On increasing pressure, both UiO‐67 and UiO‐abdc were found to be incompressible when filled with methanol molecules within a diamond anvil cell. Stabilization in both cases is attributed to dynamical linker disorder. The diazo‐linker of UiO‐abdc possesses local site disorder, which, in conjunction with its longer nature, also decreases the capacity of the framework to compress and stabilizes it against direct compression, compared to UiO‐67, characterized by a large elastic modulus. The use of non‐linear linkers in the synthesis of UiO‐MOFs therefore creates MOFs that have more rigid mechanical properties over a larger pressure range.Whilst many metal–organic frameworks possess the chemical stability needed to be used as functional materials, they often lack the physical strength required for industrial applications. Herein, we have investigated the mechanical properties of two UiO‐topology Zr‐MOFs, the planar UiO‐67 ([Zr Metal–organic frameworks (MOFs) continue to be of exceptional interest to the scientific community because of their guest‐specific gas sorption, separation, drug‐delivery, and catalytic properties.While sometimes problematic for applications, structural flexibility does however give rise to a very rich and diverse array of pressure‐ and temperature‐induced mechanical responses in MOFs, which may be tuned to individual application needs.6O4(OH)4 nodes, which are interconnected by linear or bent dicarboxylate ligands.IV and 12‐fold coordination of the metal cluster are associated with high shear and bulk moduli, which surpass those of other MOFs.6O4(OH)4(bpdc)6] 6O4(OH)4(abdc)6] The isoreticular series of UiO‐type MOFs consists of Zrl‐proline to be a highly efficient modulator in the synthesis of Zr UiO‐MOFs.l‐proline and HCl (a known synthetic promoterFm3‾m(a≈26.85 Å and 29.32 Å respectively), and contain octahedral cages of diameter 16 Å (UiO‐67) and 19 Å (UiO‐abdc), which share faces with eight smaller tetrahedral pores of diameters 12 Å (UiO‐67) and 15 Å (UiO‐abdc).Synthetic conditions leading to single‐crystals of UiO‐type MOFs are rare and typically require the addition of a significant excess of monocarboxylic acid crystallization modulators.Room temperature single‐crystal diffraction data were collected to compare to our high‐pressure data at room temperature. For UiO‐67, some libration was observed on the bpdc ligand, while much larger ellipsoids and more disorder were apparent in the abdc ligand in UiO‐abdc. Phenyl rings in UiO‐abdc were modelled over three positions , while the diazo moiety was modelled over four positions. Both libration and disorder are unsurprising, as the ligands in both cases are bisected by mirror planes, whilst occupational disorder in abdc is ascribed to the lack of ligand mirror symmetry. Whilst the average structure is cubic and isostructural with UiO‐67, the local structure of the abdc ligand must break this symmetry Figure  d. Inter6O4(OH)4 core, and one of the six linker units in each case  and an average magnitude defined for both frameworks. Whilst the bpdc ligands in UiO‐67 remain approximately planar (〈|θ|〉=3(2)°), the geometric frustration in UiO‐abdc is accommodated by a more significant bowing of the ligands 〈|θ|〉=5(3)° . Motion of the six independent linker arms was followed by MD simulations, which revealed highly dynamic behavior. Atomic probability density functions (PDFs), analogous to thermal ellipsoid models in crystallographic refinements, were derived for the ZrTo investigate the effect of the higher flexibility and geometric frustration of abdc on the mechanical properties of the UiO framework, high‐pressure experiments were performed on both UiO‐67 and UiO‐abdc by loading suitable single‐crystals into modified Merrill–Bassett diamond anvil cells (DACs).K) were extracted from the experimental cell volume‐pressure relationships using EoS Fit .On increasing pressure using MeOH as a hydrostatic liquid, both UiO‐67 and UiO‐abdc expand initially at 0.16 and 0.19 GPa, respectively Figure . Such beOn initial pressurization using FC‐70  indirect evidence of guest inclusion can be observed due to an increase in compressibility observed in UiO‐67. On increasing pressure further, direct compression takes place in both frameworks. Unlike with MeOH, further increases in pressure are not accompanied by a plateau in cell volume. In fact, Bragg diffraction is lost from UiO‐67 at a relatively modest pressure of 0.3 GPa, yet UiO‐abdc undergoes a large change in unit cell volume of almost 10 % to 1.8 GPa, while remaining crystalline. Above 1.8 GPa, the quality of data resolution for UiO‐abdc was severely reduced, such that structural responses to increasing pressure could not be determined. The flexibility of the ligand in UiO‐abdc would, however, appear to impart a greater degree of resistance to increasing pressure whilst remaining crystalline.nd order Birch–Murnaghan equation of state  allowed the determination of a bulk modulus of 16.8 GPa for UiO‐abdc; a step in the compression curve for UiO‐67 in the 0.2–0.4 GPa range prevents us from making a similar calculation. This hint of a structural transition could be linked to the loss of crystallinity observed experimentally in the same pressure range. These results are similar to previous computational work performed on evacuated MOF‐5, which reported 5 % compression at 1 GPa and a bulk modulus of 16.52 GPa.φ) of the carboxylate functional group, which undergoes changes of up to 5° during compression of UiO‐abdc .To determine the compressibility of both frameworks without inclusion of the hydrostatic media, the mean atomic position structures obtained from the MD simulations were geometrically optimized by periodic DFT calculations, and then used as starting models for simulated hydrostatic compression in 0.2 GPa steps up to 1 GPa, thereby simulating direct compression experiments on guest‐free frameworks . Compressions in cell volume of approximately 4.0 % and 6.0 % were observed at 1 GPa, respectively, for UiO‐67 and UiO‐abdc. While a 2K, and that value is similarly inaccessible from DFT under compression calculations, we turned to DFT calculations in the elastic regime . From the elastic tensors obtained , the Voigt–Reuss bulk moduli calculated for UiO‐abdc is 15.2 GPa, in good agreement with the data above despite the very different methodology. The bulk modulus of UiO‐67 is slightly higher, at 17.4 GPa. Thus, the inclusion of the more flexible azobenzene‐based linker logically leads to a softer framework (higher compressibility), though the effect is quantitatively small. In comparison, the effect on the robustness or mechanical stability of the framework is much bigger: a six‐fold increase in its hydrostatic pressure at which it loses crystallinity.For UiO‐abdc, the values of bulk moduli calculated from experimental data and calculations are in good agreement, with values of 14.8 GPa and 16.8 GPa, respectively. Because the early onset of pressure amorphization for UiO‐67 in FC‐70 precludes determination of E, and hardness, H. Single‐crystal X‐ray diffraction was performed to establish Miller indices of the crystal facets . Using the load‐displacement data  gained during the indentation, E and H were calculated as a function of depth. The average values for each sample were calculated as E=20.02 GPa and H=1.27 GPa (UiO‐67), and E=13.24 GPa and H=0.65 GPa (UiO‐abdc). The trend seen in Young's moduli is in agreement with values derived from DFT calculations of elastic stiffness tensors, with UiO‐abdc again softer than UiO‐67 under uniaxial compression .To look at the response of the material under a different mechanical stimulation, we probed evacuated single crystals of UiO‐67 and UiO‐abdc by nanoindentation, to determine their Young's moduli, E and framework solvent accessible volume (SAV), which for UiO‐67 is only moderately high .3(C9O6H3)2] has a similar SAV of 64.3 %, yet an elastic modulus of just 9.3 GPa.3H3N2)2] (E=3.15 GPa) and MOF‐5 [Zn4O(C8H4O4)3] (E=9.5 GPa), having SAVs of 50.3 % and 77.7 %, respect‐ively.The elastic modulus of UiO‐67 is amongst the largest reported by nanoindentation for MOFs, and agrees with the low compressibility of the framework. The magnitude of this rigidity is, however, surprising given the empirical inverse relationship observed between The elastic modulus of UiO‐abdc is substantially lower than that of UiO‐67, which is in agreement with its higher SAV (71.8 %). It is interesting to note that this large decrease in rigidity is accompanied by a relatively small increase in SAV, whereas previous work on a different family of MOFs noted that changes in SAV of around 20 % would be required to elicit decreases in mechanical response of a similar order (ca. 40 %).E in each case lies above those of other highly porous MOFs, and indeed approaches the mechanical response expected of dense hybrid frameworks.In conclusion, the different mechanical behavior of two UiO‐type frameworks has been fully characterized by computational and experimental methodologies. Bulk and elastic moduli for both UiO‐67 and UiO‐abdc demonstrate mechanical robustness.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


