9Allocation and management of agricultural land is of emergent concern due to land scarcity, diminishing 10 supply of energy and water, and the increasing demand of food globally. To achieve social, economic 11 and environmental goals in a specific agricultural land area, people and society must make decisions 12 subject to the demand and supply of food, energy and water (FEW). Interdependence among these 13 three elements, the Food-Energy-Water Nexus (FEW-N), requires that they be addressed concertedly.14 Despite global efforts on data, models and techniques, studies navigating the multi-faceted FEW-N 15 space, identifying opportunities for synergistic benefits, and exploring interactions and trade-offs in 16 agricultural land use system are still limited. Taking an experimental station in China as a model 17 system, we present the foundations of a systematic engineering framework and quantitative decision-18 making tools for the trade-off analysis and optimization of stressed interconnected FEW-N networks. 19 The framework combines data analytics and mixed-integer nonlinear modeling and optimization meth-20 ods establishing the interdependencies and potentially competing interests among the FEW elements 21 in the system, along with policy, sustainability, and feedback from various stakeholders. A multi-22 objective optimization strategy is followed for the trade-off analysis empowered by the introduction 23 of composite FEW-N metrics as means to facilitate decision-making and compare alternative process 24 and technological options. We found the framework works effectively to balance multiple objectives 25 and benchmark the competitions for systematic decisions. The optimal solutions tend to promote the 26 food production with reduced consumption of water and energy, and have a robust performance with 27 alternative pathways under different climate scenarios. 28 (Efstratios N. Pistikopoulos ), jie.li-2@manchester.ac.uk (Jie Li )Agricultural land is the largest ecosystem to provide food for human (Ellis & Ramankutty, 2008). 32 Agricultural production accounts for ∼30% of the global energy consumption, ∼92% of the human 33 water footprint, and over 20% of global greenhouse gas emissions (Alexandratos et al., 2012; Sims, 34 2011). The Food and Agricultural Organization (FAO) estimates a ∼60% increase of food demand 35 (compared with that of 2005/2007) for feeding 9.7 billion people by 2050, but the contribution of 36 cropland expansion to the increase is expected to reduce from 14% to 10% due to environmental reasons 37 at that time (Alexandratos et al., 2012; Ramankutty et al., 2018). Several countries, particularly in 38 the Near East/North Africa and South Asia, have already reached or are close to the limits of land 39 resource (FAO, 2009). Thus, there is an increasing pressure to meet the food demand of current and 40 future human populations with limited land expansion while minimizing the consumption of energy 41and water and conserving the environment. 42Typically, agricultural food production is a w...

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.

BackgroundIdentifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.ResultsAmong function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.ConclusionsIntegrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1205-4) contains supplementary material, which is available to authorized users.

BackgroundMining novel breast cancer genes is an important task in breast cancer research. Many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. However, different types of data often contain varying degrees of noise. For effective data integration, it’s important to design methods that work robustly with respect to noise.ResultsGene Ontology (GO) annotations were often utilized in cancer gene mining works. However, the vast majority of GO annotations were computationally derived, thus not completely accurate. A set of genes annotated with breast cancer enriched GO terms was adopted here as a set of source data with realistic noise. A novel noise tolerant approach was proposed to rank candidate breast cancer genes using noisy source data within the framework of a comprehensive human Protein-Protein Interaction (PPI) network. Performance of the proposed method was quantitatively evaluated by comparing it with the more established random walk approach. Results showed that the proposed method exhibited better performance in ranking known breast cancer genes and higher robustness against data noise than the random walk approach. When noise started to increase, the proposed method was able to maintained relatively stable performance, while the random walk approach showed drastic performance decline; when noise increased to a large extent, the proposed method was still able to achieve better performance than random walk did.ConclusionsA novel noise tolerant method was proposed to mine breast cancer genes. Compared to the well established random walk approach, it showed better performance in correctly ranking cancer genes and worked robustly with respect to noise within source data. To the best of our knowledge, it’s the first such effort to quantitatively analyze noise tolerance between different breast cancer gene mining methods. The sorted gene list can be valuable for breast cancer research. The proposed quantitative noise analysis method may also prove useful for other data integration efforts. It is hoped that the current work can lead to more discussions about influence of data noise on different computational methods for mining disease genes.

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