Systems Bioinformatics: An Engineering Case-Based Approach

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My personal favorites are in bold. Let me know, if anything is missing and needs to be added here. Jump to Menu. Bioinformatics Books Here is a list of books on Bioinformatics which I compiled. Updated hourly. Rashidi, Lukas K. Baxevanis, B. Malcolm Campbell and Laurie J. Wesley Hatfield and Wesley G. Kohane, Alvin Kho, Atul J. Ewens, Gregory R.

The authors further provided evidence that expression of all components of the tissue-specific disease module was necessary for determining the disease outcome. The construction of this tissue-specific disease network further allowed for predictions on novel disease—tissue relationships. The attack process is usually based on specific mathematical models that decide which nodes-edges or even specific hubs to remove, to examine issues like controllability [ 76 ], error tolerance [ 77 ], attack vulnerability [ 77 , 78 ], robustness [ 79 , 80 ], topological characteristics [ 81 ] and control centrality [ 82 ].

Cascade attacks [ 83 ], degree and betweenness-based attacks and prominence-based attacks are commonly used types of attacks [ 84 ]. It has been shown that intentional attacks as well as random failures may easily affect or even destroy network functions such as connectivity and synchronization [ 84 , 85 ], while a small range of node failures can affect the network controllability [ 86 ]. In the case of Systems Bioinformatics there are relatively limited, yet of great interest, studies that have used such an approach [ 87 ]. Control theory shows that to direct a complex network towards a desired state, there is a minimum number of driving nodes.

Determining the minimum number of driving nodes can be demanding with respect to both computational resources and time, hence novel non-exhaustive algorithms for determining driving nodes are necessary. A recently published study [ 88 ] presents the actuation spectrum method that optimizes the trade-off between driving node prediction and time. The authors validate their methodology across numerous complex networks and show that a small number of driving nodes are sufficient to determine the state of a complex network. Another approach makes use of PPI networks and network controllability.

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By controlling the structure of human PPI networks, using the correct queues or inputs, it is possible to activate specific cellular processes that determine disease outcome i. The authors quantified the effects of removing a specific protein from the network by calculating the number of remaining driving nodes. Results showed that the most important proteins according to this analysis were also the primary targets of disease-causing mutations, human viruses and drugs. This study showed that controllability of a network can provide crucial information for the shift between healthy and disease states, at the same time highlighting novel candidate drug targets.

A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers Figure 2. Different disease modules have been shown to act in synergy. Thus, to obtain a holistic picture of the complex mechanisms that underlie disease manifestation, it is necessary to construct networks of integrated disease modules. Such an integration can be achieved via various ways: i By investigating gene association it is possible to construct connections between different disease modules by looking at shared, common genes.

This can reflect the genetic basis of diseases and provide associations between diseases of a common genetic background. SNPs, indels. Networks of disease modules sharing genetic mutations can lead to important findings such as establishment of linkage and associations of variants as well as environmental factors with disease modules. Disorders that affect specific metabolic pathways i.

Specific cellular components or modules associated with a disease are believed to share a topological neighbourhood within the human interactome [ 90 ]. In a recent study [ 90 ] the authors utilized novel mathematical conditions to map the topological relationships between diseases in the human interactome.

They showed that diseases with common expression profiles, symptoms and comorbidity share overlapping modules in contrast to more phenotypically distinct diseases, which appear in distant topological neighbourhoods. These tools can provide valuable insights in predicting drug therapy for diseases with common phenotypes, even if they are genetically distinct. Another recent study [ 91 ] adopted a novel, multiple-network-framework integration for epigenetic modules.

The authors of this study also showed that epigenetic modules also estimate the survival time of patients, and this factor is critical for cancer therapy. Tools that analyse the structure and topology of these integrated networks are of extreme value and can provide insights into the synergistic role of multiple network components in diseases of interest. The methods for network analysis and integration we have discussed so far are mainly used to describe the topology of a biological network or a set of networks.

