| 07-03-2023 |
SimSpread-Ensemble - Development of an ensemble network-based method for drug discovery
SimSpread is a novel computational method to predict protein–ligand interaction that combines network-based inference with chemical similarity, useful for predicting drug targets, virtual screening, and drug repositioning. This project proposal intents to improve several limitations of SimSpread. Hypothesis: The combination of predictions obtained from SimSpread that use different similarity cutoffs into a single score using a machine learning (ML) model will increase predictive performance, eliminate empty predictions and eliminate the need to optimize similarity cutoff parameter, resulting in a more straightfoward and user-friendly model to predict drug-target interactions. Tasks: • Propose an ensemble predictive model. • Implement a hyperparameter optimization. • Compare the predictive performance. Candidates should have good programming skills.
Keywords:
machine learning
bioinformatica
red
red social
aprendizaje maquina
Proteínas
Diseño de fármacos
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 20 créditos y tiene 1/1 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 07-03-2023 |
Keywords:
machine learning
bioinformatica
red
red social
aprendizaje maquina
Proteínas
Diseño de fármacos
Prerequisites:
None.
Evaluation method: Nota 1-7, with 1/1 available vacants |
Mentor(s): Open in the plataform |
| 05-12-2022 |
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 06-07-2022 |
Automatic 3D image integration in a portable device using deep learning
To prevent melanoma in patients with many lesions under surveillance, one challenge is the integration of dermatological images for effective lesion monitoring. We are developing a portable device based on NVidia microcomputer with stereo cameras to achieve the automatic integration of 3D images of the skin of patients. In this project we will implement machine learning techniques to merge and record images on dedicated embedded hardware. Tasks in this project will include operating hardware including microcomputers and cameras, developing and implementing computer vision and image processing algorithms. Experience in programming, computer vision, and machine learning is welcome but not necessary.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/3 available vacants |
Mentor(s): Open in the plataform |
| 05-01-2022 |
Keywords:
control automático
aprendizaje maquina
Prerequisites:
ICM2813
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |