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Sysoev, Oleg
Publications (10 of 23) Show all publications
Schäfer, S., Smelik, M., Sysoev, O., Zhao, Y., Eklund, D., Lilja, S., . . . Benson, M. (2024). scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases. Genome Medicine, 16(1), Article ID 42.
Open this publication in new window or tab >>scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases
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2024 (English)In: Genome Medicine, E-ISSN 1756-994X, Vol. 16, no 1, article id 42Article in journal (Refereed) Published
Abstract [en]

Background Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.Methods Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.Results scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.Conclusions We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Place, publisher, year, edition, pages
BMC, 2024
Keywords
Single-cell RNA sequencing; scRNA-seq; Immune-mediated inflammatory disease; Drug prioritisation; Drug repurposing; Drug prediction
National Category
Pharmacology and Toxicology
Identifiers
urn:nbn:se:liu:diva-202257 (URN)10.1186/s13073-024-01314-7 (DOI)001190561100002 ()38509600 (PubMedID)
Note

Funding Agencies|European Commission grant

Available from: 2024-04-09 Created: 2024-04-09 Last updated: 2025-02-11
Gawel, D., Bojner Horwitz, E., Sysoev, O., Jacobsson, B., Jönsson, J.-I., Melén, E., . . . Benson, M. (2021). Stor potential när genomikdatakan implementeras i klinisk rutin: [Clinical translation of genomic medicine]. Läkartidningen, 118
Open this publication in new window or tab >>Stor potential när genomikdatakan implementeras i klinisk rutin: [Clinical translation of genomic medicine]
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2021 (Swedish)In: Läkartidningen, ISSN 0023-7205, E-ISSN 1652-7518, Vol. 118Article, review/survey (Refereed) Published
Abstract [en]

Recent technical developments and early clinical examples support that precision medicine has potential to provide novel diagnostic and therapeutic solutions for patients with complex diseases, who are not responding to existing therapies. Those solutions will require integration of genomic data with routine clinical, imaging, sensor, biobank and registry data. Moreover, user-friendly tools for informed decision support for both patients and clinicians will be needed. While this will entail huge technical, ethical, societal and regulatory challenges, it may contribute to transforming and improving health care towards becoming predictive, preventive, personalised and participatory (4P-medicine).

Abstract [sv]

Ett av de största problemen för många patienter är attde inte förbättras av läkemedelsbehandling. Detta orsakar, förutom lidande, stora samhällskostnader. Viktigaorsaker är sjukdomars molekylära komplexitet samt sendiagnostik och behandling.

Precisionsmedicin kan bidra till att lösa problem genom att karakterisera och tolka denna komplexitet. Ettexempel är att läkemedelsbehandling kan provas ut ochindividualiseras genom datorsimuleringar.

Precisionsmedicin har potential att påtagligt förbättrabåde hälsa och ekonomi. Detta kräver en bred IT-strategi för att integrera genomik- och andra storskaligadatakällor, som inkluderar nationella superdator-,visualiserings- och AI-centrum.

Place, publisher, year, edition, pages
Sveriges Läkarförbund, 2021
National Category
Medical Ethics
Identifiers
urn:nbn:se:liu:diva-185093 (URN)33977515 (PubMedID)
Available from: 2022-05-17 Created: 2022-05-17 Last updated: 2022-05-31Bibliographically approved
Sysoev, O. & Burdakov, O. (2019). A smoothed monotonic regression via L2 regularization. Knowledge and Information Systems, 59(1), 197-218
Open this publication in new window or tab >>A smoothed monotonic regression via L2 regularization
2019 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 59, no 1, p. 197-218Article in journal (Refereed) Published
Abstract [en]

Monotonic regression is a standard method for extracting a monotone function from non-monotonic data, and it is used in many applications. However, a known drawback of this method is that its fitted response is a piecewise constant function, while practical response functions are often required to be continuous. The method proposed in this paper achieves monotonicity and smoothness of the regression by introducing an L2 regularization term. In order to achieve a low computational complexity and at the same time to provide a high predictive power of the method, we introduce a probabilistically motivated approach for selecting the regularization parameters. In addition, we present a technique for correcting inconsistencies on the boundary. We show that the complexity of the proposed method is O(n2). Our simulations demonstrate that when the data are large and the expected response is a complicated function (which is typical in machine learning applications) or when there is a change point in the response, the proposed method has a higher predictive power than many of the existing methods.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Monotonic regression, Kernel smoothing, Penalized regression, Probabilistic learning, Constrained optimization
National Category
Probability Theory and Statistics Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-147628 (URN)10.1007/s10115-018-1201-2 (DOI)000461390300008 ()
Available from: 2018-04-27 Created: 2018-04-27 Last updated: 2019-04-03Bibliographically approved
Sysoev, O., Bartoszek, K., Ekström, E.-C. & Ekström Selling, K. (2019). PSICA: Decision trees for probabilistic subgroup identification with categorical treatments. Statistics in Medicine, 38(22), 4436-4452
Open this publication in new window or tab >>PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
2019 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 22, p. 4436-4452Article in journal (Refereed) Published
Abstract [en]

Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two‐arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community‐based nutrition intervention trial that justifies the validity of our method.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019
National Category
Computer Sciences Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-159305 (URN)10.1002/sim.8308 (DOI)000484974200020 ()31246349 (PubMedID)2-s2.0-85068189287 (Scopus ID)
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2020-01-29Bibliographically approved
Burdakov, O. & Sysoev, O. (2017). A Dual Active-Set Algorithm for Regularized Slope-Constrained Monotonic Regression. Iranian Journal of Operations Research, 8(2), 40-47
Open this publication in new window or tab >>A Dual Active-Set Algorithm for Regularized Slope-Constrained Monotonic Regression
2017 (English)In: Iranian Journal of Operations Research, ISSN 2008-1189, Vol. 8, no 2, p. 40-47Article in journal (Refereed) Published
Abstract [en]

In many problems, it is necessary to take into account monotonic relations. Monotonic (isotonic) Regression (MR) is often involved in solving such problems. The MR solutions are of a step-shaped form with a typical sharp change of values between adjacent steps. This, in some applications, is regarded as a disadvantage. We recently introduced a Smoothed MR (SMR) problem which is obtained from the MR by adding a regularization penalty term. The SMR is aimed at smoothing the aforementioned sharp change. Moreover, its solution has a far less pronounced step-structure, if at all available. The purpose of this paper is to further improve the SMR solution by getting rid of such a structure. This is achieved by introducing a lowed bound on the slope in the SMR. We call it Smoothed Slope-Constrained MR (SSCMR) problem. It is shown here how to reduce it to the SMR which is a convex quadratic optimization problem. The Smoothed Pool Adjacent Violators (SPAV) algorithm developed in our recent publications for solving the SMR problem is adapted here to solving the SSCMR problem. This algorithm belongs to the class of dual active-set algorithms. Although the complexity of the SPAV algorithm is o(n2) its running time is growing in our computational experiments almost linearly with n. We present numerical results which illustrate the predictive performance quality of our approach. They also show that the SSCMR solution is free of the undesirable features of the MR and SMR solutions.

Place, publisher, year, edition, pages
Tehran: , 2017
Keywords
Monotonic regression, Regularization, Quadratic penalty, Convex quadratic optimization, Dual active-set method, Large-scale optimization
National Category
Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-148061 (URN)10.29252/iors.8.2.40 (DOI)
Available from: 2018-05-29 Created: 2018-05-29 Last updated: 2018-06-07Bibliographically approved
Sysoev, O. & Burdakov, O. (2016). A Smoothed Monotonic Regression via L2 Regularization. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>A Smoothed Monotonic Regression via L2 Regularization
2016 (English)Report (Other academic)
Abstract [en]

Monotonic Regression (MR) is a standard method for extracting a monotone function from non-monotonic data, and it is used in many applications. However, a known drawback of this method is that its fitted response is a piecewise constant function, while practical response functions are often required to be continuous. The method proposed in this paper achieves monotonicity and smoothness of the regression by introducing an L2 regularization term, and it is shown that the complexity of this method is O(n2). In addition, our simulations demonstrate that the proposed method normally has higher predictive power than some commonly used alternative methods, such as monotonic kernel smoothers. In contrast to these methods, our approach is probabilistically motivated and has connections to Bayesian modeling.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. p. 17
Series
LiTH-MAT-R, ISSN 0348-2960 ; 2016:01
Keywords
Monotonic regression, Kernel smoothing, Penalized regression, Bayesian modeling
National Category
Probability Theory and Statistics Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-125398 (URN)LiTH-MAT-R--2016/01--SE (ISRN)
Available from: 2016-02-22 Created: 2016-02-22 Last updated: 2016-09-26Bibliographically approved
Kalish, M. L., Dunn, J. C., Burdakov, O. P. & Sysoev, O. (2016). A statistical test of the equality of latent orders. Journal of mathematical psychology (Print), 70, 1-11, Article ID YJMPS2051.
Open this publication in new window or tab >>A statistical test of the equality of latent orders
2016 (English)In: Journal of mathematical psychology (Print), ISSN 0022-2496, E-ISSN 1096-0880, Vol. 70, p. 1-11, article id YJMPS2051Article in journal (Refereed) Published
Abstract [en]

It is sometimes the case that a theory proposes that the population means on two variables should have the same rank order across a set of experimental conditions. This paper presents a test of this hypothesis. The test statistic is based on the coupled monotonic regression algorithm developed by the authors. The significance of the test statistic is determined by comparison to an empirical distribution specific to each case, obtained via non-parametric or semi-parametric bootstrap. We present an analysis of the power and Type I error control of the test based on numerical simulation. Partial order constraints placed on the variables may sometimes be theoretically justified. These constraints are easily incorporated into the computation of the test statistic and are shown to have substantial effects on power. The test can be applied to any form of data, as long as an appropriate statistical model can be specified.

