Waldir Leoncio Netto

• Ghannoum, Salim; Fantini, Damiano; Zahoor, Muhammad; Reiterer, Veronika; Phuyal, Santosh & Netto, Waldir Leoncio [Show all 13 contributors for this article] (2023). A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression. PLoS Computational Biology. ISSN 1553-734X. 19(4). doi: 10.1371/journal.pcbi.1010995. Full text in Research Archive Show summary

  Our understanding of how speed and persistence of cell migration affects the growth rate and size of tumors remains incomplete. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation, a phenomenon often linked to alterations of cell migration. Golgi fragmentation was induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the experimentally obtained migration and invasion traits of Giantin depleted breast cancer cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression.

• Battauz, Michela & Netto, Waldir Leoncio (2023). A Likelihood Approach to Item Response Theory Equating of Multiple Forms. Applied Psychological Measurement. ISSN 0146-6216. 47(3), p. 200–220. doi: 10.1177/01466216231151702. Full text in Research Archive
• Netto, Waldir Leoncio; Wiberg, Marie & Battauz, Michela (2022). Evaluating Equating Transformations in IRT Observed-Score and Kernel Equating Methods. Applied Psychological Measurement. ISSN 0146-6216. doi: 10.1177/01466216221124087. Full text in Research Archive Show summary

  Test equating is a statistical procedure to ensure that scores from different test forms can be used interchangeably. There are several methodologies available to perform equating, some of which are based on the Classical Test Theory (CTT) framework and others are based on the Item Response Theory (IRT) framework. This article compares equating transformations originated from three different frameworks, namely IRT Observed-Score Equating (IRTOSE), Kernel Equating (KE), and IRT Kernel Equating (IRTKE). The comparisons were made under different data-generating scenarios, which include the development of a novel data-generation procedure that allows the simulation of test data without relying on IRT parameters while still providing control over some test score properties such as distribution skewness and item difficulty. Our results suggest that IRT methods tend to provide better results than KE even when the data are not generated from IRT processes. KE might be able to provide satisfactory results if a proper pre-smoothing solution can be found, while also being much faster than IRT methods. For daily applications, we recommend observing the sensibility of the results to the equating method, minding the importance of good model fit and meeting the assumptions of the framework.

• Costa, Denise Reis & Netto, Waldir Leoncio (2022). Process Data Analysis in ILSAs. In Nilsen, Trude; Stancel-Piątak, Agnes & Gustafsson, Jan-Eric (Ed.), International Handbook of Comparative Large-Scale Studies in Education. Springer. ISSN 9783030382988. p. 927–952. doi: 10.1007/978-3-030-88178-8_60. Full text in Research Archive Show summary

  Computational advancements in the last couple of decades have brought forth a new era of international large-scale assessments (ILSAs) in which the administration of computer-based tests is becoming the norm. Beyond collecting the correct/incorrect answers for each item, computer-based assessments are also able to collect a range of actions performed by the respondents in the computer testing application during the course of the test administration. Both respondents’ actions – starting a unit, clicking a button, spending time until inputting or submitting an answer, and so forth – and their overall behavior – what they do with their keyboards, mice, and even their own eyes – are recorded as a new set of data called “process data,” which are normally time-stamped and can be stored in the so-called log files. There may be plenty of useful insight into the respondent’s cognitive process, and process data can potentially become a relevant element in the scoring process of an assessment, validate test score interpretations, to name a few possibilities for the analysis of such data. This chapter aims to contribute to the body of knowledge in this area by offering (1) an introduction to process data, what kind of data it contains, its relation to the cognitive process, and how it can be organized into a proposed six-layered ecological framework that facilitates its analysis; (2) a literature review of 37 seminal and state-of-the-art publications produced in the last decade on the topic, which are then analyzed both in their chronological perspective as well as in how they fit into the ecological framework; and (3) a discussion of the potential and limitations of using process data in the assessment framework, including a view of what could be the next steps in the analysis of process data from ILSAs.

• Ghannoum, Salim; Netto, Waldir Leoncio; Fantini, Damiano; Ragan-Kelley, Benjamin; Parizadeh, Seyedamirabbas & Jonasson, Emma [Show all 9 contributors for this article] (2021). DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics. International Journal of Molecular Sciences. ISSN 1661-6596. 22(3). doi: 10.3390/ijms22031399. Full text in Research Archive Show summary

  The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

• Ghannoum, Salim; Antos, Kamil; Netto, Waldir Leoncio; Gomes, Cecil; Köhn-Luque, Alvaro & Farhan, Hesso (2021). CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems. Cells. 10(2), p. 1–18. doi: 10.3390/cells10020469. Full text in Research Archive Show summary

  Cell migration is a fundamental biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cell motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. One renowned goal in the field is to provide tools that track cell movement as comprehensively and automatically as possible. However, fully automated tracking over long intervals of time is challenged by dividing cells, thus calling for a combination of automated and supervised tracking. Furthermore, after the emergence of various experimental tools to monitor cell-cycle phases, it is of relevance to integrate the monitoring of cell-cycle phases and motility. We developed CellMAPtracer, a multiplatform tracking system that achieves that goal. It can be operated as a conventional, automated tracking tool of single cells in numerous imaging applications. However, CellMAPtracer also allows adjusting tracked cells in a semiautomated supervised fashion, thereby improving the accuracy and facilitating the long-term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical interface and does not require any coding or programming skills. CellMAPtracer is compatible with two- and three-color fluorescent ubiquitination-based cell-cycle indicator (FUCCI) systems and allows the user to accurately monitor various migration parameters throughout the cell cycle, thus having great potential to facilitate new discoveries in cell biology.

View all works in NVA

• Halnes, Geir; Javaid, Anam; Solvang, Michael; Nichele, Stefano; Riegler, Michael & Singstad, Bjørn-Jostein [Show all 82 contributors for this article] (2025). Proceedings of NORA’s annual conference 2025. Nordic Machine Intelligence (NMI). 5(3), p. 1–14. doi: 10.5617/nmi.12551. Full text in Research Archive

View all works in NVA