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Although these methods capture the relationships between components, they fail to capture the dynamics, i. For example, static insights into the molecular basis of a disease do not provide a complete picture with regards to drug response without access to time-dependent data. Hence, the use of mathematical algorithms and computational tools for modelling the dynamics of these networks complements network analysis and is further detailed in the following section. To test the validity and predict the behaviour of complex biochemical systems, such as gene networks, it is often required to describe the effects of multiple, simultaneous and dynamic interactions within the components of the system that are too complex to interpret intuitively.

Developing and simulating mathematical models is essential in investigating such complex biological systems. These complex systems can be further explored using mathematical models to describe the valid structure i. Quantitative models use differential equations to describe the non-linear dynamic interactions in a network, whereas logical models use the Boolean approach to describe dynamics in a qualitative way.

Quantitative models provide precise information and can be directly compared with experiments including time-dependent data. However, they require sufficient knowledge of the mechanistic details and kinetic parameters and, thus, they are limited to applications to networks which are well characterized and are of small to moderate size. Logical models do not require such information and can be applied to large-scale networks with known structure, yet only provide limited information, as they cannot provide quantitative predictions and assist in choosing better alternative behaviours.

In summary, each modelling approach has its advantages and disadvantages and recent work suggests that hybrid approaches might be optimal for challenges in systems biology for a detailed discussion see [ 93 ]. Mathematical models are indispensable in pharmacology and diagnostics. For example, spatio-temporal mathematical models of the blood coagulation network have been developed to aid drug development and diagnostics as extensively reviewed in [ 95 ].

Another relevant application is the use of mathematical models of drug-targeted pathways modelled with a set of differential equations based on the mass action law to explore drug combinations [ 96 ]. Classical bioinformatics and systems biology can complement and strengthen each other in drug discovery and therapeutics where concrete predictions are required [ 92 ].

The value of combining high-throughput data with mathematical modelling is shown, for example, in devising personalized treatments in cancer for extensive review see [ 97 ]. Integration of multi-omic data can be used as an additional constraint in constraint-based modelling in systems biology to optimize parameter estimation and validation. For a recent survey summarizing constraint-based metabolomic modelling and multi-omic integration methods see [ 98 ].

It is important to highlight some of the modern computing trends that play an important role in driving research in Systems Bioinformatics and facilitate the transition to personalized medicine. The main limiting factor for research laboratories specializing in Systems Bioinformatics is computational power and resources. Significant investment is required to attain high-performance computer HPC servers or clusters, which have the capacity to store, manage and process the vast amount of data generated from high-throughput omics technologies.

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Often sheer maintenance of these machines can be a costly and a limiting factor that disallows the exploitation of the full potential of HPC. Cloud computing promises to solve major issues of system administration for these computer clusters by allowing for the exploitation of HPC, stored and managed in an expert environment, as virtual resources that are made available through the internet. The use of networks in computational diagnostics via the detection of molecular biomarkers is one of the hallmarks of Systems Bioinformatics.

Numerous recent state-of-the-art studies have made use of such networks to characterize cellular systems by simultaneously analysing thousands of genes, proteins, isoforms and complexes to address issues of computational diagnostics. A recent study [ 62 ] used a machine learning approach, which integrates topological features from PPI networks, to identify candidate AD-associated genes. The positive data set consisted of genes known to be associated with AD.

These features included nine topological properties of the PPI network for each gene, namely, the average shortest path length, betweenness centrality, closeness centrality, clustering coefficient, degree, eccentricity, neighbourhood connectivity, topological coefficient and radiality. Findings and significance of research : The authors further combined sequence features and functional annotations features and concurrently performed feature selection using seven methods including gain-ratio-based attribute evaluation, oneR algorithm, chi-square-based selection, correlation-based selection, information gain-based attribute evaluation and relief-based selection.

The most important features were fed into 11 machine learning algorithms to generate classifiers using the training data set capable of predicting AD- and non-AD-associated genes using the selected network, sequence and functional features. Training of sophisticated machine learning classifiers using systemic properties can be a key feature in generating personalized medicine diagnostic approaches.

