Network Inference Algorithms Research Articles

SPLANG—a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms

Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

Approximate inference on optimized quantum Bayesian networks

In recent years, there has been a significant upsurge in the interest surrounding Quantum machine learning, with researchers actively developing methods to leverage the power of quantum technology for solving highly complex problems across various domains. However, implementing gate-based quantum algorithms on noisy intermediate quantum devices (NISQ) presents notable challenges due to limited quantum resources and inherent noise. In this paper, we propose an innovative approach for representing Bayesian networks on quantum circuits, specifically designed to address these challenges and highlight the potential of combining optimized circuits with quantum hybrid algorithms for Bayesian network inference. Our aim is to minimize the required quantum resource needed to implement a Quantum Bayesian network (QBN) and implement quantum approximate inference algorithm on a quantum computer. Through simulations and experiments on IBM Quantum computers, we show that our circuit representation significantly reduces the resource requirements without decreasing the performance of the model. These findings underscore how our approach can better enable practical applications of QBN on currently available quantum hardware.

Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Construction of pan-cancer regulatory networks based on causal inference

The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC).

The Differentiation of Residents’ Cultural Consumption Tendency and Consumption Recommendation System Based on Network Inference Algorithm

Abstract To address the issue of insufficient accuracy in consumer recommendation systems, a new biased network inference algorithm is proposed based on traditional network inference algorithms. This new network inference algorithm can significantly improve the resource allocation ability of the original one, thereby improving recommendation performance. Then, the performance of this algorithm is verified through comparative experiments with network-based inference algorithms, network inference algorithms with initial resource optimization, and heterogeneous network inference algorithms. The results showed that the accuracy of the new network inference algorithm was 24.5%, which was superior to traditional one. In terms of system performance testing, the recommendation hit rate of the new network inference algorithm increased by 13.97%, which was superior to the other three comparative algorithms. The experimental results indicated that a novel network inference algorithm with bias can improve the performance of consumer recommendation systems, providing new ideas for improving the performance of consumer recommendation systems.

Computational Ensemble Gene Co-Expression Networks for the Analysis of Cancer Biomarkers

Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new biomarkers for such diseases. These biomarkers are essential for the discovery of new treatments for genetic diseases such as cancer. In this work, we introduce an algorithm for genetic network inference based on an ensemble method that improves the robustness of the results by combining two main steps: first, the evaluation of the relationship between pairs of genes using three different co-expression measures, and, subsequently, a voting strategy. The utility of this approach was demonstrated by applying it to a human dataset encompassing breast and prostate cancer-associated stromal cells. Two gene networks were computed using microarray data, one for breast cancer and one for prostate cancer. The results obtained revealed, on the one hand, distinct stromal cell behaviors in breast and prostate cancer and, on the other hand, a list of potential biomarkers for both diseases. In the case of breast tumor, ST6GAL2, RIPOR3, COL5A1, and DEPDC7 were found, and in the case of prostate tumor, the genes were GATA6-AS1, ARFGEF3, PRR15L, and APBA2. These results demonstrate the usefulness of the ensemble method in the field of biomarker discovery.

Relapse to cocaine seeking is regulated by medial habenula NR4A2/NURR1 in mice

Drugs of abuse can persistently change the reward circuit in ways that contribute to relapse behavior, partly via mechanisms that regulate chromatin structure and function. Nuclear orphan receptor subfamily4 groupA member2 (NR4A2, also known as NURR1) is an important effector of histone deacetylase 3 (HDAC3)-dependent mechanisms in persistent memory processes and is highly expressed in the medial habenula (MHb), a region that regulates nicotine-associated behaviors. Here, expressing the Nr4a2 dominant negative (Nurr2c) in the MHb blocks reinstatement of cocaine seeking in mice. We use single-nucleus transcriptomics to characterize the molecular cascade following Nr4a2 manipulation, revealing changes in transcriptional networks related to addiction, neuroplasticity, and GABAergic and glutamatergic signaling. The network controlled by NR4A2 is characterized using a transcription factor regulatory network inference algorithm. These results identify the MHb as a pivotal regulator of relapse behavior and demonstrate the importance of NR4A2 as a key mechanism driving the MHb component of relapse.