Unravelling gene regulatory networks (GRNs) influenced by chemicals is a major challenge in systems toxicology. Because toxicant-induced GRNs evolve over time and dose, the analysis of global gene expression data measured at multiple time points and doses will provide insight in the adverse effects of compounds. Therefore, there is a need for mathematical methods for GRN identification from time-over-dose-dependent data. One of the current approaches for GRN inference is Time Series Network Identification (TSNI). TSNI is based on ordinary differential equations (ODE), describing the time evolution of the expression of each gene, which is assumed to be dependent on the expression of other genes and an external perturbation . Here, we present Dose-Time Network Identification (DTNI), a method extending TSNI by including ODE describing how the expression of each gene evolves with dose, which is supposed to depend on the expression of other genes and the exposure time. We also adapted TSNI in order to enable inclusion of time-over-dose-dependent data from multiple compounds. Here, we show that DTNI outperforms TSNI in inferring a toxicant-induced GRN. Moreover, we show that DTNI is a suitable method to infer a GRN dose- and time-dependently induced by a group of compounds influencing a common biological process. Applying DTNI on experimental data from TG-GATEs, we demonstrate that DTNI provides in-depth information on the mode of action of compounds, in particular key events and potential molecular initiating events. Furthermore, DTNI also discloses several unknown interactions which have to be verified experimentally.The online version of this article (doi:10.1007/s00204-016-1922-5) contains supplementary material, which is available to authorized users. A dedicated and robust analysis of human in vitro toxicogenomics data by means of computational methods is crucial for the development of alternatives to animal testing   linear regression; (2) pairwise scores; (3) nonlinear regression; (4) ordinary differential equations (ODE); and (5) Bayesian networks : the nuclear factor-kappa B (NF-kB) signalling pathway and the nuclear factor (erythroid-derived 2)-like 2 (NRF2) pathway.We furthermore demonstrate that heat maps of starting nodes (nodes with only outgoing edges) in networks inferred with DTNI at the earliest time point can be used to infer potential molecular initiating events (MIEs) within the adverse outcome pathway (AOP) framework . Therefore, we can permute the residuals instead of the data and add the permuted residuals to the data estimated from the ODE model  was performed by leaving out the data from one compound. This was repeated for each compound in the data set to create as many LOOCV data sets as there were compounds. DTNI was performed on each LOOCV data set. Interactions found by applying DTNI to the full data set that were not found for all LOOCV data sets were removed from the network.http://copasi.org/), a bioinformatics tool able to simulate time series data from an ODE model by solving its equations over a time interval of interest  a direct gene–gene interaction: two genes interacting without the intervention of other genes; (2) an indirect gene–gene interaction: two genes interacting through the intervention of one or more intermediate genes; (3) a true-positive interaction (TP): a correctly identified direct interaction; (4) a false-positive interaction (FP): an incorrectly identified direct interaction ; (5) a true negative (true absent interaction) (TN): absence of a direct interaction is correctly identified; and (6) a false negative  (FN): absence of a direct interaction is incorrectly identified (there is a direct interaction in the network extracted from BioModels).The following performance measures were used to check out the inferred networks: (1) sensitivity (true-positive rate (TPR)): the proportion of directly interacting gene pairs that are inferred as such; (2) specificity (true-negative rate (TNR)): the proportion of not directly interacting gene pairs that are inferred as such; (3) area under the receiver operator characteristics curve (AUROC): graph of TPR against the false-positive rate (FPR = 1 − TNR), a larger AUROC means a better prediction of the toxicant(s)-induced GRN; (4) geometric mean score (geometric mean of the sensitivity and the specificity), a larger geometric mean score means a better prediction of the toxicant(s)-induced GRN; and (5) positive predictive value (PPV): the proportion of direct interactions predicted by network inference that are true direct interactions.Apart from conducting a simulation study, we also validated DTNI in real experiments with compounds that were known to have an effect on the peroxisome proliferator-activated receptor (PPAR) signalling pathway, among other clofibrate. A detailed description of the experiments and their analysis is given in Supplementary Material 4 .http://consensuspathdb.org/) . GeneCards      by automated hypothesis generation in Biograph  .Prior to analysis, compounds were pre-selected based on full availability of data from doses, time points and both replicates. Raw files were then pre-processed using the R package affy  ≤0.05.The R/Bioconductor package LIMMA  lower than 0.05 were considered significant.From the pathway lists obtained by means of over-representation analysis in ConsensusPathDB, pathways with adjusted We selected the NF-kB and the NRF2 pathways as examples to evaluate DTNI, because these pathways are very well established in the literature, in comparison with other DILI-related pathways.The data of the compounds influencing the NF-kB pathway were analysed by applying DTNI. The NF-kB pathway was selected a priori because it is known to play an important role in toxicant-induced liver injury (He and Karin http://www.genome.jp/kegg/)  .Figure S3-2 compares DTNI with TSNI for simulated data of a single compound with three time points , three doses  and time-matched controls (dose = 0). ROC curves were produced by varying the Figures S3-3a and S3-3b show that increasing the number of time points or the number of doses leads to a better prediction of a toxicant-induced network . However, the curves of Figures S3-3a and S3-3b only slightly increase after having 72 time points and 14 doses, respectively. Figure S3-3c shows that the prediction of a toxicant-induced network increases with the number of compounds .The ROC curves in Figure S3-3d show that noise  significantly influences the performance of DTNI. However, as to be expected, having replicates reduces the influence of noise.Figure S3-4 shows that applying LOOCV significantly increases the proportion of direct interactions predicted by DTNI that are true direct interactions .For the real experiments of compounds affecting the PPAR signalling pathway, geometric mean scores of ~0.69 (69%) were obtained (see Table S3-6). Furthermore, the inferred network for clofibrate contains key genes for compound reactions, but also many other genes, of which most of them have already been related to clofibrate exposure in previous studies (see Table S3-7).Four compounds could be related to the NF-kB pathway upon over-representation analysis of the DEGs: acetaminophen, carbon tetrachloride, interleukin-1 and interleukin-6. The interaction network inferred by applying DTNI on this group of compounds consists of 25 nodes, connected by 21 edges , of which 14 connections (66.7%) are true-positive interactions, only 2 connections are false-positive interactions (9.5%) and 5 connections are unknown (23.8%). Details on the edges are presented in Table S4-1. Table S4-2 presents the list of pathways related to the inferred network. The network can be divided into 6 subnetworks . The inferred network and its subnetworks are related to apoptotic pathways  