CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets.

MOTIVATION: Causal biological interaction networks represent cellular regulatory pathways. Their fusion with other biological data enables insights into disease mechanisms and novel opportunities for drug discovery. RESULTS: We developed Causal Network of Diseases (CaNDis), a web server for the exploration of a human causal interaction network, which we expanded with data on diseases and FDA-approved drugs, on the basis of which we constructed a disease-disease network in which the links represent the similarity between diseases. We show how CaNDis can be used to identify candidate genes with known and novel roles in disease co-occurrence and drug-drug interactions. AVAILABILITYAND IMPLEMENTATION: CaNDis is freely available to academic users at http://candis.ijs.si and http://candis.insilab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential Response of Grapevine to Infection with 'Candidatus Phytoplasma solani' in Early and Late Growing Season through Complex Regulation of mRNA and Small RNA Transcriptomes.

Bois noir is the most widespread phytoplasma grapevine disease in Europe. It is associated with 'Candidatus Phytoplasma solani', but molecular interactions between the causal pathogen and its host plant are not well understood. In this work, we combined the analysis of high-throughput RNA-Seq and sRNA-Seq data with interaction network analysis for finding new cross-talks among pathways involved in infection of grapevine cv. Zweigelt with 'Ca. P. solani' in early and late growing seasons. While the early growing season was very dynamic at the transcriptional level in asymptomatic grapevines, the regulation at the level of small RNAs was more pronounced later in the season when symptoms developed in infected grapevines. Most differentially expressed small RNAs were associated with biotic stress. Our study also exposes the less-studied role of hormones in disease development and shows that hormonal balance was already perturbed before symptoms development in infected grapevines. Analysis at the level of communities of genes and mRNA-microRNA interaction networks revealed several new genes (e.g., expansins and cryptdin) that have not been associated with phytoplasma pathogenicity previously. These novel actors may present a new reference framework for research and diagnostics of phytoplasma diseases of grapevine.

Interactive exploration of heterogeneous biological networks with Biomine Explorer.

SUMMARY: Biomine Explorer is a web application that enables interactive exploration of large heterogeneous biological networks constructed from selected publicly available biological knowledge sources. It is built on top of Biomine, a system which integrates cross-references from several biological databases into a large heterogeneous probabilistic network. Biomine Explorer offers user-friendly interfaces for search, visualization, exploration and manipulation as well as public and private storage of discovered subnetworks with permanent links suitable for inclusion into scientific publications. A JSON-based web API for network search queries is also available for advanced users. AVAILABILITY AND IMPLEMENTATION: Biomine Explorer is implemented as a web application, which is publicly available at https://biomine.ijs.si. Registration is not required but registered users can benefit from additional features such as private network repositories.

Homogeneous clusters of Alzheimer's disease patient population.

BACKGROUND: Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis. METHODS: We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors. RESULTS: The main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes. CONCLUSIONS: The results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.

GMOseek: a user friendly tool for optimized GMO testing.

BACKGROUND: With the increasing pace of new Genetically Modified Organisms (GMOs) authorized or in pipeline for commercialization worldwide, the task of the laboratories in charge to test the compliance of food, feed or seed samples with their relevant regulations became difficult and costly. Many of them have already adopted the so called "matrix approach" to rationalize the resources and efforts used to increase their efficiency within a limited budget. Most of the time, the "matrix approach" is implemented using limited information and some proprietary (if any) computational tool to efficiently use the available data. RESULTS: The developed GMOseek software is designed to support decision making in all the phases of routine GMO laboratory testing, including the interpretation of wet-lab results. The tool makes use of a tabulated matrix of GM events and their genetic elements, of the laboratory analysis history and the available information about the sample at hand. The tool uses an optimization approach to suggest the most suited screening assays for the given sample. The practical GMOseek user interface allows the user to customize the search for a cost-efficient combination of screening assays to be employed on a given sample. It further guides the user to select appropriate analyses to determine the presence of individual GM events in the analyzed sample, and it helps taking a final decision regarding the GMO composition in the sample. GMOseek can also be used to evaluate new, previously unused GMO screening targets and to estimate the profitability of developing new GMO screening methods. CONCLUSION: The presented freely available software tool offers the GMO testing laboratories the possibility to select combinations of assays (e.g. quantitative real-time PCR tests) needed for their task, by allowing the expert to express his/her preferences in terms of multiplexing and cost. The utility of GMOseek is exemplified by analyzing selected food, feed and seed samples from a national reference laboratory for GMO testing and by comparing its performance to existing tools which use the matrix approach. GMOseek proves superior when tested on real samples in terms of GMO coverage and cost efficiency of its screening strategies, including its capacity of simple interpretation of the testing results.