Promoting the transformation of agricultural supply chain management under the development of digital economy in the Internet era

Abstract Agricultural products, as the necessities of people’s daily consumption, are related to the national economy and people’s livelihood, and the development of information technology provides a new direction and ideas for the transformation of agricultural products supply chain management. The article proposes the risk assessment method for the agricultural products supply chain based on the Bayesian network inference algorithm and OWA algorithm, firstly constructs the Bayesian network model for risk assessment of agricultural products supply chain, elaborates on the belief updating algorithm and the belief-free updating algorithm in the Bayesian network, then combines with the OWA operator, proposes the method of risk diagnosis of agricultural products supply chain based on the Bayes-OWA hybrid operator, and with this The algorithm is the core of the proposed agricultural supply chain management strategy. Through empirical tests, it can be obtained that the average supply time of enterprise 4 in agricultural products is 22.3 days, and the reliability of supply is 92.3%, which is highly efficient. In the agricultural products supply program, the average value of quality qualification rate has been improved by 2.67%, the average number of days for supplier delivery has been shortened by 13 days, and the supplier’s ability to respond quickly to demand has been improved by 3.61%. Therefore, actively constructing the informationization construction of the agricultural supply chain, building a logistics information platform, improving rural communication facilities, and providing timely market information to farmers can better ensure the efficient operation of the agricultural supply chain.

Two-Level Scheduling Algorithms for Deep Neural Network Inference in Vehicular Networks

In vehicular networks, task scheduling at the microarchitecture-level and network-level offers tremendous potential to improve the quality of computing services for deep neural network (DNN) inference. However, existing task scheduling works only focus on either one of the two levels, which results in inefficient utilization of computing resources. This paper aims to fill this gap by formulating a two-level scheduling problem for DNN inference tasks in a vehicular network, with an objective of minimizing total weighted sum of response time and energy consumption for all tasks under the following constraints: per task response time, per vehicle energy consumption, per vehicle storage capacity. We first formulate the problem and prove that it is NP-hard. A group transformation based algorithm, called GTA, is proposed. GTA makes scheduling decisions at the network-level using the group transformation based approach, and at the microarchitecture-level using a greedy strategy. In addition, an algorithm, denoted as DRL, is proposed to decrease total weighted sum of response time and energy consumption for all tasks. DRL trains two models with deep reinforcement learning to achieve two-level scheduling. The proposed algorithms are evaluated on a platform consisting of a desktop, Raspberry Pi, Eyeriss, OSM, SUMO, NS-3. Simulation results show that DRL outperforms the state-of-the-art methods for all cases, while the proposed GTA outperforms the state-of-the-art methods for most cases, in terms of total weighted sum of response time and energy consumption. Compared with four baseline algorithms, GTA and DRL reduce the total weighted sum of response time and energy consumption by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$41.49\%$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$62.38\%$</tex-math> </inline-formula> , on average respectively, for different numbers of tasks.

Integrative transcriptome analysis identifies MYBL2 as a poor prognosis marker for osteosarcoma and a pan-cancer marker of immune infiltration

MYBL2 (MYB proto-oncogene like 2) is an emerging prognostic marker for malignant tumors, and its potential role in osteosarcoma and its relationship with immune infiltration in pan-cancer is yet to be elucidated. We constructed a transcription factor activity profile of osteosarcoma using the single-cell regulatory network inference algorithm based on single-cell RNA sequencing data obtained from the Gene Expression Omnibus. Subsequently, we calculated the extent of MYBL2 activation in malignant proliferative osteoblasts. We also explored the association between MYBL2 and chemotherapy resistance in osteosarcoma. Furthermore, we systematically correlated MYBL2 with immunological signatures in the tumor microenvironment in pan-cancer, including immune cell infiltration, immune checkpoints, and tumor immunotherapy prognosis. Finally, we developed and validated a risk score (MRGS), derived an osteosarcoma risk score nomogram based on MRGS, and tested its ability to predict prognosis. MYBL2 and gene enrichment analyses in osteosarcoma and pan-cancer revealed that MYBL2 was positively correlated with cell proliferation and tumor immune pathways. MYBL2 expression positively correlated with SLC19A1 in pan-cancer and osteosarcoma cell lines. Pan-cancer immune infiltration analysis revealed that MYBL2 was correlated with myeloid-derived suppressor cells, Th2 cell infiltration, CD276, RELT gene expression, and tumor mutation burden. In summary, MYBL2 regulates proliferation, progression, and immune infiltration in osteosarcoma and pan-cancer. Therefore, we found that MYBL2 could be used as a potential marker for predicting the osteosarcoma prognosis. Patients with osteosarcoma and high MYBL2 expression are theoretically more sensitive to methotrexate. An osteosarcoma prognostic nomogram can provide new ideas in the search for osteosarcoma prognostic markers.