The authors finally combined diagnostics with therapeutics by screening 45 known anti-Alzheimer drugs from DrugBank against novel predicted probable AD targets, obtained from their trained classifiers, using molecular docking. They further proposed a novel candidate untried drug, AL, with high affinity to potential therapeutic targets.

Another interesting study [ ] used data from TCGA [ ] to successfully construct a multidimensional subnetwork atlas for cancer prognosis. The authors addressed how multiple genetic and epigenetic factors i. Network analysis : The authors fitted a univariate Cox proportional hazards model between each molecular feature and patient survival time and thus scored each gene based on its significance to predict survival.

Genes with a positive score were considered as survival-related genes. They next used this score heat score as the input into HotNet2, which uses a heat diffusion process and a statistical test-based algorithm to discover subnetwork signatures in the PPI network. Through this way subnetwork signatures of survival-related genes were determined both by the scores of their genes as well as gene topology in the PPI network. Findings and significance of research : The authors then used Monte Carlo cross-validation and permutation testing procedure to assess predictive power of the subnetworks on patient overall survival.

They used a Cox proportional hazards model with L1 penalized log partial likelihood LASSO for feature selection to train the models based on the molecular profile of individual subnetworks. Finally, the prognostic outcomes for the training set were used to determine the regression coefficients. These coefficients were then used in the testing model to predict outcomes for patients in the test set and calculate the concordance index C-index. Results reveal novel PPI subnetworks with significant prognostic capabilities for a variety of cancer types.

The authors further validated their subnetworks by performing prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumour stratification and independent validation. They highlighted distinct pathways in the underlying subnetworks as potential new targets for therapeutic intervention for certain cancer types. This study integrated the protein interactome with cancer genomics data, thus allowing for a systemic analysis of the molecular mechanisms that underlie genesis of cancer and provides new directions in personalized cancer therapy [ ].

Another recent study [ ] adopted an approach that uses an enriched library of single-stranded oligodeoxynucleotides to profile complex biological samples. This method allows for the analysis of systemic native biomolecules. The authors defined their method as Adaptive Dynamic Artificial Poly-ligand Targeting and further utilized it as a diagnostic tool to profile plasma exosome of cancer patients. They achieved high classification accuracy in breast cancer patients by analysing the circulating exosomes in their blood [ ].

The online database MelGene is yet another example of successful integration of Systems Bioinformatics approaches in current research for molecular diagnostics. This tool provides a comprehensive, regularly updated collection of data from genetic association studies in cutaneous melanoma, including random-effects meta-analysis results of all eligible polymorphisms [ ]. The MelGene proposed network connections highlight potentially new loci in relation to melanoma risk.

Recent studies have shown that interpretation of proteomics data using network-based approaches can offer additional insights into the mechanistic and dynamics of protein assemblies, and hence into the molecular mechanisms underlying the system under study.

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Moreover, network-based approaches can be used to reconstruct a disease-perturbed cellular network model showing the interactions of identified differentially expressed proteins involved in selected cellular pathways related to the target pathophysiology. For example, Shirasaki et al. In particular, using a monoclonal antibody against huntingtin Htt , they identified proteins to be complexed with Htt.

A systems-level view of Htt interactome was achieved by using WGCNA, which was used to construct weighted links between the Htt co-purifying proteins. Using topological overlap, the data were clustered into eight Htt-interactome modules that were related to distinct functional aspects such as brain region specificity, aging and protein aggregation modulation or Htt functions directly [ ].

Moreover, several network-based approaches have been developed that can identify the cellular pathways which are altered under pathophysiological conditions, and can hence enrich biomarker discovery. Furthermore, pathway topology approaches have been developed as alternative to enrichment analysis. For example, Signalling Pathway Impact Analysis [ ] and Network Perturbation Amplitude [ ] deliberate whether the proteins involved in functional modules defined by other databases interact with each other in cellular networks. Various tools are currently available, which can aid the Systems Bioinformatics application in biomarker discovery.

