Systems Biomedicine Research Articles

Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

Towards Omni-Tomography—Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography

We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose “omni-tomography”, a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine.

An Overview of Membrane Computing

School of Computer and Information, Anqing Normal University, Anqing 246133, Anhui, ChinaMembrane computing is a new branch of natural computing whose aim is to abstract computingideas from the structure and the functioning of living cells, as well as from the cell cooperationin tissues, populations of cells and the brain. The obtained models are called P systems. Theresearch in this area developed very fast, both towards theory, such as Turing computability, lan-guage and automata theory (e.g., characterizations of Turing completeness), computational effi-ciency (polynomial solutions to NP-complete problems by trading off time to space) and applications(in system biology, bio-medicine, computer graphics, approximate optimization, robot control, cryp-tography, ecology and economics modeling, etc.). There are three main classes of P systems: (1)cell-like P systems inspired from living cells, (2) tissue-like P systems inspired from tissues, and(3) neural-like P systems inspired from neural systems. In this paper, we briefly introduce cell-likeand tissue-like P systems, and then discuss a class of neural-like P systems, called spiking neuralP systems, in details: the formal definition of the system is firstly introduced, and some results ofthe systems are recalled in two aspects: computational completeness and computational efficiency.

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

BackgroundThe use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment.ResultsOur findings demonstrate that a miRNA’s functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set.ConclusionsWe describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.

Meeting report: BioVision – a sumptuous table of food‐for‐thought

Meeting report: <i>BioVision</i> – a sumptuous table of food‐for‐thought

Systems Biology: Evolving into the Mainstream

Systems approaches to biology are steadily widening their reach, but the road to integration and acceptance has been fraught with skepticism and technical hurdles. Interdisciplinary research teams at systems biology centers around the globe are working to win over the critics.

Prognostic transcriptional association networks: a new supervised approach based on regression trees

Motivation: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains.Results: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine.Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/.Contact: inepomuceno@us.esSupplementary information: Supplementary data are available at Bioinformatics online.

A machine learning pipeline for quantitative phenotype prediction from genotype data

BackgroundQuantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered.MethodsThe core element in the pipeline is the L1L2 regularization method based on the naïve elastic net. The method gives at the same time a regression model and a dimensionality reduction procedure suitable for correlated features. Model and SNP markers are selected through a DAP originally developed in the MAQC-II collaborative initiative of the U.S. FDA for the identification of clinical biomarkers from microarray data. The L1L2 approach is compared with standard Support Vector Regression (SVR) and with Recursive Jump Monte Carlo Markov Chain (MCMC). Algebraic indicators of stability of partial lists are used for model selection; the final panel of markers is obtained by a procedure at the chromosome scale, termed ’saturation’, to recover SNPs in Linkage Disequilibrium with those selected.ResultsWith respect to both MCMC and SVR, comparable accuracies are obtained by the L1L2 pipeline. Good agreement is also found between SNPs selected by the L1L2 algorithms and candidate loci previously identified by a standard GWAS. The combination of L1L2-based feature selection with a saturation procedure tackles the issue of neglecting highly correlated features that affects many feature selection algorithms.ConclusionsThe L1L2 pipeline has proven effective in terms of marker selection and prediction accuracy. This study indicates that machine learning techniques may support quantitative phenotype prediction, provided that adequate DAPs are employed to control bias in model selection.

A molecular systems biomedicine approach to Diabetes: characterization, prioritization and network analysis of new candidate genes from the apoptotic machinery

A molecular systems biomedicine approach to Diabetes: characterization, prioritization and network analysis of new candidate genes from the apoptotic machinery

Integrative biology — a strategy for systems biomedicine

The precision of genome sequences, together with advanced computational approaches, allows complex, clinically relevant biological systems to be examined. Here, I describe the experiences of the Genome Institute of Singapore (GIS) in using genome-to-systems strategies to accelerate biomedical research. To maximize clinically relevant output, we have explored an organizational strategy that encourages coordinated experimentation among investigators with diverse skills and interests through building a culture of integrative science. Our experience suggests that systems biomedicine is a real and potentially fruitful strategy for translational research.