Nervous system-related gene regulatory networks and functional evolution of ETS proteins across species

The ETS domain transcription factor family is one of the major transcription factor superfamilies that play regulatory roles in development, cell growth, and cancer progression. Although different functions of ETS member proteins in the nervous system have been demonstrated in various studies, their role in neuronal cell differentiation and the evolutionary conservation of its target genes have not yet been extensively studied. In this study, we focused on the regulatory role of ETS transcription factors in neuronal differentiation and their functional evolution by comparative transcriptomics. In order to investigate the regulatory role of ETS transcription factors in neuronal differentiation across species, transcriptional profiles of ETS members and their target genes were investigated by comparing differentially expressed genes and gene regulatory networks, which were analyzed using human, gorilla, mouse, fruit fly and worm transcriptomics datasets. Bioinformatics approaches to examine the evolutionary conservation of ETS transcription factors during neuronal differentiation have shown that ETS member proteins regulate genes associated with neuronal differentiation, nervous system development, axon, and synaptic regulation in different organisms. This study is a comparative transcriptomic study of ETS transcription factors in terms of neuronal differentiation using a gene regulatory network inference algorithm. Overall, a comparison of gene regulation networks revealed that ETS members are indeed evolutionarily conserved in the regulation of neuronal differentiation. Nonetheless, ETS, PEA3, and ELF subfamilies were found to be relatively more active transcription factors in the transcriptional regulation of neuronal differentiation.

Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data.

In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell data bring great difficulties to the analysis of scRNA-seq data, resulting in lower accuracy of gene regulatory networks reconstructed by traditional methods. In this article, we propose a novel supervised convolutional neural network (CNNSE), which can extract gene expression information from 2D co-expression matrices of gene doublets and identify interactions between genes. Our method can avoid the loss of extreme point interference by constructing a 2D co-expression matrix of gene pairs and significantly improve the regulation precision between gene pairs. And the CNNSE model is able to obtain detailed and high-level semantic information from the 2D co-expression matrix. Our method achieves satisfactory results on simulated data [accuracy (ACC): 0.712, F1: 0.724]. On two real scRNA-seq datasets, our method exhibits higher stability and accuracy in inference tasks compared with other existing gene regulatory network inference algorithms.

Constrained expectation-maximisation for inference of social graphs explaining online user–user interactions

Current network inference algorithms fail to generate graphs with edges that can explain whole sequences of node interactions in a given dataset or trace. To quantify how well an inferred graph can explain a trace, we introduce feasibility, a novel quality criterion, and suggest that it is linked to the result’s accuracy. In addition, we propose CEM-*, a network inference method that guarantees 100% feasibility given online social media traces, which are a non-trivial extension of the Expectation-Maximization algorithm developed by Newman (Nature Phys 14:67–75, 2018). We propose a set of linear optimization updates that incorporate a set of auxiliary variables and a set of feasibility constraints; the latter takes into consideration all the hidden paths that are possible between users based on their timestamps of interaction and guides the inference toward feasibility. We provide two CEM-* variations, that assume either an Erdős–Rényi (ER) or a Stochastic Block Model (SBM) prior for the underlying graph’s unknown distribution. Extensive experiments on one synthetic and one real-world Twitter dataset show that for both priors CEM-* can generate a posterior distribution of graphs that explains the whole trace while being closer to the ground truth. As an additional benefit, the use of the SBM prior infers and clusters users simultaneously during optimization. CEM-* outperforms baseline and state-of-the-art methods in terms of feasibility, run-time, and precision of the inferred graph and communities. Finally, we propose a heuristic to adapt the inference to lower feasibility requirements and show how it can affect the precision of the result.

