|Propostes de tesi||Investigadors/es|
High Performance Computing in Bioinformatics
This research line focuses in the use of HPC tecniques for optimizing and developping new bioinformatics tecniques taking advantatge of advanced computer architectures. Trying to use effectively environments like Supercomputing, HPC clusters, Grids, and Cloud Computing. And also exploring GPUs and other computing accelerators to enhance the performance of bioinformatic algorithms.
|Dr. Josep Jorba Esteve|
Application of Metaheuristics & Simulation in Bioinformatics
Metaheuristic algorithms are being applied to a large variety of bioinformatic problems, such as gene sequence analysis, molecular 3D structure prediction, microarray analysis, multiple sequence alignment, etc. Similarly, modeling and simulation methods are also employed in biosciences and bioinformatics, including: biological systems, healthcare facilities, epidemics spreading, etc. This research line aims at studying some of the potential applications of metaheuristic algorithms and simulation methods in the area of bioinformatics.
|Dr. Angel A. Juan Perez|
Application of Deep Learning to Bioinformatics
Currently there exist a large amount of biomedical data available. In the age of Big Data, the need for new pattern discovery methods urges computer scientists to effectively collaborate with biologists and computational biologists. In this partnership, one of the most interesting and explored fields is machine learning (ML). Nevertheless, during the last years Deep Learning methods have dominated most of the ML applications (Natural Language Processing, Computer Vision, etc.).
This research proposal pretends to cover the applications of Deep Learning to all kind of biological data. Particularly we will develop: novel CNN (convolutional Neural Networks) architectures to model Gene Expression regulation, image segmentation, or protein structures among others; Recurrent neural networks applied to sequence understanding (RNN, LSTM and GRUs), and other novel schemes such as Deep Reinforcement Learning or Generative Adversarial Networks, which can alleviate the need for large scale labelled data.
Alipanahi, Babak, et al. "Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning." Nature biotechnology 33.8 (2015): 831-838.
|Dr. David Masip Rodó|