Titulo Estágio
Analyzing Gene Expression Data using Spiking Neural Networks
Local do Estágio
Laboratory of Artificial Neural Networks (LARN-CISUC)
Enquadramento
Spiking Neural Networks (SNNs) are computational learning algorithms inspired by biological neurons that have attracted the attention of researchers due to their potential for more energy-efficient computing and their ability to analyze patterns in biological data. SNNs can learn gene interactions using information encoded from gene expression data in time series in the form of spikes. Temporal interactions are learned, and activity patterns are extracted and can be analyzed as a network of genetic interactions. In this study, the goal is to apply SNN architectures to model, analyze, and extract information about the regulatory processes of gene expression that are important for drug development and disease treatment through the discovery of biomarkers from gene expression patterns.
Objetivo
1.Analysis and Modeling of Gene Interactions: Develop SNN models capable of learning gene interactions using gene expression data information;
2.Computational Efficiency: Demonstrate the efficiency of SNNs in operating on gene expression data compared to existing models for describing the regulatory processes of gene expression over time;
3.Contributions to Future Research: Provide insights into the practical implementation of SNNs in bioinformatics and inspire the exploration of bio-inspired computing paradigms for modeling gene expression data.
Plano de Trabalhos - Semestre 1
1.Study SNN methodologies and data representations used in gene expression analysis;
2. Analyze the requirements of the architecture to be developed, considering existing methodologies;
3. Design the architecture of the framework to be developed and create a prototype;
4. Write an intermediate internship report.
Plano de Trabalhos - Semestre 2
1.Gather gene expression data from publicly available repositories described in the literature (NCBI, Gene Expression Omnibus (GEO) repository, etc.);
2.Implement preprocessing steps to define the model input data;
3.Design and train SNN architectures using frameworks such as PyTorch or TensorFlow, exploring different neural network configurations and learning strategies;
4.Evaluate the performance of SNN models using appropriate metrics and analyze model efficiency for gene expression data modeling;
5.Submit an article to a journal or conference (to be determined);
6.Write and submit the thesis.
Condições
The internship will take place at the Laboratory of Artificial Neural Networks (LARN-CISUC) with regular meetings with the supervision team. The candidate should be strongly motivated to conduct research in the areas of artificial intelligence, machine learning and data science. Experience in Python and artificial neural networks is essential, as well as in related machine learning algorithms and software/programming tools. The selected candidate will be integrated within the research team currently working on the ongoing project NextGenAI – Centre For Responsible AI, supported by the National Recovery and Resilience Plan (PRR) and the Next Generation EU Funds.
Observações
A research grant opportunity, supported by the abovementioned project, will become available during the internship.
Orientador
Bernardete Ribeiro (Prof.) / Joel Arrais (Prof.) / Luis Torres (Prof. Convidado)
luistorres@dei.uc.pt 📩