A data-driven optimization method for coarse-graining gene regulatory networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

Data-Efficient Inference of Nonlinear Oscillator Networks

Decoding the connectivity structure of a network of nonlinear oscillators from measurement data is a difficult yet essential task for understanding and controlling network functionality. Several data-driven network inference algorithms have been presented, but the commonly considered premise of ample measurement data is often difficult to satisfy in practice. In this paper, we propose a data-efficient network inference technique by combining correlation statistics with the model-fitting procedure. The proposed approach can identify the network structure reliably in the case of limited measurement data. We compare the proposed method with existing techniques on a network of Stuart-Landau oscillators, oscillators describing circadian gene expression, and noisy experimental data obtained from Rössler Electronic Oscillator network.

Interaction-based transcriptome analysis via differential network inference.

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.

Network biology: Recent advances and challenges

Biological networks have garnered widespread attention. The development of biological networks has spawned the birth of a new interdisciplinary field &amp;ndash; network biology. Network biology involves the exploration of complex biological systems through biological networks for better understanding of biological functions. This paper reviews some of the recent development of network biology. On the one hand, various approaches to constructing different types of biological networks are reviewed, and the pros and cons of each approach are discussed; on the other hand, the recent advances of information mining in biological networks are reviewed. The principles of guilt-by-association and guilt-by-rewiring in network biology and their applications are discussed. Although great advances have been achieved in the field of network biology over the past decades, there are still many challenging issues. First, efficient and reliable network inference algorithms for high-dimensional and highly noisy omics data are still in great demand. Second, the research focus will be on multilayer biological network theory. This plays a critical role in the exploration of the multi-scale or dynamical characteristics of complex biomolecular networks by integrating multi-source heterogeneous omics data. Third, a close cooperation among biologists, medical workers, and researchers from network science is still a prerequisite in the applications of network biology. The rapid development of network biology will undoubtedly raise important clues for understanding complex phenotypes in biological systems.

AML-144 Uroporphyrinogen Decarboxylase (UROD) Is a Therapeutically Targetable Dependency in Acute Erythroid Leukemia

Acute erythroid leukemia (AEL) is a disease of erythroid lineage that commonly exhibits TP53 mutations, a complex karyotype, and poor prognosis irrespective of currently available therapies. To identify AEL dependencies and novel therapeutic targets in AEL using genome-wide CRISPR/Cas9 knockout screens in mouse models of AEL. Analysis of the screens performed using the Brie library identified the Urod gene encoding uroporphyrinogen decarboxylase as a critical dependency in two Trp53/Bcor mutated mouse AEL cell lines. UROD is a cytosolic enzyme in the heme biosynthesis pathway involved in converting cytosolic uroporphyrinogen III into the key heme biosynthetic intermediate, coproporphyrinogen III. In an analysis of RNA-seq data from over 2,000 acute leukemia samples, UROD was significantly overexpressed and active by a data-driven network inference algorithm in AEL compared to other leukemia subtypes. We validated UROD as a dependency by electroporation of Cas9 and synthetic sgRNA ribonucleoprotein complex in the two screened cell lines, additional mouse leukemia cell lines, and two commercially available human AEL cell lines. Targeted knockout of UROD resulted in decreased tumor cell growth and viability compared to the non-targeting guide control cells. Sequencing analysis showed high editing efficiency (>85%) followed by recovery of the wildtype cells, indicating that UROD is a dependency in these cells. These results correlated with a significant increase of necrotic cells preceding loss of editing in the two tested human cell lines. We also observed an increase in ALAS1 expression, which is negatively regulated by cellular heme, indicating that UROD knockout leukemic cells cannot generate heme, leading to necrotic cell death. Moreover, mitochondrial function assays showed significant decreases in basal respiration, ATP production, maximal respiration, and spare capacity in a time-dependent manner, followed by the rescue of mitochondrial function after loss of editing efficiency and re-expression of UROD. We have recently developed homozygous UROD-dTAG engineered human AEL cell lines to perform in-vivo survival studies. Through whole-genome CRISPR knockout screening and functional studies, we show that UROD is a dependency in AEL and provides a basis for exploring its therapeutic inhibition.

Using empirical biological knowledge to infer regulatory networks from multi-omics data

BackgroundIntegration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge.ResultsWe present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples.ConclusionsWe provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.