Human disease classification in the postgenomic era: A complex systems approach to human pathobiology

Contemporary classification of human disease derives from observational correlation between pathological analysis and clinical syndromes. Characterizing disease in this way established a nosology that has served clinicians well to the current time, and depends on observational skills and simple laboratory tools to define the syndromic phenotype. Yet, this time-honored diagnostic strategy has significant shortcomings that reflect both a lack of sensitivity in identifying preclinical disease, and a lack of specificity in defining disease unequivocally. In this paper, we focus on the latter limitation, viewing it as a reflection both of the different clinical presentations of many diseases (variable phenotypic expression), and of the excessive reliance on Cartesian reductionism in establishing diagnoses. The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non-reductionist approach of systems biomedicine.

Hall David L, McMullen Sonya AH: Mathematical Techniques in Multisensor Data Fusion

Data fusion has been a trend in the field of imaging and signal/image analysis. Although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. With this second edition, the authors have been successful in updating us with state-of-the-art methods and techniques in multisensor data fusion. The book involves both algorithms and software tools, and also covers contemporary subjects like smart agents, cognitive aides, and so on. This book is written according to the Joint Directors of Laboratories (JDL) data fusion group model. There are five levels in the JDL model. The first level deals with association, correlation, estimation, and identification in the data domain. The second and third levels perform knowledge-based processing and utilize expert systems. The fourth level is focused on process monitoring and optimization. The fifth level is devoted to human computer interaction. Chapter 1 serves as an overview. Then, Chapter 2 introduces the JDL model and associated algorithms. Chapters 3–6 cover processing techniques at level one. Chapter 7 gives methods at levels two and three. Chapter 8 targets the control of sensor and information resources at level four. Chapter 9 is for data fusion at level five. Chapter 10 discusses implementation of fusion systems. Chapters 11 and 12 describe emerging applications and information management. The book contains 100 equations, 75 illustrations and key references. The typesetting quality is generally excellent but it would be better if some labels in the figures have been put in larger size. Note that the additons in this book include materials on fusion system control, DARPA's TRIP model, and applications in data warehousing, medical equipment, and defense systems. Hall (associate dean for research, Pennsylvania State University School of Information Sciences and Technology) and McMullen (captain, US Air Force) are well known experts in the field, and should be congratulated for accomplishing such an excellent job in summarizing the up-to-date essential ideas and results on multisensor fusion. Overall, the book is very informative and not difficult to read for electrical and computer engineers as well as technical managers. These types of practitioners can gain solid advice from the book regarding selection of data fusion methods, balance of trade-offs among commercial off-the-shelf tools, development of multisensor data fusion systems and their applications to solve real-world problems. However, to build a sophisticated data fusion system one may need other references for more comprehensive mathematical theories, more rigorous statistical treatments, and more technical details. The cited literature in the book represents appropriate pointers for that purpose. A good example for further reading is the Handbook [1]. As an engineer in biomedical imaging, I would like to underline the relevance of this book to research on systems biomedicine. Given the completion of the Human Genome Project [2] and the momentum of the Physiome Project [3], in 2003 the NIH defined its Roadmap to guide our efforts towards most important biomedical research, including studies on biological building blocks, pathways, and networks using molecular libraries, imaging, bioinformatics and computational biology. Consequently, multi-modality signal sensing and imaging plays a critical role, which often necessarily generates heterogeous sparse/huge datasets that are multi-dimensional, multi-spectral, dynamic, noisy and/or incomplete. The data fusion techniques discussed in this book are closely related to that for biomedical image registration/fusion. Major challenges and new opportunities are enormous ahead of us. In that context, I hope this book will have a significant impact on the biomedical imaging areas, and recommend it to biomedical imaging researchers in particular and other interested engineers in general.

Obesity as a culture-bound syndrome.

Although the term "culture-bound syndrome" has been used for many years, a concise definition has not been available. The less precise synonym "folk illness" has implied that such syndromes exist only in other cultures. This paper provides a four-part definition to permit examination and comparison of disease categories in any system, including biomedicine. Anthropologists have tended to view biomedicine as the standard for comparison, and have not examined it in the same critical light as other systems. This may be due in part to a confusion of the biomedical classificatory system (biomedicine per se, emic level) with the biological data on which in is based (etic level). One can in fact retain use of the biological data while analyzing biomedicine, which is understood to include cultural components. Mild-to-moderate obesity in the U.S. today fits the proposed definition of a culture-bound syndrome. This paper offers a brief overview of the evidence that culture has shaped both the definition of the disease over time and its treatment.