| 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 |
| 24-07-2020 |
Mobile Machine Learning: Investigación y Desarrollo
Los framework y técnicas de aprendizaje de máquina proporcionan varios cambios revolucionarios para el desarrollo de aplicaciones en dispositivos móviles e IoT. Esto se debe a la capacidad de esta tecnología para reforzar las aplicaciones en estos dispositivos, es decir, para permitir más experiencias de usuario, entre otras cosas más. La investigación y desarrollo está orientada a identificar ventajas y desventajas de diferentes ML (Machine Learning) Frameworks que permiten desarrollar aplicaciones para entornos móviles (por ejemplo basados en Android o iOS). El alumno realizará un trabajo de investigación de las diferentes propuestas de ML Frameworks y desarrollará una aplicación aplicada a un escenario en particular. Para el desarrollo del proyecto, el alumno cuenta con dispositivos y acceso al software para el desarrollo.
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 2/10 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 |
| 24-07-2020 |
Mobile Machine Learning: Research and Development
The framework and techniques of machine learning provide a number of revolutionary changes for the development of applications on mobile devices and IoT. This is due to the ability of this technology to enhance the applications in these devices, that is to say, to allow for more user experiences, among other things. Research and development is oriented to identify advantages and disadvantages of different ML (Machine Learning) Frameworks that allow to develop applications for mobile environments (for example based on Android or iOS). The student will perform a research work of the different proposals of ML Frameworks, and will develop an application applied to a particular scenario. For the development of the project, the student has devices and software access for the development.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 2/10 available vacants |
Mentor(s): Open in the plataform |