GWAB, a recent tool, makes use of systems approaches and computational methods to boost weak association signals for Genome Wide Association Studies GWAS , a common problem when analysing this type of data. This tool works by incorporating publicly available data in the form of using GWAS summary statistics p -values for SNPs along with reference genes for a disease of interest.

Systems Bioinformatics contributes in computational therapeutics by providing tools and algorithms for novel drug discovery. Research in this direction is often done in close collaboration with pharmaceutical companies. One of the main challenges faced by both the research community and the industry is the prediction of adverse drug effects, especially during the early stages of drug development.

These types of predictions can lead to significant cost reductions by allowing for accurate drug assessment and discontinuation of development for drugs with severe adverse effects. The use of human genetic variation has been known to play an important role in drug response [ ]; however, the effect of this factor alone is not sufficient to provide a complete perspective on the matter in hand.

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Systems pharmacology is a term that is widely used today in many high-calibre, recently published studies [ — ]. It shifts away from traditional practice, which considers the effects of a drug with respect to its target protein and instead strives to address the effects of the drug by considering a network of drug—target interactions.

Systems Bioinformatics is a precious field in the neighbourhood of systems pharmacology that provides important methods and tools for multi-source and multilevel integration of the omics spectrum with drug networks shedding light in the area of modern drug discovery. A recent area of great interest where Systems Bioinformatics can be of substantial impact and value is the area of drug repurposing or repositioning [ ].

This entails the use of Food and Drug Administration FDA -approved drugs to treat new diseases, which are different from the ones they were initially designed for. This allows for obvious shortcuts for pharmaceutical companies allowing them to by-pass the timely and costly process of FDA approval for novel drugs. Recent studies used gene expression data derived from microarrays or RNA-Seq data to obtain specific expression profiles for specific diseases of interest.

By comparing these to collections of data sets from repositories such as CMap [ ], Drugmap Central and more advanced versions like LINCS and the recent Drug Repurposing Hub [ , ] allows for alternative drugs to be proposed for the treatment of diseases under investigation. From a total of samples primary solid tumour samples and 61 primary solid normal samples— Network construction : The authors examined three major categories of statistical network inference methods: i mutual information-based methods, ii correlation-based methods and iii tree-based methods.

They further utilized Biological information-based network methods and one ensemble scheme using all statistical network inference methods. They used the Cytoscape platform and more specifically the GeneMania plug-in to reconstruct the biological information-based gene network. This plug-in uses a large data set unifying functional networks comprising approximately networks for six organisms including Homo sapiens. Using the H. Network analysis : The authors further performed gene re-ranking using the underlying networks.

To investigate the influence of the reconstructed 17 gene networks 12 statistically and 5 biologically inferred on gene prioritization, they applied a method that allows for a custom network selection combining the log fold change absolute values with the selected underlying network topology to re-rank the initial DEGs. The basic idea of the method is the reconciliation of the gene expression values taking into account the underlying gene network topological features such as degree and betweenness.

The network patterns were further analysed to investigate their exclusive contribution with respect to breast cancer subtypes and stages. In summary, the authors obtained 63 unique drugs for the breast cancer stages and 58 for the breast cancer subtypes. To further examine the resulting drugs, the authors constructed super networks by combining top drugs extracted from their analysis with the FDA-approved breast cancer drugs, connecting them with their target genes and superimposing these on the gene expression networks.

These patterns were shown to highlight four exclusive stage-related pathways including phenylalanine metabolism for Stage II, peroxisome proliferator-activated signalling pathway and glycolysis and gluconeogenesis for Stage III and toll-like receptor signalling pathway for Stage IV.

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Systems Bioinformatics: An Engineering Case-Based Approach [Gil Alterovitz, Marco F Ramoni] on *FREE* shipping on qualifying offers. Editorial Reviews. About the Author. Gil Alterovitz is on the faculty at Harvard Medical School, where he is engaged in research that applies engineering systems.

Finally, the authors performed drug repurposing to elucidate potential anti-breast-cancer properties for known drugs and they compared the molecular structure for their predicted re-purposed drugs against 25 FDA-approved drugs of clinical use. Two out of these 25 drugs Gemcitabine and Palbociclib were also found as repurposed drugs by the authors.

