Source: http://mensxmachina.org/en/
Timestamp: 2019-04-20 18:17:04+00:00

Document:
I. Ferreirós-Vidal, T. Carroll, T. Zhang, V. Lagani, R. N. Ramirez, E. Ing-Simmons, A. Garcia, L. Cooper, Z. Liang, G. Papoutsoglou, G. Dharmalingam, Y. Guo, S. Tarazona, S. J. Fernandes, P. Noori, G. Silberberg, A. G. Fisher, I. Tsamardinos, A. Mortazavi, B. Lenhard, A. Conesa, J. Tegner, M. Merkenschlager, and D. Gomez-Cabrero, “Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation,” PLOS Biology, vol. 17, iss. 4, pp. 1-28, 2019.
Y. Pantazis and I. Tsamardinos, “A unified approach for sparse dynamical system inference from temporal measurements,” , 2019.
Temporal variations in biological systems and more generally in natural sciences are typically modeled as a set of ordinary, partial or stochastic differential or difference equations. Algorithms for learning the structure and the parameters of a dynamical system are distinguished based on whether time is discrete or continuous, observations are time-series or time-course and whether the system is deterministic or stochastic, however, there is no approach able to handle the various types of dynamical systems simultaneously.In this paper, we present a unified approach to infer both the structure and the parameters of non-linear dynamical systems of any type under the restriction of being linear with respect to the unknown parameters. Our approach, which is named Unified Sparse Dynamics Learning (USDL), constitutes of two steps. First, an atemporal system of equations is derived through the application of the weak formulation. Then, assuming a sparse representation for the dynamical system, we show that the inference problem can be expressed as a sparse signal recovery problem, allowing the application of an extensive body of algorithms and theoretical results. Results on simulated data demonstrate the efficacy and superiority of the USDL algorithm under multiple interventions and/or stochasticity. Additionally, USDL’s accuracy significantly correlates with theoretical metrics such as the exact recovery coefficient. On real single-cell data, the proposed approach is able to induce high-confidence subgraphs of the signaling pathway.Source code is available at Bioinformatics online. USDL algorithm has been also integrated in SCENERY (http://scenery.csd.uoc.gr/); an online tool for single-cell mass cytometry analytics.Supplementary data are available at Bioinformatics online.
“Forward-Backward Selection with Early Dropping,” Journal of Machine Learning Research, vol. 20, pp. 1-39, 2019.
K. Tsirlis, V. Lagani, S. Triantafillou, and I. Tsamardinos, “On scoring Maximal Ancestral Graphs with the Max\textendashMin Hill Climbing algorithm,” International Journal of Approximate Reasoning, vol. 102, pp. 74-85, 2018.
We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max–Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraint-based first phase that greatly reduces the space of structures to investigate. On a large scale experimentation we show that the proposed algorithm greatly improves on GSMAG in all comparisons, and over a set of known networks from the literature it compares positively against FCI and cFCI as well as competitively against GFCI, three well known constraint-based approaches for causal-network reconstruction in presence of latent confounders.
M. Tsagris, “Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation,” Applied Artificial Intelligence , vol. 33, iss. 2, pp. 101-123, 2018.
PC is a prototypical constraint-based algorithm for learning Bayesian networks, a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg, was developed to make the skeleton phase order independent. In return, it has notably increased execution time. In this paper, we clarify that the PC algorithm the skeleton phase of PC is indeed order independent. The modification we propose outperforms pcalg’s variant of the PC in terms of returning correct networks of better quality as is less prone to errors and in some cases it is a lot more computationally cheaper. In addition, we show that pcalg’s variant does not return valid acyclic graphs.
I. Tsamardinos, E. Greasidou, and G. Borboudakis, “Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation,” Machine Learning, vol. 107, iss. 12, pp. 1895-1922, 2018.
Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration is optimistically biased. We present an efficient bootstrap method that corrects for the bias, called Bootstrap Bias Corrected CV (BBC-CV). BBC-CV’s main idea is to bootstrap the whole process of selecting the best-performing configuration on the out-of-sample predictions of each configuration, without additional training of models. In comparison to the alternatives, namely the nested cross-validation (Varma and Simon in BMC Bioinform 7(1):91, 2006) and a method by Tibshirani and Tibshirani (Ann Appl Stat 822–829, 2009), BBC-CV is computationally more efficient, has smaller variance and bias, and is applicable to any metric of performance (accuracy, AUC, concordance index, mean squared error). Subsequently, we employ again the idea of bootstrapping the out-of-sample predictions to speed up the CV process. Specifically, using a bootstrap-based statistical criterion we stop training of models on new folds of inferior (with high probability) configurations. We name the method Bootstrap Bias Corrected with Dropping CV (BBCD-CV) that is both efficient and provides accurate performance estimates.
I. Tsamardinos, G. Borboudakis, P. Katsogridakis, P. Pratikakis, and V. Christophides, “A greedy feature selection algorithm for Big Data of high dimensionality,” Machine Learning, 2018.
We present the Parallel, Forward–Backward with Pruning (PFBP) algorithm for feature selection (FS) for Big Data of high dimensionality. PFBP partitions the data matrix both in terms of rows as well as columns. By employing the concepts of p-values of conditional independence tests and meta-analysis techniques, PFBP relies only on computations local to a partition while minimizing communication costs, thus massively parallelizing computations. Similar techniques for combining local computations are also employed to create the final predictive model. PFBP employs asymptotically sound heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores. An extensive comparative evaluation also demonstrates the effectiveness of PFBP against other algorithms in its class. The heuristics presented are general and could potentially be employed to other greedy-type of FS algorithms. An application on simulated Single Nucleotide Polymorphism (SNP) data with 500K samples is provided as a use case.
M. Adamou, G. Antoniou, E. Greasidou, V. Lagani, P. Charonyktakis, I. Tsamardinos, and M. Doyle, “Toward Automatic Risk Assessment to Support Suicide Prevention,” Crisis, pp. 1-8, 2018.
K. Lakiotaki, N. Vorniotakis, M. Tsagris, G. Georgakopoulos, and I. Tsamardinos, “BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology,” Database, iss. bay011, 2018.
M. Markaki, I. Tsamardinos, A. Langhammer, V. Lagani, K. Hveem, and O. D. Røe, “A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types: A HUNT Study.,” EBioMedicine, 2018.
Lung cancer causes >1·6 million deaths annually, with early diagnosis being paramount to effective treatment. Here we present a validated risk assessment model for lung cancer screening. The prospective HUNT2 population study in Norway examined 65,237 people aged >20years in 1995-97. After a median of 15·2years, 583 lung cancer cases had been diagnosed; 552 (94·7%) ever-smokers and 31 (5·3%) never-smokers. We performed multivariable analyses of 36 candidate risk predictors, using multiple imputation of missing data and backwards feature selection with Cox regression. The resulting model was validated in an independent Norwegian prospective dataset of 45,341 ever-smokers, in which 675 lung cancers had been diagnosed after a median follow-up of 11·6years. Our final HUNT Lung Cancer Model included age, pack-years, smoking intensity, years since smoking cessation, body mass index, daily cough, and hours of daily indoors exposure to smoke. External validation showed a 0·879 concordance index (95% CI 0·866-0·891) with an area under the curve of 0·87 (95% CI 0·85-0·89) within 6years. Only 22% of ever-smokers would need screening to identify 81·85% of all lung cancers within 6years. Our model of seven variables is simple, accurate, and useful for screening selection.
M. Panagopoulou, M. Karaglani, I. Balgkouranidou, V. Vasilakakis, E. Biziota, T. Koukaki, E. Karamitrousis, E. Nena, I. Tsamardinos, G. Kolios, E. Lianidou, S. Kakolyris, and E. Chatzaki, “Circulating cell free DNA in Breast cancer: size profiling, levels and methylation patterns lead to prognostic and predictive classifiers,” (to appear) Oncogene , 2018.
Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical information for cancer, meeting the need for a minimally-invasive advancement in the route of precision medicine. In this paper, we evaluated the prognostic and predictive potential of ccfDNA parameters in early and advanced breast cancer. Groups consisted of 150 and 16 breast cancer patients under adjuvant and neoadjuvant therapy respectively, 34 patients with metastatic disease and 35 healthy volunteers. Direct quantification of ccfDNA in plasma revealed elevated concentrations correlated to the incidence of death, shorter PFS, and non-response to pharmacotherapy in the metastatic but not in the other groups. The methylation status of a panel of cancer-related genes chosen based on previous expression and epigenetic data (KLK10, SOX17, WNT5A, MSH2, GATA3) was assessed by quantitative methylation-specific PCR. All but the GATA3 gene was more frequently methylated in all the patient groups than in healthy individuals (all p < 0.05). The methylation of WNT5A was statistically significantly correlated to greater tumor size and poor prognosis characteristics and in advanced stage disease with shorter OS. In the metastatic group, also SOX17 methylation was significantly correlated to the incidence of death, shorter PFS, and OS. KLK10 methylation was significantly correlated to unfavorable clinicopathological characteristics and relapse, whereas in the adjuvant group to shorter DFI. Methylation of at least 3 or 4 genes was significantly correlated to shorter OS and no pharmacotherapy response, respectively. Classification analysis by a fully automated, machine learning software produced a single-parametric linear model using ccfDNA plasma concentration values, with great discriminating power to predict response to chemotherapy (AUC 0.803, 95% CI [0.606, 1.000]) in the metastatic group. Two more multi-parametric signatures were produced for the metastatic group, predicting survival and disease outcome. Finally, a multiple logistic regression model was constructed, discriminating between patient groups and healthy individuals. Overall, ccfDNA emerged as a highly potent predictive classifier in metastatic breast cancer. Upon prospective clinical evaluation, all the signatures produced could aid accurate prognosis.