and inflammatory response pathways .Within the complete network of 25 nodes, 12 were starting nodes (with only outgoing edges). Figure S4-2 presents a heat map of these starting nodes at the earliest time point (2 h). TLR4, which plays a role in the inflammatory response : CFLAR-LTB, PLCG2-ICAM1 and TNFRSF11A-CD14. These gene pairs could be connected through intermediates involved in inflammatory response, cell–cell communication and immune response, respectively. The interaction involved in cell–cell communication could also be predicted from STRING (Table S4-6). One of the five gene pairs (LBP-TNFSF14) could only be connected in CTD by its interaction with acetaminophen (Tables S4-7 and S4-8). For the remaining unknown gene interaction (LTB → ICAM1), no evidence for a functional relationship could be found in the consulted databases.When DTNI analysis was repeated for 4 compounds not affecting the NF-kB pathway , none of the 21 edges in Figure S4-1 could be inferred, which shows that the network in Figure S4-1 is specific for compounds affecting the NF-kB pathway.By performing pathway analysis on the DEGs, we found ten compounds related to the NRF2 pathway: azathioprine, carbamazepine, coumarin, diazepam, flutamide, ketoconazole, lomustine, nitrofurantoin, propylthiouracil and valproic acid. DTNI, applied to this group of compounds, inferred an interaction network of 123 nodes, connected by 811 edges . Within the network, 196 connections (24.0%) are true-positive interactions, 23 are false-positive interactions (3.0%), and 592 connections are unknown (73.0%). Table S5-1 presents the details on the edges. The network has no subnetworks. Pathways related to the inferred network are listed in Table S5-2. The inferred network is related to solute carrier (SLC)-mediated transmembrane transport, biotransformation pathways (phase I/phase II), response to oxidative stress (e.g. degradation of reactive oxygen species), metabolism , aryl hydrocarbon receptor signalling, apoptosis, tumour protein p53 signalling, transforming growth factor beta signalling, gene regulation by peroxisome proliferators and ABC family proteins-mediated transport.Within the complete network of 123 nodes, 13 were starting nodes. The heat map in Figure S5-2 shows that SLC5A9 is most affected at the earliest time point (2 h). SLC5A9 plays a role in transporter activity and metabolism, in particular the carbohydrate metabolism . When restricting to the ten interactions in Biograph that have the highest interaction strength (determined by DTNI), we found that five gene pairs could only be related to each other because both genes react with NADP, water or oxygen (Table S5-5). Interaction between the other five gene pairs  could be explained by a common biological function between the genes (Table S5-5). SLC6A9-HSPA1A could be connected by intermediates involved in inflammatory response. MAFG-SLC39A7, SLC6A8-HSPA1A and their intermediates play a role in cell signalling. The interaction of TXN and HBEGF through EGFR is part of the immune system. PDGFB-NRG1 and its intermediates play a role in cell–cell communication.Thirteen interactions could be retrieved from STRING, of which 4 could also be inferred from Biograph (Table S5-8). One interaction was known from curated databases, two were experimentally determined, one gene pair was known to be co-expressed, and the remainder could be extracted with the help of text mining (Table S5-6). A total of 379 gene pairs could only be connected in CTD by their interaction with at least one of the 10 studied compounds (Tables S5-7 and S5-8). For the remaining 141 interactions, no evidence for a functional relationship could be found in the consulted databases.When DTNI analysis was repeated for 10 compounds not affecting the NRF2 pathway , 32 (3.9%) of the 811 edges in Figure S5-1 could be inferred, which shows that 96.1% of the network in Figure S5-1 is specific for compounds affecting the NRF2 pathway. Of the 32 edges in the overlap, 9 were true-positive interactions and 23 unknown in CPDB. The 9 true-positive interactions are relationships between genes from the NRF2 pathway that occur in other pathways, in particular xenobiotics metabolism by cytochrome P450 and transport pathways (Table S5-9). Of the 23 unknown interactions, none of the gene pairs has overlap in GO terms or an interaction in STRING. Only 4 of these 23 interactions could be inferred from known interactions in Biograph (Table S5-10). Three of the 32 interactions in the intersection have gene pairs that are affected by one of the 10 compounds (not affecting the NRF2 pathway) in CTD: TGFA-GCLC (both affected by diclofenac), TXN-HBEGF (both affected by diclofenac) and SLC7A11-ABCC3 (both affected by indomethacin).Unravelling the mechanism of action (MoA) of compounds is a challenging topic in toxicology. Previous methods for elucidating MoA by analysing gene expression data on multiple time points and doses, such as DeMAND  a direct gene–gene interaction; (2) an indirect gene–gene interaction (through one or more intermediates); (3) both genes affected by one or more of the studied compounds.The ROC curves for the simulations show that DTNI outperforms TSNI in inferring a network induced by a toxicant. Moreover, DTNI allows including data from multiple perturbations (compounds), while only one perturbation can be included within TSNI. Including multiple compounds has the additional advantage that a LOOCV step can be applied, leaving out one compound, which further improves the proportion of direct interactions predicted by network inference that are true direct interactions (PPV).In this study, we demonstrated the performance of DTNI on experimental data for two groups of compounds: those affecting the NF-kB pathway and those influencing the NRF2 pathway.The network inferred for the NF-kB pathway is specific for compounds affecting this pathway and cannot be inferred when analysing a group of compounds not influencing the NF-kB pathway. For the NRF2 pathway, most of the network (96.1%) was specific for compounds affecting that pathway. This shows that DTNI can infer a network specific for compounds affecting a common pathway for a moderate number (≤90) genes, but also that the performance in terms of inferring a network specific for a group of compounds slightly decreases when evaluating larger networks.In the first example, the NF-kB pathway, we found a network related to apoptosis and inflammation, which are key events in both the AOPs for drug-induced liver fibrosis and cholestasis Vinken . From thThe network inferred for the NRF2 pathway is involved in pathways which are associated with key events indicated in AOPs for fibrosis  or cholestasis  Vinken , or are For the NF-kB pathway, the largest percentage of edges in the network (66.7%) is true-positive interactions, while only 9.5% of the edges were false-positive interactions. This shows that DTNI is a reliable method for analysing a group of genes of moderate size (≤90 genes). Furthermore, 23.8% of unknown interactions could be discovered. Three of the five unknown interactions in CPDB could be predicted from known relationships in Biograph and/or STRING. One interaction could only be explained by the influence of one of the studied compounds on both genes. For the remaining interaction, no evidence could be found in the consulted databases.http://www.wikipathways.org/index.php/Pathway:WP2884). This could explain the large percentage of unknown interactions reported here. After further consulting of other biological databases , 72 of these interactions could be explained from known interactions in Biograph and/or STRING. For 379 interactions, the only evidence for an interaction was that at least one of the studied compounds affected both genes. For the remaining 141 interactions, no evidence could be extracted.For the larger group of genes of the NRF2 pathway, the percentage of true-positive interactions was still high (24%) compared to the number of false-positive interactions (3%). Interestingly, the largest part of the network (73%) represents unknown interactions. It has to be noted that on the Wikipathways website, it is reported that the NRF2 pathway is not fully connected and probably incomplete Supplementary material 2 (PDF 569 kb)Supplementary material 3 (PDF 378 kb)Supplementary material 4 (PDF 948 kb)Supplementary material 5 (PDF 734 kb)Supplementary material 6 (PDF 2325 kb)Below is the link to the electronic supplementary material.


Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signaling, immunity, and the cell cycle. We mapped a range of diseases onto this network and find that pathways associated with diseases tend to be functionally connected, highlighting the perturbed functions that result in disease phenotypes. We demonstrated that disease pathways cluster within the network. We then examined the distribution of cancer pathways and showed that cancer pathways tend to localize within the signaling, DNA processes and immune modules, although some cancer-associated nodes are found in other network regions. Altogether, we generated a high-confidence functional network, which avoids some of the shortcomings faced by conventional molecular models. Our representation provides an intuitive functional interpretation of cellular organization, which relies only on high-quality pathway and Gene Ontology data. The network is available at  Molecules within the cell interact to perform biological functions and disruption of these functions often results in disease . Molecular networks, such as protein-protein interaction networks, are used to map function; however, these models have limited ability to show certain aspects of biological complexity. For example, proteins may perform different interactions and have distinct functional roles under various cellular contexts or within different tissues. Molecular networks are confined to a single representation of each protein, prohibiting separation of functional roles and muddling the representation of these functions. We assign function to pathways rather than molecules, allowing proteins to be functionally represented within multiple pathways. The model is used to map diseases to their underlying disturbed cellular function. The conventional approach used to study function has been based on molecular interaction networks, which have improved our understanding of disease,4 infection,5 drug pharmacodynamics,6 and evolution.7 In this paper, we describe data and networks as “molecular” if they are concerned with interactions between individual biological molecules. This is in contrast to our focus on pathway-level representations, which represent pathway gene sets, with interactions between individual molecules subsumed into the “pathway nodes”. Pathways are considered to collectively participate in biological processes, the functions of individual genes or gene products are not represented.Cellular processes are carried out by groups of interacting proteins.3 Modular organization of function has been shown to exist across species, and is used to predict gene function.9 Similar networks have also been generated using co-expression data,7 genetic interaction data,10 and by combining data types.11 However, a disadvantage is that these networks contain false positive and false negative interactions, which may distort our understanding of functional organization.14There are various approaches for studying biological processes using molecular interaction networks. Protein–protein interaction (PPI) data is frequently used to construct networks, in which proteins are shown interacting with functionally related partners. This results in the emergence of functionally related sub-networks known as “functional modules”.17 This property is not captured in static networks, where interactions appear permanent in time. Proteins may participate in different functions, depending on the interactions they make in various cellular contexts19 and subcellular compartments,20 making representation of dynamic interactions critical for the accurate portrayal function.22 To capture the inherently temporal nature of molecular interactions, dynamic models incorporating additional data have been developed. For example, gene expression data have been mapped onto PPI networks to reflect the dynamic nature of protein interactions. Active sub-networks, defined as connected regions of the network that show altered gene expression under particular conditions, can then be identified.25 Additionally, longitudinally sampled data can be represented using multiple time series networks.15 An advantage of this approach is that refining the edges to those present at each time point produces modules that are smaller and more functionally specific.17 However, the use of gene expression data fails to capture interactions between proteins that do not have correlated expression.12 The correlation between gene expression and protein abundance is also weak.30In PPI networks, the edges link each protein to all of its known interacting partners. However, protein interactions are often dynamic, assembling when needed to perform a function, then disassembling.We suggest that the utilization of more reliable data could allow functional models to reach their full potential. In this work, we address the previous limitations by introducing a representation of cellular functions that uses pathways, rather than genes, as the constitutive elements. Pathways are comprised of sets of proteins (and complexes) that interact with each other serially, for example, to form signaling or metabolic pathways. This allows us to group sets of proteins known to interact under particular conditions. Although pathway data is based on molecular interactions within a specific cellular functional context, individual pathways do not include all the interactions that each protein participates in. Pathway data is considered to be more reliable than molecular data since it is based on a consensus reached by biochemists over an extended period of time and repeated experimentation. In this study we have excluded the individual molecular interactions and represented pathways as sets of proteins. As a consequence of this reduction in network complexity, the issues of individual false positive and negative PPIs are avoided, since individual molecular interactions are not represented in the network. The method also avoids gene expression data and the assumption that gene expression represents protein levels. In addition, the pathway model allows proteins to be represented independently in multiple pathways, separating pleiotropic functions. Finally, by simplifying the complexity of PPI networks to a smaller number of pathways, computational analysis becomes less demanding and more accessible.31 Cancer genes have been found to be especially highly connected within PPI networks,2 with different types of cancer forming highly connected overlapping modules.32 Our representation provides a higher-level view of the pathways and functions affected by disease, without the inaccuracies inherent in molecular-level interaction data.We present a human pathway network representing global biological function. By incorporating pathways from multiple data sources we aim to maximize functional coverage while minimizing the overlap between pathways. To assess the ability of our network to interpret disease functions, we mapped a broad range of disorders onto the network, before focusing more specifically on cancer. Disease pathway “modules” or clusters are known to form within molecular networks, showing overlap with functional modules.In order to generate a representation of biological processes based on pathways, we first selected a set of non-redundant, functionally annotated human pathways Fig. . The oriDiseases reflect perturbations of normal cellular functions. In order to represent diseases we first generated a network of pathways showing the cell in a normal, healthy state, then mapped disease genes onto the network. However, the data set contained pathways that were already representations of disease perturbations, and were therefore unsuitable for inclusion in the network. Specifically, disease pathways such as colorectal cancer, asthma and HIV infection were removed from the data set. Drug metabolism and addiction pathways also show the cell in an altered state, therefore drug metabolic pathways such as doxorubicin and statin pathways, and addiction pathways such as cocaine addiction were removed. A total of 484 pathways, with 225 pathways containing disease terms, 30 containing drug terms and 221 addiction terms were removed (see Methods). This only reduced the number of genes in the data set to 10,833.33 (GO) assigned a mean of 8.2 terms to each gene . Addition of parent terms increased the mean number of GO terms per gene to 75.3 . It was necessary to remove 1263 genes, as they did not have experimentally validated GO annotations, resulting in a loss of two pathways. Of the unannotated genes, 4.0% had no Biological Process annotations and 7.6% only had Biological Process annotations inferred from electronic annotation (IEA), which are considered less reliable. We removed 298 pathways with fewer than four annotated genes, as they were too small for enrichment analysis. Enrichment analysis returned at least one high confidence enriched Biological Process GO term  for 2514 out of the 2521 remaining pathways. Pathways without enriched GO terms were removed, as functional annotations were required to create edges in the network.The Gene Ontologyp-value threshold of 0.01. These enriched GO terms varied greatly in their significance and included many similar terms and parent terms. We aimed to generate a network that linked pathways based on the similarity of their enriched GO terms; however, GO terms assigned with low significance had the potential to make spurious connections or link pathways based on highly general terms. To address these issues we selected the most specific set of GO terms available to represent the genes in the pathway. We used the set cover for enrichment