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In Stage I, two repurposed drugs, Clofarabine and Kinetin-riboside, were found to be structurally similar to Gemcitabine. Clofarabine seems to have potential efficacy in epigenetic therapy of solid tumours, especially at early stages of carcinogenesis. Another recent line of work [ 64 ] performed network-based in silico drug efficacy screening by exploiting network-based approaches. The authors investigated the association between drug targets and diseases, presenting a drug—disease proximity measure [ 64 ].

Network construction : The human PPI network was compiled using information extracted from the databases described above, to generate an elaborate human interactome. Entrez Gene IDs were used to map disease-associated genes to the corresponding proteins in the interactome. Network analysis : The proximity between a disease and a drug was evaluated using various distance measures that take into account the path lengths between drug targets and disease proteins.

Based on these results the authors showed that network proximity delineates therapeutic effects of a drug. This approach of utilizing network proximity in the interactome for drug targets and diseases, allowed for increased understanding in the therapeutic effect of drugs. This approach can potentially have significant applications in drug discovery, drug repurposing and assessment of drug adverse effects.

Another study [ ] led to the development of a current state-of-the-art tool that addresses computational therapeutics from a network perspective, the TCM-Mesh system. Network construction : The authors used Cytoscape as well as a web-based software to facilitate visualization of a compound—gene—disease network construction between TCM and treated diseases. Network analysis : The authors based their network analysis and scored their compounds using the combined score as defined and obtained from the STITCH database.

This score represents the strength of the links between the compounds and their associated proteins. Findings and significance of research : The authors used FDA-approved drugs, as well as compounds from a herbal material Panax ginseng and a patented drug Liuwei Dihuang Wan for evaluating their database. By comparison of different databases, as well as checking against literature, they demonstrated the completeness, effectiveness and accuracy of the TCM-Mesh database and further aided in increased understanding of the molecular mechanisms of TCM action.

Various tools are currently available, which can aid the Systems Bioinformatics application in drug discovery. For a full list of tools and databases adopting or supporting Systems Bioinformatics methodologies, see Table 1. A more comprehensive list of related tools and databases going back to can be found in Supplementary Table S1. The concept of utilizing networks to visualize the complex interaction of mechanisms implicated in disease has been around for several years.

However, two important breakthroughs separate previous network-based approaches and are currently driving the state-of-the-art in Systems Bioinformatics: i construction of multiple networks representing each level of the omics spectrum and the integration of these in a layered network that exchanges information within and between layers [ 62 , 67 , 97 ] and ii the advent of novel techniques and methodologies for analysing and understanding these networks using mathematical algorithms and approaches derived from graph theory and information theory [ 6 ].

Using these methods for extracting biologically meaningful information from multiple levels of the omics spectrum can provide the integrated systemic knowledge for the development of a comprehensive Human System profile, which increases diagnostic accuracy and concurrently allows for novel therapeutic advances and assess response to therapy.

An Engineering Case-based Approach

Learning-based approaches have classically used bag-of-words representations see Section 4. A popular approach to this utilizes knowledge about the gene and the context in which the gene is mentioned. Basic network measures can be used to analyse the components of a network, both locally and globally, and facilitate the analysis and extraction of useful information from a biological network. This study showed that controllability of a network can provide crucial information for the shift between healthy and disease states, at the same time highlighting novel candidate drug targets. There please forever 60s clinical hormones, which is as it should Sound after 9 streams. For example, in a recent study, a network-based analysis of mass-spectrometry MS -based proteomics data of spinal nerves led to the identification of 19 biological processes to be involved in retrograde motoneurodegeneration and neuroprotection after axonal damage [ 31 ]. In the medical domain, summarization has been applied to clinical notes, journal articles, and a variety of other input types.

