariate Analysis for a Network Pharmacology-Informed Phytogenomic Approach

Thanks to omic disciplines and a systems biology approach, the study of essential oils and phytocomplexes has been lately rolling on a faster track. While metabolomic fingerprinting can provide an effective strategy to characterize essential oil contents, network pharmacology is revealing itself as an adequate, holistic platform to study the collective effects of herbal products and their multi-component and multi-target mediated mechanisms. Multivariate analysis can be applied to analyze the effects of essential oils, possibly overcoming the reductionist limits of bioactivity-guided fractionation and purification of single components. Thanks to the fast evolution of bioinformatics and database availability, disease-target networks relevant to a growing number of phytocomplexes are being developed. With the same potential actionability of pharmacogenomic data, phytogenomics could be performed based on relevant disease-target networks to inform and personalize phytocomplex therapeutic application.

Analysis of Scientific Collaboration Networks among Authors, Institutions, and Countries Studying Adolescent Myopia Prevention and Control: A Review Article

BACKGROUND

Studies related to the prevention and control of myopia in adolescents have increased rapidly, but only a few have measured the levels of scientific collaboration among authors, institutions and countries in this field. Thus, in this study, we aimed to reveal the status and levels of scientific collaboration in this field.

METHODS

The research population included all published papers in the field of adolescent myopia prevention and control indexed in the Web of Science databases from 1997-2016. The co-authorship networks were drawn using SATI (Statistical Analysis Toolkit for Informetrics), Ucinet and VOS viewer (Visualisation of Similarities viewer). Active authors and some measures of co-author network, including degree centrality, closeness, betweenness, density and diameter, were also assessed.

RESULTS

Overall, 610 records were obtained, and a number of publications developed through an increase in different collaboration types, with cooperation among authors and institutions as the most apparent ones. The top ten active authors and institutions were identified. The density of cooperative networks of the top 70 authors and the first 69 institutions were 0.043 and 0.011, respectively, with corresponding diameters of five and six, respectively. Seven distinct clusters formed the cooperation network among 38 countries. The top three clusters were centered in China, the United States and Australia, also identified as the most productive countries.

CONCLUSION

The flow of information is slow and the collaboration among authors and institutions in the network are not close enough. Thus, multiple collaboration types should be encouraged in this field, especially among countries.

Exploring the Diversity of Plant Metabolism

Plants produce a huge array of metabolites, far more than those produced by most other organisms. Unraveling this diversity and its underlying genetic variation has attracted increasing research attention. Post-genomic profiling platforms have enabled the marriage and mining of the enormous amount of phenotypic and genetic diversity. We review here achievements to date and challenges remaining that are associated with plant metabolic research using multi-omic strategies. We focus mainly on strategies adopted in investigating the diversity of plant metabolism and its underlying features. Recent advances in linking metabotypes with phenotypic and genotypic traits are also discussed. Taken together, we conclude that exploring the diversity of metabolism could provide new insights into plant evolution and domestication.

Prediction of Medicinal Properties Using Mathematical Models and Computation, and Selection of Plant Materials

In any phytochemical drug discovery programme, one of the major issues is the appropriate selection of target plant species that may provide lead for new drug discovery and development. Conducting research without any working hypotheses may produce serendipitous discoveries, but the chances of success are much slimmer than any information-based targeted approach. Therefore, the plant selection process is extremely important for ensuring success. In recent years, there have been significant amounts of work involving applications of various mathematical modelling and computational techniques to predict medicinal properties of plants, and thus to provide information-based selection of plant materials for further studies aiming at potential drug discovery and development. This chapter presents an overview of methods and processes involved in plant selection by utilizing various mathematical modelling and computational techniques.

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

The Role of Soil Microorganisms in Plant Mineral Nutrition-Current Knowledge and Future Directions

In their natural environment, plants are part of a rich ecosystem including numerous and diverse microorganisms in the soil. It has been long recognized that some of these microbes, such as mycorrhizal fungi or nitrogen fixing symbiotic bacteria, play important roles in plant performance by improving mineral nutrition. However, the full range of microbes associated with plants and their potential to replace synthetic agricultural inputs has only recently started to be uncovered. In the last few years, a great progress has been made in the knowledge on composition of rhizospheric microbiomes and their dynamics. There is clear evidence that plants shape microbiome structures, most probably by root exudates, and also that bacteria have developed various adaptations to thrive in the rhizospheric niche. The mechanisms of these interactions and the processes driving the alterations in microbiomes are, however, largely unknown. In this review, we focus on the interaction of plants and root associated bacteria enhancing plant mineral nutrition, summarizing the current knowledge in several research fields that can converge to improve our understanding of the molecular mechanisms underpinning this phenomenon.