analysis algorithm  to select the most significant GO terms capable of covering the genes in each pathway [Stoney 2017 submitted], reducing the mean number of GO terms from 411.8 to 4.7 (standard deviation 4.2). These reduced functional profiles provide a precise representation of the pathways’ function without large numbers of similar GO terms or parent terms.Between 1 and 3459 enriched GO terms were assigned to each pathway , using the 34 We allowed the set cover algorithm to finish after 99.95% of the genes had been covered, reducing the number of pathways required to 1014. The only difference between this set cover and the set cover produced to cover 100% of genes was the absence of pathways “gene expression” and “metabolism”. This reduced the maximum pathway size from 1442 (metabolism) to 426 (“generic transcription pathways”), while resulting in the loss of only 4 genes.Next we selected a subset of pathways with reduced redundancy and minimal pathway size variability. Pathway size was controlled since the dataset included pathways with up to 2154 genes, which are unhelpful since they lack functional specificity. We used the proportional set cover algorithm (see “Reduction of redundancy between pathways” Section) to reduce redundancy while preferentially selecting pathways with sizes close to the median size of 23.35 without being undermined by excessive pathway overlap.Figure 36 The number of genes annotated with the lowest common ancestor term is used to calculate the probability that the GO terms were linked to the lowest common ancestor by chance. In contrast, the Wang method considers all the parent terms of both GO terms, and semantic similarity is calculated based on the proportion of parent terms that are shared by both terms.37 The influence of each parent term on the GO terms of interest is considered with greater weights attributed to close parent terms, and with “is-a” links being weighted more heavily than “part-of” links. We then compared the pairwise and best-match average methods for measuring distances between sets of GO terms (see “Measuring the semantic distance between GO sets” Section). To assess the suitability of each method, we identified the approach that gave the greatest difference between the semantic similarities of GO term pairs within pathways, compared to semantic similarities between different pathways . Semantically similar GO pairs are more frequent within pathways than between them, although the difference is small especially when using the Resnik method . The Resnik method measures the distance between two GO terms based on the lowest node shared by both terms within the Gene Ontology topology (referred to as the lowest common ancestor).hod Fig. .Fig. 3PaTo generate the pairwise average measure, we calculated the mean similarity between GO terms within each pathway and between each pair of pathways. This increases the distinction between semantic similarities observed between pathway nodes and within pathways. The difference is clearer when distances between GO terms are generated using the Wang measure Fig. , rather Figure 38 This is because rather than comparing each GO term to all available terms within each pathway or pathway pair, the best-match average is generated using the most similar GO term pairs. For example if “GO:1” and “GO:2” have a semantic similarity of 0, and are both allocated to “pathway x” and “pathway y”, the pairwise average method will assign an average similarity of 0.5, despite the pathways having identical terms. The best-match average would assign a more intuitive score of 1. The finding that the Wang method outperforms Resnik indicates that pathways are not being assigned a single semantic function but instead are enriched with multiple semantically different GO terms. Clusters of pathways are formed within the network when pathways share at least one function.The best-match average typically out-performs the pairwise method when unrelated annotations are allocated to the same pathway or gene.37 Therefore two identical generic terms would receive the same score as two highly specific child terms of the generic ancestor, despite their increased difference. The Wang measure considers all ancestral terms shared by two GO terms and reduces the score if the shared ancestors are distantly removed from the terms being compared.37 In this way it is better able to distinguish between pairs of general GO terms and pairs of distantly removed GO terms. For these reasons we generated the network using the Wang method in conjunction with the best match average method.The Wang method demonstrably out-performs the Resnik measure, in each recorded instance. To interpret these results, we note that the Resnik measure is based on the lowest common ancestor in the GO ontology capable of covering both GO terms. The score is calculated to describe the specificity of the lowest common ancestor, based on the number of genes associated with the term. A disadvantage of this approach is that it does not consider how far removed each GO term is from the common ancestor.We linked the pathways into a network based on shared functionality, represented by semantic similarity between GO terms. We used the Wang method to calculate functional semantic similarities between each pair of pathways, in order to generate a set of weighted network edges. Inclusion of all the edges generates a highly dense network reflecting the cross-talk between all biological processes, which impedes analysis and structural visualization of the network.41 the degree distribution of the pathway network did not follow a power law distribution (p < 0.05).To reduce the number of edges while preserving the underlying structure of the network, we removed weaker edges. To avoid disconnecting large numbers of pathway nodes from the network, we calculated the minimum edge weight threshold for reducing edges while retaining nodes. Using the best-match average technique the optimum threshold to provide the highest number of nodes with the lowest number of edges was 0.56, which conserved 987 nodes (97.1%) and 20,642 edges (4.0%). We used the minimum edge threshold to select a set of edges to construct the network. The resulting network was highly modular with a clustering coefficient of 0.593. Random networks generated to preserve the degree distribution had clustering coefficients ranging from 0.186 to 0.205 (mean 2.01), indicating that the pathway network is more modular than expected from chance. For the 987 nodes in the network, 974 were located within the largest connected component. Application of the Kolmogorov-Smirnov test revealed that, in contrast to many molecular networks,42Figure p-value threshold of 0.01. By focusing on diseases  rather than phenotypes  we capture the range of symptoms induced by disorders.We used enrichment analysis to assign 404 OMIM diseases to 219 pathways, using a p-value of <0.01.To test the hypothesis that disease nodes form highly linked disease pathway modules we measured the shortest paths between disease nodes. Figure p-value of <0.01. These were comprised of 39 types of cancer affecting a range of cell types. Many pathways were enriched for multiple cancer phenotypes (mean 3.3). The pathway associated with the most cancer types (17) was “extracellular vesicle mediated signaling in recipient cells”, which contains cancer causing genes including WNT, EGFR, RAF, NRAS, and KRAS, and is upstream of pivotal cancer pathways.43 Other pathways associated with high numbers of cancers were the “RAC1 PAC1 P38 MMP2 pathway” containing MAPK, ERK, KRAS, RAC, RAS genes and “copper homeostatis” which has been found to be relevant to multiple tumor types and is being trialed as a chemotherapy target.44We identified 166 pathways enriched with cancer genes at a p-value < 0.01). To assess whether the formation of disease clusters was unique to cancer we also tested pathway sets related to diabetes and obesity, disorders of the nervous system, immune system and cardiovascular system .To assess the claim that cancers cluster within particular network regions, we measured the shortest paths between cancer nodes within the network Fig. . The Koltem Fig. . In eachWe examined the distribution of cancer within the network. Figure 46 Correspondingly, within the network both cancers are found to be enriched in BMP signaling pathways, which have been shown to affect gastric inflammation.47 DNA repair, cell cycle, extracellular vascular mediated signaling and RAF activation pathways were frequently shared by multiple cancer types.The distribution of disease pathways within the network can indicate similarities and common risk factors between related distorders. To demonstrate this application, we present the distribution of gastorintestinal cancer pathways onto the network Fig. . Some pa48 Pathways are sets of interactions, which were manually curated to adopt the most appropriate shape for the data, therefore they represent coherent functions independently of the molecular topology.The use of molecular networks to study biological processes has been highly insightful. However, limitations with molecular interaction data and issues representing multi-functional genes make the development of alternative methods a necessity. We have constructed a functional network from existing pathway data and biological process annotations. The pathway network portrays a higher-level representation of the organization of biological processes, composed of functional pathway modules. Clustering methods used in molecular networks identify specific relationships in which each node shows a high