SegMine workflows for semantic microarray data analysis in Orange4WS.

BACKGROUND: In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. RESULTS: We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. CONCLUSIONS: Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.

autoBOT: evolving neuro-symbolic representations for explainable low resource text classification.

Learning from texts has been widely adopted throughout industry and science. While state-of-the-art neural language models have shown very promising results for text classification, they are expensive to (pre-)train, require large amounts of data and tuning of hundreds of millions or more parameters. This paper explores how automatically evolved text representations can serve as a basis for explainable, low-resource branch of models with competitive performance that are subject to automated hyperparameter tuning. We present autoBOT (automatic Bags-Of-Tokens), an autoML approach suitable for low resource learning scenarios, where both the hardware and the amount of data required for training are limited. The proposed approach consists of an evolutionary algorithm that jointly optimizes various sparse representations of a given text (including word, subword, POS tag, keyword-based, knowledge graph-based and relational features) and two types of document embeddings (non-sparse representations). The key idea of autoBOT is that, instead of evolving at the learner level, evolution is conducted at the representation level. The proposed method offers competitive classification performance on fourteen real-world classification tasks when compared against a competitive autoML approach that evolves ensemble models, as well as state-of-the-art neural language models such as BERT and RoBERTa. Moreover, the approach is explainable, as the importance of the parts of the input space is part of the final solution yielded by the proposed optimization procedure, offering potential for meta-transfer learning.

PubMed-Scale Chemical Concept Embeddings Reconstruct Physical Protein Interaction Networks.

PubMed is the largest resource of curated biomedical knowledge to date, entailing more than 25 million documents. Large quantities of novel literature prevent a single expert from keeping track of all potentially relevant papers, resulting in knowledge gaps. In this article, we present CHEMMESHNET, a newly developed PubMed-based network comprising more than 10,000,000 associations, constructed from expert-curated MeSH annotations of chemicals based on all currently available PubMed articles. By learning latent representations of concepts in the obtained network, we demonstrate in a proof of concept study that purely literature-based representations are sufficient for the reconstruction of a large part of the currently known network of physical, empirically determined protein-protein interactions. We demonstrate that simple linear embeddings of node pairs, when coupled with a neural network-based classifier, reliably reconstruct the existing collection of empirically confirmed protein-protein interactions. Furthermore, we demonstrate how pairs of learned representations can be used to prioritize potentially interesting novel interactions based on the common chemical context. Highly ranked interactions are qualitatively inspected in terms of potential complex formation at the structural level and represent potentially interesting new knowledge. We demonstrate that two protein-protein interactions, prioritized by structure-based approaches, also emerge as probable with regard to the trained machine-learning model.

New Cross-Talks between Pathways Involved in Grapevine Infection with 'Candidatus Phytoplasma solani' Revealed by Temporal Network Modelling.

Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with 'Candidatus Phytoplasma solani', and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with 'Ca. P. solani'. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to 'Ca. P. solani' to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.

Primary health-care network monitoring: a hierarchical resource allocation modeling approach.

Management of a primary health-care network (PHCN) is a difficult task in every country. A suitable monitoring system can provide useful information for PHCN management, especially given a large quantity of health-care data that is produced daily in the network. This paper proposes a methodology for structured development of monitoring systems and a PHCN resource allocation monitoring model based on this methodology. The purpose of the monitoring model is to improve the allocation of health-care resources. The proposed methodology is based on modules that are organized into a hierarchy, where each module monitors a particular aspect of the system. This methodology was used to design a PHCN monitoring model for Slovenia. Specific aspects of the Slovenian PHCN were taken into account such as varying needs of patients from different municipalities, existence of small municipalities having less than 1000 residents, the fact that many patients visit physicians in other municipalities, and that physicians may work at more than one location or organization. The main modules in the model are focused on the overall assessment of the PHCN, monitoring of patients visits to health-care providers (HCPs), physical accessibility of health services, segment of patients in municipalities who have not selected a personal physician, assessment of the availability of HCPs for patients, physicians working on more than one location, and available human resources in the PHCN. Most of the model's components are general and can be adapted for other national health-care systems.

Embedding-based Silhouette community detection.

Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. This paper proposes the embedding-based Silhouette community detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on 234 synthetic networks, as well as on a real-life social network. Even though SCD is not based on any form of modularity optimization, it performs comparably or better than state-of-the-art community detection algorithms, such as the InfoMap and Louvain. Further, we demonstrate that SCD's outputs can be used along with domain ontologies in semantic subgroup discovery, yielding human-understandable explanations of communities detected in a real-life protein interaction network. Being embedding-based, SCD is widely applicable and can be tested out-of-the-box as part of many existing network learning and exploration pipelines.

Propositionalization and embeddings: two sides of the same coin.

Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. The results show that the new algorithms can outperform existing relational learners and can solve much larger problems.

CSM-SD: methodology for contrast set mining through subgroup discovery.

This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning settings, a one-versus-all and a pairwise contrast set mining setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered contrast sets can be improved by offering additional contrast set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.

GMOtrack: generator of cost-effective GMO testing strategies.

Commercialization of numerous genetically modified organisms (GMOs) has already been approved worldwide, and several additional GMOs are in the approval process. Many countries have adopted legislation to deal with GMO-related issues such as food safety, environmental concerns, and consumers' right of choice, making GMO traceability a necessity. The growing extent of GMO testing makes it important to study optimal GMO detection and identification strategies. This paper formally defines the problem of routine laboratory-level GMO tracking as a cost optimization problem, thus proposing a shift from "the same strategy for all samples" to "sample-centered GMO testing strategies." An algorithm (GMOtrack) for finding optimal two-phase (screening-identification) testing strategies is proposed. The advantages of cost optimization with increasing GMO presence on the market are demonstrated, showing that optimization approaches to analytic GMO traceability can result in major cost reductions. The optimal testing strategies are laboratory-dependent, as the costs depend on prior probabilities of local GMO presence, which are exemplified on food and feed samples. The proposed GMOtrack approach, publicly available under the terms of the General Public License, can be extended to other domains where complex testing is involved, such as safety and quality assurance in the food supply chain.

SEGS: search for enriched gene sets in microarray data.

Gene Ontology (GO) terms are often used to interpret the results of microarray experiments. The most common approach is to perform Fisher's exact tests to find gene sets annotated by GO terms which are over-represented among the genes declared to be differentially expressed in the analysis of microarray data. Another way is to apply Gene Set Enrichment Analysis (GSEA) that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. However, after correcting for multiple hypotheses testing, few (or no) GO terms may meet the threshold for statistical significance, because the relevant biological differences are small relative to the noise inherent to the microarray technology. In addition to the individual GO terms, we propose testing of gene sets constructed as intersections of GO terms, Kyoto Encyclopedia of Genes and Genomes Orthology (KO) terms, and gene sets constructed by using gene-gene interaction data obtained from the ENTREZ database. Our method finds gene sets that are significantly over-represented among differentially expressed genes which cannot be found by the standard enrichment testing methods applied on individual GO and KO terms, thus improving the enrichment analysis of microarray data.

Symptoms and medications change patterns for Parkinson's disease patients stratification.

Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients' medical conditions and changes in the prescribed therapy when the medical conditions change. We present two novel approaches. The first algorithm discovers symptoms' impact on Parkinson's disease progression. Experiments on the Parkinson Progression Markers Initiative (PPMI) data reveal a subset of symptoms influencing disease progression which are already established in Parkinson's disease literature, as well as symptoms that are considered only recently as possible indicators of disease progression by clinicians. The second novelty is a methodology for detecting patterns of medications dosage changes based on the patient status. The methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI data demonstrate that, using the proposed methodology, we can identify some clinically confirmed patients' symptoms suggesting medications change. In terms of predictive performance, our multitask predictive clustering tree approach is mostly comparable to the random forest multitask model, but has the advantage of model interpretability.

Data mining and visualization for decision support and modeling of public health-care resources.

This paper proposes an innovative use of data mining and visualization techniques for decision support in planning and regional-level management of Slovenian public health-care. Data mining and statistical techniques were used to analyze databases collected by a regional Public Heath Institute. We also studied organizational aspects of public health resources in the selected Celje region with the objective to identify the areas that are atypical in terms of availability and accessibility of public health services for the population. The most important step was the detection of outliers and the analysis of availability and accessibility deviations. The results are applicable to health-care planning and support in decision making by local and regional health-care authorities. In addition to the practical results, which are directly useful for decision making in planning of the regional health-care system, the main methodological contribution of the paper are the developed visualization methods that can be used to facilitate knowledge management and decision making processes.

Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease.

The heterogeneity of Alzheimer's disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients.

Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.

Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer's disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.