Multistage analysis method for detection of effective herb prescription from clinical data

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.

Leguminous Plants in the Indonesian Archipelago: Traditional Uses and Secondary Metabolites

Indonesia is one of the richest countries with respect to plants resources. People from various ethnic, language, and religious groups have used the plants as alternative medicines, health foods and beverages for hundreds of years. To establish modern application for these understudied plant resources, ethnopharmacological data from more than 40 leguminous plants in Indonesia, spanning the western to the eastern parts of the Indonesian archipelago, were reviewed. In particular, bioactive secondary metabolites, including flavonoids, were described in detail to promote research into these plants as functional foods, nutraceuticals, and medicines.

Visualizing the knowledge structure and evolution of big data research in healthcare informatics

BACKGROUND

In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field.

METHODS

To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future.

RESULTS

By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People's Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing).

CONCLUSION

This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions.

Informatics framework of traditional Sino-Japanese medicine (Kampo) unveiled by factor analysis

Kampo, an empirically validated system of traditional Sino-Japanese medicine, aims to treat patients holistically. This is in contrast to modern medicine, which focuses in principle on treating the affected parts of the body of the patient. Kampo medicines formulated as combinations of crude drugs are prescribed based on a Kampo-specific diagnosis called Sho (in Japanese), defined as the holistic condition of each patient. Therefore, the medication system is very complex and is not well understood from a modern scientific perspective. Here, we show the informatics framework of Kampo medication by multivariate factor analysis of the elements constituting Kampo medication. First, the variation of Kampo formulas projected by principal component analysis (PCA) indicated that the combination patterns of crude drugs were highly correlated with Sho diagnoses of Deficiency and Excess. In an opposite way, partial least squares projection to latent structures (PLS) regression analysis could also predict Deficiency/Excess only from the composed crude drugs. Secondly, to chemically verify the correlation between Deficiency/Excess and crude drugs, we performed mass spectrometry (MS)-based metabolome analysis of Kampo prescriptions. PCA and PLS regression analysis of the metabolome data also suggested that Deficiency/Excess could be theoretically explained based on the variation in chemical fingerprints of Kampo medicines. Our results show that factor analysis of Kampo concepts and of the metabolomes of Kampo medicines enables interpretation of the complex system of Kampo. This study will theoretically form the basis for establishing traditionally and empirically based medications worldwide, leading to systematically personalized medicine.

Toward a Literature-Driven Definition of Big Data in Healthcare

Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

Detection of herb-symptom associations from traditional chinese medicine clinical data

Background. Traditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients. We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data. Methods. To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure. To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations. Results. The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians. Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations. Conclusions. Symptoms play significant roles for the prescriptions of herb treatment. The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations.

Systems biology in the context of big data and networks

Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

Supervised clustering based on DPClusO: prediction of plant-disease relations using Jamu formulas of KNApSAcK database

Indonesia has the largest medicinal plant species in the world and these plants are used as Jamu medicines. Jamu medicines are popular traditional medicines from Indonesia and we need to systemize the formulation of Jamu and develop basic scientific principles of Jamu to meet the requirement of Indonesian Healthcare System. We propose a new approach to predict the relation between plant and disease using network analysis and supervised clustering. At the preliminary step, we assigned 3138 Jamu formulas to 116 diseases of International Classification of Diseases (ver. 10) which belong to 18 classes of disease from National Center for Biotechnology Information. The correlation measures between Jamu pairs were determined based on their ingredient similarity. Networks are constructed and analyzed by selecting highly correlated Jamu pairs. Clusters were then generated by using the network clustering algorithm DPClusO. By using matching score of a cluster, the dominant disease and high frequency plant associated to the cluster are determined. The plant to disease relations predicted by our method were evaluated in the context of previously published results and were found to produce around 90% successful predictions.