density of interactions with all of the other nodes in the cluster. These methods are less suitable for identifying linear functional structures, in which chains of nodes interact without having a high clustering coefficient. Other studies have also approached the issue that network structures, other than clusters, may represent functional modules.4 identification of new disease-associated genes3 and indication of potential drug targets.49 However, gene mutations can be phenotypically diverse, such as AKT1, which is associated with schizophrenia, colorectal cancer, ovarian cancer and breast cancer.50 Further evidence suggesting that diseases may act independently within different pathways comes from the finding that many disease pairs with shared genes do not show significant co-morbidity.51 Phenotypically diverse genes may also interact with different partners in different tissues, for example AKT1 participates in a range of interactions dependent on tissue type,50 further supporting the hypothesis that the results arise from the gene acting in different pathways. This shows that pleiotropy allows genes to be involved in multiple disorders in different contexts, demonstrating that pathways are better suited than molecular networks to map functional perturbations occurring in diseases. It should be noted that although string matching effectively removes a majority of disease pathways from the network, some disease pathways may remain within the network, making identification of the normal functions affected more difficult. In addition, removal of pathways containing disease, drug and addiction terms may have resulted in the loss of some pathways representing normal, healthy biological processes. However, the remaining pathways cover 97% of the initial gene set, reducing the impact of this concern.Mapping diseases onto molecular interaction networks has contributed towards the elucidation of disease mechanisms,Examining the similarities and differences between diseases is necessary to assess the shared applicability of knowledge and drugs. Our map makes these relationships immediately obvious. This method can be generalized to facilitate understanding of any group of disorders or phenotypes.34 Finally we looked at the biological processes attributed to each area of the network and investigated the distribution of disease pathway nodes.To generate the data for the network, we selected a low-redundancy set of human pathways, representing healthy biological processes. We assigned function to the pathway nodes and generated edges building on a method previously developed in yeast.th Sept 2015,35 providing a dataset of 4011 unique pathways containing 11,196 genes. CPDB collects and compiles data from major pathway databases such as KEGG, Reactome and WikiPathways. Of these pathways, 706 were exact duplicates and were removed. To be included in the network, pathways had to meet the following three requirements, they: represent the cell in a normal, healthy state ; had high confidence enriched GO annotations ; and belong to a reduced redundancy subset (see “Reduction of redundancy between pathways” Section).Pathways were downloaded from CPDB on 24To generate the functional network, we identified a set of pathways representing normal functions. We removed diseases by searching for disease terms within the pathway names , discarding electronically annotated (IEA) terms as they are of lower confidence than experimentally validated terms.38The Gene Ontology provides Biological Process annotations for individual genes, along with information specifying how annotations are generated.52 To meet the minimum criteria for enrichment analysis, each pathway must contain at least four genes with Biological Process GO annotations.53 Any pathways that contained fewer than four annotated genes were removed.We were able to assign high confidence, curated GO annotations to 88% of the genes in normal cellular pathways. We also added all non-IEA parent terms to the GO terms allocated to each gene, since for every GO term associated with a gene, all of the GO term’s ancestors apply.54 Enrichment analysis returned large sets of GO terms with p-values below 0.01 for pathway nodes (mean of 412.0 GO terms per pathway), using the Benjamini and Hochberg correction55 for multiple testing.Functional enrichment analysis was carried out using the R package clusterProfiler.34 which we use here to remove redundancy from each pathway’s enriched GO terms. The most specific/enriched GO terms that describe the function of all the genes in each pathway are identified and retained. GO terms describing the same genes with a lower level of significance are discarded, resulting in a reduced functional profile  is selected. This continues until all of the elements in the dataset are covered .We note that significant improvements in the algorithm’s ability to control pathway size variability have been observed when the algorithm was allowed to cover “most” rather than all of the genes in the dataset.To generate the edges in the network, we measured the semantic similarity of each pair of pathway nodes based on their associated GO terms in the minimized functional annotation profile . These values, between zero and one, formed the basis of the network edges.56 We selected our method based on its ability to comply with the assumption that GO terms within pathways should be more closely related than GO terms between different pathways.To calculate the semantic similarity between pairs of pathways, we first needed to measure the similarity between pairs of GO terms. This was necessary since the methods used to generate semantic similarity are not suitable for highly redundant sets of GO terms (see “Measuring the semantic distance between GO sets” Section). Since pathways are enriched with multiple GO terms, we established the most suitable method for comparing GO sets. Various measures are available for measuring the distance between GO terms and GO term sets.36 and Wang37 measures have been shown to outperform other methods in previous studies.38 We therefore implemented these methods using the R package GOSemSim.57 Supplementary Figure Of the various methods available to measure the distance between two GO terms, the Resnik38 The pairwise average method measures the similarity between every pair of GO terms between two pathways and then calculates the mean. The best-match average records the similarity between each GO term in the first pathway and the closest GO term in the second pathway. It then performs the symmetric calculation, before generating a mean distance based on both sets of scores. This produced a semantic distance between every pair of pathways generating a complete network. The complete network was impractical for global analysis, therefore edges were reduced (see “Pruning edges between pathway nodes” Section).To calculate the similarity between pathways, we tested two approaches: the pairwise average method and the best-match average.58 The clustering coefficient was calculated using Cytoscape 3.2.159 and for comparison ten randomized networks were generated using Network Randomizer 1.1.3.60Our network links pathway nodes using weighted edges based on their similarity. We aimed to reduce the number of edges in the network to show only the most significant functional links between the pathways. We generated a range of 50 thresholds between zero and one and calculated the proportion of nodes and edges retained by each. By subtracting the proportion of nodes retained by each threshold by the proportion of edges retained, we identified the threshold that linked the maximum number of nodes into the network using the fewest edges. Following network generation the degree distribution of the network was subjected to power law analysis using the R igraph package version 1.0.0.th of April 2016. This dataset contained 293,556 disease gene annotations for hereditary and non-hereditary disorders; this data includes both OMIM diseases such as “migraine, familial hemiplegic, 1; FHM1” and phenotypes, such as “visual hallucinations”. Using the Fisher’s exact test to map disease terms onto pathway genes we revealed 1061 disease annotations associated with at least 4 genes to ensure significant enrichment analysis across multiple pathways. We used the Fisher’s exact test to identify 219 pathways associated with 404 OMIM diseases, using a p-value threshold of 0.01.We mapped diseases on to our network using the Human Phenotype Ontology (HPO) disease data, downloaded on the 3061 This algorithm calculates the shortest path between two nodes. This measure conventionally uses distance rather than similarity. We compared these results to sets of shortest paths generated from sets of random nodes. We selected randomized sets of nodes of equal size to the set of disease nodes. We repeated this method 100 times for each disease.To test whether diseases tended to cluster within the network, we measured the shortest paths between pathways associated with each disease using NetworkX.cancer,tumor, tumour, melanoma, carcinoma, leukemia, lymphoma, and sarcoma in the set of HPO phenotypes enriched to a p-value of 0.01. We mapped the locations of 166 cancer related pathways onto the network and examined associations with biological processes. To measure the tendency of cancers to cluster within the network, we measured the shortest paths between pathway nodes with the same phenotype (see “Finding the shortest paths in disease sub-networks” Section).We selected cancers by searching for the terms: Similar pathway sets were generated for the immune diseases, cardiovascular diseases, disorders of the nervous system, obesity and diabetes, using string searches for common disorders and anatomical terms. Supplementary data https://data.mendeley.com/datasets/hn6t9hjfry (see declarations for full break down of main results and data files).The code required to generate these results is available at https://data.mendeley.com/datasets/37pkdchpf9/1. The code and data files results are available at . The file “main results” contains all the main results including the set cover pathways, pathway functions, pathway disease annotations, all semantic distances and the Cytoscape file containing the network (https://data.mendeley.com/datasets/3pbwkxjxg9/2).Data files can be found at Files containing the code are available along with the CPDB and Gene Ontology files used. OMIM files were not included for licencing reasons. The main project is written in Python however it requires output from an (included) R script.data 1Supplimentary figure 1Supplementary Data 2


