VB-MK-LMF: Fusion of drugs, targets and interactions using Variational Bayesian Multiple Kernel Logistic Matrix Factorization

Background Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance. Method We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Results VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of ``small sample size'' regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time. Conclusion In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions. Availability Data and code are available at http://bioinformatics.mit.bme.hu

Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression

Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: bioinformatics.mit.bme.hu/UKBNetworks

VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering

BACKGROUND: The low concordance between different variant calling methods still poses a challenge for the wide-spread application of next-generation sequencing in research and clinical practice. A wide range of variant annotations can be used for filtering call sets in order to improve the precision of the variant calls, but the choice of the appropriate filtering thresholds is not straightforward. Variant quality score recalibration provides an alternative solution to hard filtering, but it requires large-scale, genomic data. RESULTS: We evaluated germline variant calling pipelines based on BWA and Bowtie 2 aligners in combination with GATK UnifiedGenotyper, GATK HaplotypeCaller, FreeBayes and SAMtools variant callers, using simulated and real benchmark sequencing data (NA12878 with Illumina Platinum Genomes). We argue that these pipelines are not merely discordant, but they extract complementary useful information. We introduce VariantMetaCaller to test the hypothesis that the automated fusion of measurement related information allows better performance than the recommended hard-filtering settings or recalibration and the fusion of the individual call sets without using annotations. VariantMetaCaller uses Support Vector Machines to combine multiple information sources generated by variant calling pipelines and estimates probabilities of variants. This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, ranging from a few hundred kilobases to whole exomes. We also demonstrated that VariantMetaCaller supports a quantitative, precision based filtering of variants under wider conditions. Specifically, the computed probabilities of the variants can be used to order the variants, and for a given threshold, probabilities can be used to estimate precision. Precision then can be directly translated to the number of true called variants, or equivalently, to the number of false calls, which allows finding problem-specific balance between sensitivity and precision. CONCLUSIONS: VariantMetaCaller can be applied to small target regions and whole exomes as well, and it can be used in cases of organisms for which highly accurate variant call sets are not yet available, therefore it can be a viable alternative to hard filtering in cases where variant quality score recalibration cannot be used. VariantMetaCaller is freely available at http://bioinformatics.mit.bme.hu/VariantMetaCaller

Bayesi technikák a járványmodellezésben

INST: L_200A járványterjedés matematikai modellezése különösen fontossá vált a 2020-as koronavírus-járvány következtében. Az előrejelzést, beavatkozást támogató korábbi modellek mellett egy sor új technikát is kidolgoztak, amelyek között a gépi tanulás területéről származó, Bayes-statisztikán alapuló megközelítések is helyet kaptak. Ebben a munkában a járványterjedés kompartment-modelljeinek bayesi kezelését tűzzük ki célul; emellett bemutatunk egy középiskolai járványmodellező szakköri foglalkozást

Additional file 2 of VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

Derivation of the lower bound using Jaakkola’s bound on the logistic sigmoid. (PDF 107 kb

Additional file 2 of VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering

Variant annotations used as features for SVMs. The full listing and short description of the variant annotations used as features for Support Vector Machines

Additional file 1 of VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering

Supplementary results, figures and tables. Supplementary results, Figures S1–S11 and Tables S1–S6