M. Tsagris, V. Lagani, and I. Tsamardinos, ” Feature selection for high-dimensional temporal data,” BMC Bioinformatics, iss. 1, 2018.
M. Tsagris, G. Borboudakis, V. Lagani, and I. Tsamardinos, “Constraint-based causal discovery with mixed data,” International Journal of Data Science and Analytics, 2018.
M. Adamou, G. Antoniou, E. Greassidou, V. Lagani, P. Charonyktakis, and I. Tsamardinos, “Mining Free-Text Medical Notes for Suicide Risk Assessment.” 2018.
Suicide has been considered as an important public health issue for a very long time, and is one of the main causes of death worldwide. Despite suicide prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Advances in machine learning make it possible to attempt to predict suicide based on the analysis of relevant data to inform clinical practice. This paper reports on findings from the analysis of data of patients who died by suicide in the period 2013-2016 and made use of both structured data and free-text medical notes. We focus on examining various text-mining approaches to support risk assessment. The results show that using advance machine learning and text-mining techniques, it is possible to predict within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.
V. Lagani, G. Athineou, A. Farcomeni, M. Tsagris, and I. Tsamardinos, “Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets,” Journal of Statistical Software, vol. 80, iss. 7, 2017.
The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constraint-based learning of Bayesian networks. Most of the currently available feature selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. In that respect the SES algorithm subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. The SES algorithm is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning ‘mind from the machine’ in Latin. The MXM implementation of SES handles several data analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of predictive accuracy and that multiple, equally predictive signatures are actually present in real world data.
G. Orfanoudaki, M. Markaki, K. Chatzi, I. Tsamardinos, and A. Economou, “MatureP: prediction of secreted proteins with exclusive information from their mature regions,” Scientific Reports, vol. 7, iss. 1, p. 3263–, 2017.
More than a third of the cellular proteome is non-cytoplasmic. Most secretory proteins use the Sec system for export and are targeted to membranes using signal peptides and mature domains. To specifically analyze bacterial mature domain features, we developed MatureP, a classifier that predicts secretory sequences through features exclusively computed from their mature domains. MatureP was trained using Just Add Data Bio, an automated machine learning tool. Mature domains are predicted efficiently with ~92% success, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC). Predictions were validated using experimental datasets of mutated secretory proteins. The features selected by MatureP reveal prominent differences in amino acid content between secreted and cytoplasmic proteins. Amino-terminal mature domain sequences have enhanced disorder, more hydroxyl and polar residues and less hydrophobics. Cytoplasmic proteins have prominent amino-terminal hydrophobic stretches and charged regions downstream. Presumably, secretory mature domains comprise a distinct protein class. They balance properties that promote the necessary flexibility required for the maintenance of non-folded states during targeting and secretion with the ability of post-secretion folding. These findings provide novel insight in protein trafficking, sorting and folding mechanisms and may benefit protein secretion biotechnology.
G. Papoutsoglou, G. Athineou, V. Lagani, I. Xanthopoulos, A. Schmidt, S. éliás, J. Tegnér, and I. Tsamardinos, “SCENERY: a web application for (causal) network reconstruction from cytometry data,” Nucleic Acids Research, vol. 37, p. D412–D416, 2017.
Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/.
K. Siomos, E. Papadaki, I. Tsamardinos, K. Kerkentzes, M. Koygioylis, and C. Trakatelli, “Prothrombotic and Endothelial Inflammatory Markers in Greek Patients with Type 2 Diabetes Compared to Non-Diabetics,” Endocrinology & Metabolic Syndrome, iss. 1, 2017.
S. Triantafillou, V. Lagani, C. Heinze-Deml, A. Schmidt, J. Tegner, and I. Tsamardinos, “Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells,” Triantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, Tsamardinos I. Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells. Scientific Reports. 2017;7:12724. doi:10., 2017.
Mens Ex Machina, Mind from the Machine or “Ο από Μηχανής Νους” paraphrases the latin expression Deus Ex Machina, God from the Machine. The name was suggested by Lucy Sofiadou, Prof. Tsamardinos’ wife.
Theoretical, algorithmic, and applied research in all of the above areas; we are also involved in interdisciplinary collaborations with biologists, physicians and practitioners from other fields.
Educational activities, such as teaching university courses, tutorials, summers schools, as well as supervising undergraduate dissertations, masters projects, and Ph.D. theses.
Implementation of tools, systems, and code libraries to aid the dissemination of the research results.Funding is provided from through the University of Crete, often originating from European and International research grants.
Causal discovery methods and the induction of causal models from observational studies. Specifically, we have recently introduced the problem of Integrative Causal Analysis (INCA).
Feature selection (a.k.a. variable selection) for classification and regression.
Induction of graphical models, such as Bayesian Networks from data.
Analysis of biomedical data and applications of AI and Machine Learning methods to induce new biomedical knowledge.
Activity recognition in Ambient Intelligent environments.

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