Organizing biological information, representing it, and making it accessible, or biocuration, is an important aspect of biomedical research and discovery. However, because maintaining sophisticated biocuration is highly resource dependent, it continues to lag behind the continually being generated biomedical data. Another critical aspect of cancer research, pathway analysis, has proven to be an efficient method for gaining insight into the underlying biology associated with cancer. We propose a deep-learning-based model, Stacked Denoising Autoencoder Multi-Label Learning (SdaMLL), for facilitating gene multi-function discovery and pathway completion. SdaMLL can capture intermediate representations robust to partial corruption of the input pattern and generate low-dimensional codes superior to conditional dimension reduction tools. Experimental results indicate that SdaMLL outperforms existing classical multi-label algorithms. Moreover, we found some gene functions, such as Fused in Sarcoma  and p27 , that can be used to complete the related pathways. We provide a visual tool ( Among the 22 novel drugs approved by the U.S. Food and Drug Administration (FDA), six of them were designed for treating or diagnosing cancer2. Tomas Lindahl and Paul Modrich’s Mechanistic Studies of DNA Repair won the 2015 Nobel Prize in Chemistry, and their work may potentially advance the development of new cancer treatment3. In 2016, the 21st Century Cures Act provided 4.8 billion for the Cancer Moonshot4 and Precision Medicine Initiative, which aims at dramatically accelerating efforts to prevent, diagnose, and treat cancer5. Scientists from various fields, such as biology, statistics, and computer science, are using a vast array of approaches, trying their best to not only wage a battle but win the war against cancer worldwide.Cancer research has witnessed rapid advances year by year, generating a more abundant and complex body of knowledge. Researchers continue to come up with ingenious approaches for treating, preventing, and curing the disease. However, the war on cancer still has a long way to go6. At the present rate, however, the further and farther the curated data lags behind current biological knowledge, either way, the greater and more apparent the daily knowledge gaps will become. Originating with the Human Genome Project (HGP), microarray expression analysis, investments in large-scale sequencing centres and high-throughput analytical facilities have been increasing sharply, all leading to the exponential growth of biological data. The 2016 Nucleic Acids Research (NAR) online database collections, containing 15 categories and 41 subcategories, listed 1664 published biological databases. By July 2017, more than 27 million citations for biomedical literature from MEDLINE, life science journals, and online books had been indexed in Pubmed7. However, the resources for generating and testing hypotheses will soon become depleted or ineffective because of gap expansions. The purpose of providing a greater amount of instantaneous manual annotation associated with increased data acquisition, while being prepared to address the possibility of having to make best use of purely human labour, creates a virtually insurmountable dilemma8. It is because this approach is totally dependent on well-trained professional biocurators who can analyse and extract categorized information from the published literature.Among these approaches, biocuration, which involves organizing, representing, and providing biological information for humans and computers, is an essential part of biomedical discovery and research9. In 2005, most text mining tools were suited merely to a limited number of tasks10. With more yearly biomedical challenges and newly published databases such as i2b2, TREC Medical/CDS and BioNLP, biomedical text mining techniques have been driven to progress sharply. Hisschman et al. list the following four requirements for a biocurator text mining tool11: 1. Easy to use, install and maintain by the intended end user; 2. The tool need not be perfect, but it needs to complement the biocurator’s function; 3. Initial batch processing is necessary; and 4. The tool should provide linking of gene expression mentions of biological entities identified in the text with their referents identified in biological databases, then link them to the appropriate ontological terms.Text mining denotes the process of deriving high-quality information from text through the devising of patterns and trends. When properly processed by text mining methods, biomedical data may provide invaluable information to affect peoples’ cognizance of biomedical phenomena12 collects manually drawn pathway maps integrating information from metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems and human diseases. The workflow of biological pathway construction consists of four steps13 that serve as mining information resources, using pathway building tools, refinement, and leading to the desired specific annotated pathway, a process that can be very time-consuming and costly. Therefore, computational methods are needed to characterize the pathways automatically. Traditional in silico pathway annotation methods still rely on biologists to properly define features and guide feature selection during pathway prediction14, which creates yet another time- and cost-consuming step. Moreover, the results are highly dependent on the selected features, and if the features are not good enough, the achieved result may not be satisfying.Pathway analysis is an efficient method for gaining insight into the underlying biology of differentially expressed genes and proteins with less complexity and better explanatory power. KEGG PATHWAY17, speech recognition20, language translation22, sentiment analysis25, image caption28 and so on. Deep learning is a powerful tool for discovering intricate structures in high-dimensional data29, which is common in biomedical informatics.Intended for learning multiple levels of feature composition, models based on deep learning have become state-of-the-art methods in thriving research areas such as image recognition30. To the best of our knowledge, this is the first work applying a deep learning model31 to analyse gene multi-functions relevant to cancer pathways derived from full-text biomedical publications. In addition, the entire procedure proposed in this study does not require the involvement of a biologist to do a feature study about the data. Experimental results on eight KEGG cancer pathways reveal that SdaMLL is not only superior to classical multi-label learning models such as K-nearest neighbours and decision tree, but it can also achieve numerous gene functions related to important cancer pathways.In this paper, faced with the task of sifting through the voluminous extant biomedical publications, we propose a Stacked Denoising Autoencoder Multi-Label Learning (SdaMLL) model to explore the effects, if any, gene multi-functions may have on cancer pathways in KEGG. To acquire more functions for each gene, we explore full texts of biomedical articles where more detailed methodologies, experimental results, critical discussions and interpretations can be foundFaced with the challenge of reconciling a tremendous body of biomedical literature with its biological referents, we deem deep learning to be one of the most promising methods for fleshing out the relationship between them, and particularly in this paper, relationships with genes affecting cancer pathways. To predict multi-functions for each gene, we first generate a feature matrix following the routine in Fig. To generate the feature matrix, as shown in Fig. After generating the feature matrix, the deep learning-based method SdaMLL can unveil gene multi-function. As shown in Fig. x ∈ d to a hidden representation y ∈ d′ through a deterministic mapping:θ = {W, b} and s denotes the sigmoid function. The code y is a latent representation of x, and it is then mapped back into z, which has the same shape as x. The reconstruction mapping process is formulated using a similar transformation:θ′ = {W, b′}. The autoencoder is constrained to have tied weights, which means W = W. The goal of autoencoder is to minimize the average reconstruction error:x is stochastically mapped to x, a fixed number vd of elements is randomly chosen to be reset as 0, leaving others unchanged. The corrupted version of the original input, k + 1)th layer because we can now compute the code or latent feature representation from the layer below.A traditional autoencoder involves encoding and decoding. Encoding maps the input