Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

A Generalized Scalar Potential Integral Equation Formulation for the DC Analysis of Conductors

The electrostatic modeling of conductors is a fundamental challenge in various applications, including the prediction of parasitic effects in electrical interconnects, the design of biasing networks, and the modeling of biological, microelectromechanical, and sensing systems. The boundary element method (BEM) can be an effective simulation tool for these problems because it allows modeling three-dimensional objects with only a surface mesh. However, existing BEM formulations can be restrictive because they make assumptions specific to particular applications. For example, capacitance extraction formulations usually assume a constant electric scalar potential on the surface of each conductor and cannot be used to model a flowing current, nor to extract the resistance. When modeling steady currents, many existing techniques do not address mathematical challenges such as the null space associated with the operators representing the internal region of a conductor. We propose a more general BEM framework based on the electric scalar potential for modeling conductive objects in various scenarios in a unified manner. Restrictive application-specific assumptions are not made, and the aforementioned operator null space is handled in an intuitive and rigorous manner. Numerical examples drawn from diverse applications confirm the accuracy and generality of the proposed method.Comment: 12 pages, 13 figures. Submitted to the IEEE Transactions on Antennas and Propagatio

Računalna analiza genotipova i N-glikoma ljudske plazme

Glycosylation is one of the most extensive protein modifications. Glycans influence both structure and function of the proteins and known to have important roles in physiological and pathological processes. The absence of a universal code for glycan synthesis combined with the technological challenges faced by glycan quantification analysis has hindered the knowledge about the processes regulating the assembly of glycans. Major breakthroughs in analytical procedures created the possibility to reliably quantify glycans in a high-throughput manner and allowed the first large-scale studies on human plasma N-glycome. In order to explore the genomic and environmental regulation of glycosylation, different computational methods were employed to the integrated analysis of glycan, physiological/biochemical and genotype data in three isolated population cohorts. Specific glyco-phenotypes were identified in the general population and the potential use of glycan modifications as biomarkers was evaluated for the particular case of diabetes. General associations between glycans and phenotypes were observed and glycan, phenotypic and genotypic patterns capable of discriminating the populations were explored. The analysis of polymorphisms associated with glycosylation was addressed replicating previous findings and suggesting possible novel associations.Glikozilacija je jedna od najopsežnijih modifikacija proteina. Glikani utječu na strukturu i funkciju proteina na koje su vezani, a poznato je i da imaju važne uloge u fiziološkim i patološkim procesima. Nedostatak univerzalnog koda za sintezu glikana zajedno sa tehnološkim poteškoćama kvantifikacije glikana razlozi su ograničenom razumijevanju procesa koji reguliraju njihovu sintezu. Značajni napretci u analitičkim postupcima omogućili su razvoj pouzdanih visoko-protočnih metoda za kvantifikaciju glikana, a time i prve studije plazma N-glikoma velikog broja ljudi. Kako bi se istražila genomska i okolišna regulacija glikozilacije, u ovome su radu glikanski, fiziološki i biokemijski podaci te genotipovi iz tri različite izolirane populacije analizirani različitim računalnim metodama. Identificirani su glikanski profili specifični za opću populaciju evaluiran je potencijal glikana kao biomarkera dijabetesa. Također, analizirane su asocijacije glikana i fenotipova te su istraženi glikanski, fenotipski i genotipski uzorci koji definiraju pojedine populacije. Analize polimorfizama povezanih sa glikozilacijom potvrdile su prethodna otkrića te su otkrivene nove potencijalne poveznice

Computational Labeling, Partitioning, and Balancing of Molecular Networks

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional behaviors. We build three computational models to label, partition, and balance molecular networks using modern machine learning techniques. (1) Due to the incompleteness of protein functional annotation, we develop AptRank, an adaptive PageRank model for protein function prediction on bilayer networks. By integrating Gene Ontology (GO) hierarchy with protein-protein interaction network, our AptRank outperforms four state-of-the-art methods in a comprehensive evaluation using benchmark datasets. (2) We next extend our AptRank into a network partitioning method, BioSweeper, to identify functional network modules in which molecules share similar functions and also densely connect to each other. Compared to traditional network partitioning methods using only network connections, BioSweeper, which integrates the GO hierarchy, can automatically identify functionally enriched network modules. (3) Finally, we conduct a differential interaction analysis, namely difFBA, on protein-protein interaction networks by simulating protein fluxes using flux balance analysis (FBA). We test difFBA using quantitative proteomic data from colon cancer, and demonstrate that difFBA offers more insights into functional changes in molecular behavior than does protein quantity changes alone. We conclude that our integrative network model increases the observational dimensions of complex biological systems, and enables us to more deeply understand the causal relationships between genotypes and phenotypes

Development of optical methods for real-time whole-brain functional imaging of zebrafish neuronal activity

Each one of us in his life has, at least once, smelled the scent of roses, read one canto of Dante’s Commedia or listened to the sound of the sea from a shell. All of this is possible thanks to the astonishing capabilities of an organ, such as the brain, that allows us to collect and organize perceptions coming from sensory organs and to produce behavioural responses accordingly. Studying an operating brain in a non-invasive way is extremely difficult in mammals, and particularly in humans. In the last decade, a small teleost fish, zebrafish (Danio rerio), has been making its way into the field of neurosciences. The brain of a larval zebrafish is made up of 'only' 100000 neurons and it’s completely transparent, making it possible to optically access it. Here, taking advantage of the best of currently available technology, we devised optical solutions to investigate the dynamics of neuronal activity throughout the entire brain of zebrafish larvae