Proposals

List of the proposed projects

• MASSIV: Alternative splicing-inspired protein development Eukaryotes have evolved a transcription machinery that can augment the protein repertoire without increasing the genome size. The challenge here will be to design an artificial system able to learn the underlying rules of (functional) AS and to generalize to any protein sequence. see details.

• Projects/READY-GO: Prediction of protein functional from peptide sequences We propose to develop a probabilistic model able to identify such signals and guide the discovery of novel regulatory mechanisms associated to them. The model should be interpretable. see details.

• CASF: Comparative assessment of scoring functions for docking problem

Pharmacological research is concentrated on constructing a molecule, a ligand, which docks a given protein. This research is expensive since it goes in-vitro. We have to propose a deep neural net to forecast the probability of docking, see details.

• 3D-image reconstruction for the lens-free microscopy

One has to reconstruct an image of a small biological object (of hundreds micrometers, like a eucaryotic cell). Its video comes from a device of a brand-new technology: the free lens microscopy. This project combines modelling of interference images and its Fourier transform and GANs, the generative and discriminative deep learning models see details.

• Atom-resolution synchrotron image reconstruction

For a given synchrotron image one has to reconstruct an object of nano-meter size. There given a reciprocal lattice image. We have to train a neural net to reconstruct the real and the complex part of a sample object, see details.

• Long-live health monitoring with wearable devices

For daily accelerometer and gyroscope time series we have to reconstruct typical hand movements and represent these movements as clusters in the phase space.

• Time series segmentation in low-dimensional space

One has to mark-up zero-phase segments of various hand movements using electronic watches. Neural nets on spherical harmonics seems to be a good tool.

• Invariants and compositions of physical activities

We have to create a model to describe simple hand movements and compositions of movements. The composition of physical models must fit the composition of machine learning models.

• Generating WANN: weighted agnostic neural networks

We have to generate agnostic nets and ensembles of agnostic nets to solve one of the well-known reinforcement learning problems: controlling toy cars on the road, steps, ans pendulums.

• GAN creates WANN: generative adversarial network generates agnostic networks

We have to develop a variational autoencoder, VAE, to generate simple agnostic networks, which are evaluated by some discriminative model.