entclass1pt{minima32. Let γ = 1, 2, …, N, indicates the set of all the class labels. m indicates multi-label instances, and each Yi in {, ,…, } may contain several labels. The global error function is rewritten asi-th training example , the error term is Yi in γ is xi(k ∈ Yi) and one label not belonging to it α is the learning rate and its range is . When training the BP-MLL, the training instances are fed to the network one by one. For each multi-labeled instances , the weights are updated according to Eqs E doesn’t decrease or the training epochs hits a threshold, it stops training.The upper right part of Fig. et al. summarized six capabilities acquired by most forms of cancer, and they added two more cancer traits in 201134. Parallel pathways take part in the tumourigenesis process37. During the process, traits of cancers can be arranged in multiple permutations, which means cell transformation occurs. Semir Beyaz et. al have proposed that uncovering how PPAR-δ mediates tumourigenesis in diverse tissues and cell types in response to diet may enhance clinical utility38. Niall et al. regard targeting DNA repair and repair-deficient tumours as new avenues for treating advanced disease in the future39.The data used in our experiment is downloaded from the two databases KEGG PATHWAY and PubMed Central. Term frequencies are computed for all the words appearing in pathway-related articles retrieved from PubMed Central. To construct the feature matrix, we collect the gene names for all 1144 genes and 18930 articles closely related to these genes. The feature matrix is constructed using all the genes listed in the pathway map overviews and gene-related full-text articles fetched from Pubmed. For purposes of validating the generalization ability of the proposed method, 10-fold cross-validation was conducted while only being able to use a limited number of genes extracted from the existing KEGG pathways. Regarding the pathways, we selected the cancer pathways according to the following widely influential references. Douglas 40 and Backpropagation for Multi-Label Learning (BP-MLL)32. KNN is one of the most commonly used machine learning methods. The goal of the nearest neighbour method is to find a pre-determined number of training samples closest in distance to the new point and predict the labels among these samples. DTs is a non-parametric supervised learning classification method that aims at learning simple decision rules derived from original data and predicting results with these rules. BP-MLL is derived from the backpropagation41 algorithm via applying an error function acquiring the characteristics of multi-label learning. This is the first time this term has been employed in functional genomics and text categorization.We compare SdaMLL with the following methods: K-nearest neighbours (KNN), decision trees (DTs)42. Coverage evaluates the performance of a system for the top-ranked label, i.e., how dire is the need to go further down the set of labels to cover all the correct labels of each instance, on average. The performance is improved when the value decreases. For a set of labeled documents S = 〈, …, 〉, the coverage isrloss(H) = 0, the performance is perfect. For a set of labeled documents S = 〈,…, 〉, ranking loss is defined asY in γ and In our experiment, we adopted three different metrics for multi-label learning proposed by Schapire et. al, which are coverage, ranking loss, and average precisiony∈Y, which actually are in Y. The performance is better when the value of average precision is bigger.The average precision evaluates the average faction of labels ranked above a particular label Results for average precision can be found in the second column in Table In the third column (Ranking Loss) of Table The forth column (Coverage) of Table For the time consumption, it costs 153.07 s to train the BP-MLL model per epoch while the time of training the SdaMLL model is averaged to 38.46 s per epoch. The training time for KNN and decision tree is 0.42 s and 6.63 s. All the prediction time of KNN and decision tree is 12.6 s and 16ms, however, BP-MLL and SdaMLL is 7.16 s and 4.56 s. From the prediction time comparison, it can be seen that SdaMLL needs more time than decision trees, however, it is faster than KNN.43 and independent component analysis44 (ICA).Based on the results of the aforementioned measurements, SdaMLL outperforms BP-MLL in three respects: (1) SdaMLL converges far more quickly than BP-MLL, requiring 30 fewer training epochs on average; (2) On all of the 3 metrics, SdaMLL achieves better results than BP-MLL; and, (3) Original BP-MLL is time-consuming, while SdaMLL finishes in less time. Although the process of constructing the feature matrix is complicated, when one considers the difference between the training data distribution and the actual data distribution, the training data distribution is probably corrupted. Additionally, each instance is a 1 × 18930 vector, and most of the bits in a particular vector are zero, creating a data sparsity problem in which the data are extremely hard to interpret. Stacked denoising autoencoders, on the one hand, try to capture intermediate representation, which is robust compared to a partial corruption of the input pattern. In addition, by initializing the weights effectively, the architecture of the autoencoder is able to generate low-dimensional codes that surpass common dimension reduction tools such as principal component analysiset al. demonstrated a possible role for the candidate tumour suppressor ING genes in the biology of EBV-associated cancer45, and the N-terminal domain of EBNA3C residues 129 to 200 was previously demonstrated to associate with p53. From these clues, we may regard p53 as part of the pathway describing pathways in cancer (hsa05200).Apart from the quantity improvements, we can also ascertain the biological results. As mentioned in the dataset description, all the collected articles indicate particular functions of genes, which can serve as evidence for judging whether the gene serves as part of a pathway. From this prediction, we also found some articles that can solidly back up the correlation between genes and pathways. First, tumour Protein P53 (TP53) encodes a tumour suppressor protein containing transcriptional activation, DNA binding, and oligomerization domains. The encoded protein responds to diverse cellular stresses to regulate expression of target genes, thereby inducing cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. Saha 46, we can conclude that FUS-DDIT3 deregulates some NF-kappaB-controlled genes through interaction with NFKBIZ. FUS related genes are involved in tumour type-specific fusion oncogenes in human malignancies, which indicates that FUS is probably part of transcriptional misregulation in cancer (hsa:05202).Moreover, FUS or FUS/TLS (Fused in sarcoma/translocated in liposarcoma) is a multifunctional RNA/DNA-binding protein that is pathologically associated with cancer and neurodegeneration. It encodes a multifunctional protein component of the heterogeneous nuclear ribonucleoprotein (hnRNP) complex. From an article47. Obviously, it is also a hint that p27 becomes a member of viral carcinogenesis (hsa:05203). Other foundlings such as CCNA2, EGR2, and MAPKAPK2 belong to viruses or encode cancer pathways; CCND1, NRAS, and SDC1 all contribute to the proteoglycans pathway; AKT1, MET, TP53 and MAPK1 are adaptations of cellular metabolism pathway elements.In addition, P27 or Kip1 (Cyclin-Dependent Kinase Inhibitor 1B) encodes a cyclin-dependent kinase inhibitor, in which mutations are associated with multiple endocrine neoplasia type IV. The encoded protein binds to and prevents the activation of cyclin E-CDK2 or cyclin D-CDK4 complexes, and thus controls cell cycle progression at G1. PKB/Akt mediates cell-cycle progression by phosphorylation of P27 (Kip1) at threonine 157, and modulation of its cellular localization indicates that Akt may contribute to tumour-cell proliferation by phosphorylation and cytosolic retention of p27, thus relieving CDK2 from p27-induced inhibitionhttps://www.keaml.cn/gpvisual.These three samples are the solid proof for our biology discovery. More interesting examples (270 new functions) are listed at We also provide a visualization tool for multi-function genes in cancer pathways. Based on the text mining results, we find it is common for a gene to get involved in multiple pathways. For the sake of unveiling the relationship between genes and pathways, we try to visualize a network of genes, articles, and pathways. In Fig. In this paper, we have proposed a novel multi-label gene function annotation model based on a deep learning strategy, namely, SdaMLL, for gene multi-function discovery. This model takes advantage of both effective dimension reduction and multi-label classification on account of Stacked denoising autoencoders. Compared to BP-MLL, SdaMLL converges much faster in terms of the number of training epochs. In addition, during the experiments, we try to reduce the dimension from 18900 to 200, which helps to shorten the training time tremendously. From the results, we can conclude that SdaMLL is a state-of-the-art algorithm for finishing the task at hand. In addition, we provide a website for researchers to inspect relationships between genes and articles.This study demonstrated how the proposed method performed based on the data of eight pathways and generated a feature matrix containing genes existing in these pathways. Better annotation performance may be anticipated if more information from other pathways is integrated into the model in the future.