Place, publisher, year, edition, pages
Academic Press, 2016
Keywords
State-trace analysis, Monotonic regression, Hypothesis test
National Category
Other Mathematics
Identifiers
urn:nbn:se:liu:diva-122765 (URN)10.1016/j.jmp.2015.10.004 (DOI)000372686500001 ()
Note

free access is valid until January 8, 2016:

http://authors.elsevier.com/a/1S3XC53naPWGh

Funding agencies: Australian Research Council [0877510, 0878630, 110100751, 130101535]; National Science Foundation [1256959]; Linkoping University

Available from: 2015-11-21 Created: 2015-11-21 Last updated: 2017-12-01Bibliographically approved
Sysoev, O., Grimvall, A. & Burdakov, O. (2016). Bootstrap confidence intervals for large-scale multivariate monotonic regression problems. Communications in statistics. Simulation and computation, 45(3), 1025-1040
Open this publication in new window or tab >>Bootstrap confidence intervals for large-scale multivariate monotonic regression problems
2016 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 3, p. 1025-1040Article in journal (Refereed) Published
Abstract [en]

Recently, the methods used to estimate monotonic regression (MR) models have been substantially improved, and some algorithms can now produce high-accuracy monotonic fits to multivariate datasets containing over a million observations. Nevertheless, the computational burden can be prohibitively large for resampling techniques in which numerous datasets are processed independently of each other. Here, we present efficient algorithms for estimation of confidence limits in large-scale settings that take into account the similarity of the bootstrap or jackknifed datasets to which MR models are fitted. In addition, we introduce modifications that substantially improve the accuracy of MR solutions for binary response variables. The performance of our algorithms isillustrated using data on death in coronary heart disease for a large population. This example also illustrates that MR can be a valuable complement to logistic regression.

Place, publisher, year, edition, pages
Taylor & Francis, 2016
Keywords
Big data, Bootstrap, Confidence intervals, Monotonic regression, Pool- adjacent-violators algorithm
National Category
Probability Theory and Statistics Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-85169 (URN)10.1080/03610918.2014.911899 (DOI)000372527900014 ()
Note

Vid tiden för disputation förelåg publikationen som manuskript

Available from: 2012-11-08 Created: 2012-11-08 Last updated: 2017-12-13
Burdakov, O. & Sysoev, O. (2016). Regularized monotonic regression. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Regularized monotonic regression
2016 (English)Report (Other academic)
Abstract [en]

Monotonic (isotonic) Regression (MR) is a powerful tool used for solving a wide range of important applied problems. One of its features, which poses a limitation on its use in some areas, is that it produces a piecewise constant fitted response. For smoothing the fitted response, we introduce a regularization term in the MR formulated as a least distance problem with monotonicity constraints. The resulting Smoothed Monotonic Regrassion (SMR) is a convex quadratic optimization problem. We focus on the SMR, where the set of observations is completely (linearly) ordered. Our Smoothed Pool-Adjacent-Violators (SPAV) algorithm is designed for solving the SMR. It belongs to the class of dual activeset algorithms. We proved its finite convergence to the optimal solution in, at most, n iterations, where n is the problem size. One of its advantages is that the active set is progressively enlarging by including one or, typically, more constraints per iteration. This resulted in solving large-scale SMR test problems in a few iterations, whereas the size of that problems was prohibitively too large for the conventional quadratic optimization solvers. Although the complexity of the SPAV algorithm is O(n2), its running time was growing in our computational experiments in proportion to n1:16.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. p. 20
Series
LiTH-MAT-R, ISSN 0348-2960 ; 2016:02
Keywords
Monotonic regression, regularization, quadratic penalty, convex quadratic optimization, dual active-set method, large-scale optimization
National Category
Computational Mathematics Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-128117 (URN)LiTH-MAT-R--2016/02--SE (ISRN)
Available from: 2016-05-17 Created: 2016-05-17 Last updated: 2016-09-28Bibliographically approved
Sysoev, O., Grimvall, A. & Burdakov, O. (2013). Bootstrap estimation of the variance of the error term in monotonic regression models. Journal of Statistical Computation and Simulation, 83(4), 625-638
Open this publication in new window or tab >>Bootstrap estimation of the variance of the error term in monotonic regression models
2013 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 83, no 4, p. 625-638Article in journal (Refereed) Published
Abstract [en]

The variance of the error term in ordinary regression models and linear smoothers is usually estimated by adjusting the average squared residual for the trace of the smoothing matrix (the degrees of freedom of the predicted response). However, other types of variance estimators are needed when using monotonic regression (MR) models, which are particularly suitable for estimating response functions with pronounced thresholds. Here, we propose a simple bootstrap estimator to compensate for the over-fitting that occurs when MR models are estimated from empirical data. Furthermore, we show that, in the case of one or two predictors, the performance of this estimator can be enhanced by introducing adjustment factors that take into account the slope of the response function and characteristics of the distribution of the explanatory variables. Extensive simulations show that our estimators perform satisfactorily for a great variety of monotonic functions and error distributions.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2013
Keywords
uncertainty estimation; bootstrap; monotonic regression; pool-adjacent-violators algorithm
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-78858 (URN)10.1080/00949655.2011.631138 (DOI)000317276900003 ()
Available from: 2012-06-21 Created: 2012-06-21 Last updated: 2017-12-07
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