Nevertheless, the network-based approaches, either for evidence-based or for statistically inferred molecular networks, have a number of limitations. Specifically, networks based on experimental evidence are not complete as experiments are only a snapshot of the real biological world. Moreover, statistically inferred networks represent an undetermined computational problem because the number of the inferred relationships is much larger than the number of the independent measurements [ ]. Owing to the lack of sufficient ground truth to validate the reconstructed molecular networks, special attention must be given when choosing benchmark data sets e.

To this extent, the DREAM Dialogue on Reverse Engineering Assessments and Methods initiative facilitates researchers from the Systems Bioinformatics field to assess the validity of the networks they are using and proceed with optimization and parameter-tuning regarding network reconstruction [ ]. A subsequent limitation of the network-based approaches is the low overlap that the various network reconstruction approaches have, and the inadequacy in selecting the proper network each time. It is likely that computational approaches checking and exploiting complementarities and providing ensemble solutions of a network construction consensus will maximize the information content.

In our opinion, Systems Bioinformatics might currently appear rather aspirational, yet, considering its potential it is likely to have a major impact on medicine and pharmacology in the next decade. The field of medicine is expected to benefit from the invaluable knowledge attained from Systems Bioinformatics methodologies. The molecular basis of complex, polygenic diseases is highly heterogeneous and affected by multiple factors simultaneously. Although it might not be possible to replace the use of traditional approaches for therapeutics and diagnosis with computational methods, yet, it is likely that Systems Bioinformatics will provide revolutionary approaches and tools to clinicians in order to demystify the complex nature of these diseases.

Computational diagnostics and therapeutics, enhanced by Systems Bioinformatics approaches, will not only aid clinicians in patient consultation and care but will also catalyse significant breakthroughs in prognostic measures, detection of disease at an early onset and overall disease prevention.

The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics as a holistic and systems approach in investigating complex biological systems. The key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers.

The network approach in Systems Bioinformatics comes with limitations including the lack of ground truth for the constructed biological network. Systems Bioinformatics methods can enhance computational therapeutics and diagnostics hence, paving the way to precision medicine. Marilena M. George M. Oxford University Press is a department of the University of Oxford.

It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents. Systems bioinformatics applications.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches Anastasis Oulas. Oxford Academic. Google Scholar. George Minadakis. Margarita Zachariou. Kleitos Sokratous. Marilena M Bourdakou.

George M Spyrou. Box , Nicosia, Cyprus. E-mail: georges cing. Cite Citation. Permissions Icon Permissions. Abstract Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. Systems Bioinformatics , precision medicine , computational diagnostics , computational therapeutics , network analysis , drug repurposing.

Figure 1. Open in new tab Download slide. Figure 2. Figure 3. Table 1. Open in new tab. Search ADS. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory P4 medicine. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. Network efficiency and posterior alpha patterns are markers of recovery from general anesthesia: a high-density electroencephalography study in healthy volunteers. Retained executive abilities in mild cognitive impairment are associated with increased white matter network connectivity.

PageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow. Towards a proteome-scale map of the human protein-protein interaction network. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions.

Assessment of network inference methods: how to cope with an underdetermined problem. Network-based proteomic approaches reveal the neurodegenerative, neuroprotective and pain-related mechanisms involved after retrograde axonal damage. TRED: a transcriptional regulatory element database, new entries and other development.

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples. Google Preview.

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KEGG for integration and interpretation of large-scale molecular data sets. Discovering gene re-ranking efficiency and conserved gene-gene relationships derived from gene co-expression network analysis on breast cancer data. A gene-coexpression network for global discovery of conserved genetic modules.

ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. Information-theoretic inference of large transcriptional regulatory networks.

From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. Regularized estimation of large-scale gene association networks using graphical Gaussian models. Inferring regulatory networks from expression data using tree-based methods. Integrative approaches for finding modular structure in biological networks. Toward repurposing metformin as a precision anti-cancer therapy using structural systems pharmacology.

Towards a pathway definition of Parkinson's disease: a complex disorder with links to cancer, diabetes and inflammation. A systems biology pipeline identifies new immune and disease related molecular signatures and networks in human cells during microgravity exposure. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.