BrainMap

Aurel Mihail Baloi

- GRAPHIT INNOVATION FACTORY S.R.L.

Manager | PhD student

Web of Science ResearcherID: not public

Expertise & keywords

Development of an Integrative tool for Comprehensive Profiling of the Migratory Phenotype of Tumor Cells based on Microfluidic Platforms and Machine Learning Algorithms Call name: PNCDI IV, SP 5.7.1 - Proiect experimental demonstrativ
PN-IV-P7-7.1-PED-2024-2349 2025 - 2026

Role in this project:

Coordinating institution: INSTITUTUL ONCOLOGIC PROF.DR.I.CHIRICUTA CLUJ-NAPOCA

Project partners: INSTITUTUL ONCOLOGIC PROF.DR.I.CHIRICUTA CLUJ-NAPOCA (RO); GRAPHIT INNOVATION FACTORY S.R.L. (RO)

Affiliation:

Project website:

Abstract:

The proposed project addresses a research topic in the field of breast cancer, the evaluation and invasion of tumor cells. To be able to analyze the single cluster migration of tumor cells, the project proposes an integrative tool, MCELLML, for the comprehensive analysis of the migratory phenotype of tumor cells, based on microfluidic platforms, together with image processing and machine learning algorithms. This approach aims to obtain a tool that can be used to predict the metastatic potential of any given tumor, based on direct phenotypic data. The research project is carried out in a public-private partnership, putting together the know-how from the medical area with the applicability of computational algorithms with automatic learning. In terms of informatics approaches, the project proposes algorithms used for the automatic tracking of cell clusters migrating through microfluidic channels and for the calculation of essential characteristics of single cells and cell clusters integrated into a software application. The novelty of the proposed application is represented by the development of two different neural networks, used for the automatic identification of cell cluster events and for the migration phenotype identification, as mesenchymal or amoeboid. The software application uses the experience of medical doctors in labeling the phenotype of migrating cells, for training and validation procedures for the machine learning algorithm.

Transforming a massive volume of data and geospatial information into actionable intelligence Call name: P 2 - SP 2.1 - Soluţii - 2021
PN-III-P2-2.1-SOL-2021-0084 2021 - 2023

Role in this project:

Coordinating institution: Academia Tehnică Militară „FERDINAND I”

Project partners: Academia Tehnică Militară „FERDINAND I” (RO); INTERGRAPH COMPUTER SERVICES S.R.L. (RO); UNIVERSITATEA NAŢIONALĂ DE ŞTIINŢĂ ŞI TEHNOLOGIE POLITEHNICA BUCUREŞTI (RO); AGENTIA SPATIALA ROMANA (RO); UNIVERSITATEA BUCURESTI (RO)

Affiliation:

Project website: https://mta.ro/bdageoint

Abstract:

BDAGeoINT is a project that aims to use emerging technologies for ingesting, visualizing and storing structured and unstructured massive data sets (Big Data), as well as processing and analyzing this information in order to transform it into actionable intelligence.

It proposes an innovative conceptual framework by integrating machine learning in related fields, which capitalizes the identification methods of the entities and of the relationships between them, as a result of processing spatio-temporal data, text data and image/audio/video data. This approach is achieved by exploiting distributed analysis and optimal storage for running complex analyzes in order to generate actionable intelligence.

From a technological point of view, the project combines mature technologies (COTS: Luciad, ERDAS Apollo) with emerging technologies (open source) such as: GPU processing, HDFS (Hadoop Distributed File System), Python and Cython, machine learning algorithms for identifying entities and spatial data processing.

BDAGeoINT will seek to respond to the information cycle of an intelligence organization and the relational context in which it operates (national security system, strategic relations systems and strategic partnerships).

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects