Proposals - Revision history

Vs at 21:33, 16 February 2024

2024-02-16T21:33:15Z

Wiki at 13:28, 15 December 2020

2020-12-15T13:28:56Z

Wiki at 17:43, 6 October 2020

2020-10-06T17:43:06Z

Wiki at 16:50, 2 October 2020

2020-10-02T16:50:29Z

Wiki at 15:15, 24 September 2020

2020-09-24T15:15:17Z

Wiki at 15:14, 24 September 2020

2020-09-24T15:14:41Z

Wiki at 16:27, 18 August 2020

2020-08-18T16:27:03Z

Wiki at 16:26, 18 August 2020

2020-08-18T16:26:28Z

Wiki at 14:20, 18 August 2020

2020-08-18T14:20:44Z

Wiki at 11:14, 18 August 2020

2020-08-18T11:14:42Z

@@ Line 1: / Line 1: @@
 List of the projects
-* '''Brain-computer interface with Functional data analysis''' Human behavioral analysis and forecasting requires models, which have to predict target variables of complex structure. We develop PLS and CCA (Projection to latent space and Canonic correlation analysis) methods towards the Multiview with continuous-time data representation. [[Projects/BCI-FDA: Brain-computer interface with Functional data analysis]]
+* '''Brain-computer interface with Functional data analysis''' Human behavioral analysis and forecasting requires models, that have to predict target variables of complex structure. We develop PLS and CCA (Projection to latent space and Canonic correlation analysis) methods towards the Multiview with continuous-time data representation. [[Projects/BCI-FDA: Brain-computer interface with Functional data analysis]]
-* '''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. [[Projects/MASSIV: Alternative splicing-inspired protein development|see details.]]
+* '''MASSIV: Alternative splicing-inspired protein development''' Eukaryotes have evolved 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. [[Projects/MASSIV: Alternative splicing-inspired protein development|see details.]]
-* '''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. [[Projects/READY-GO: Prediction of protein functional from peptide sequences| see details.]]
+* '''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 with them. The model should be interpretable. [[Projects/READY-GO: Prediction of protein functional from peptide sequences| 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, [[Projects/CASF: Comparative assessment of scoring functions|see details.]]
+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, [[Projects/CASF: Comparative assessment of scoring functions|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 [[Projects/FLM: Lens-free microscopy image reconstruction|see details.]]
+One has to reconstruct an image of a small biological object (of hundreds of micrometers, like a eucaryotic cell). Its video comes from a device of a brand-new technology: free lens microscopy. This project combines modeling of interference images and its Fourier transform and GANs, the generative and discriminative deep learning models [[Projects/FLM: Lens-free microscopy image reconstruction|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 [https://en.wikipedia.org/wiki/Reciprocal_lattice reciprocal lattice] image. We have to train a neural net to reconstruct the real and the complex part of a sample object, [[Projects/SRF: Syncrotron radiation facility deep image retrieval|see details.]]
+For a given synchrotron image one has to reconstruct an object of nano-meter size. There given a [https://en.wikipedia.org/wiki/Reciprocal_lattice reciprocal lattice] image. We have to train a neural net to reconstruct the real and the complex part of a sample object, [[Projects/SRF: Synchrotron radiation facility deep image retrieval|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.
+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.
+One has to mark up zero-phase segments of various hand movements using electronic watches. Neural nets on spherical harmonics seem to be a good tool.
 * '''Invariants and compositions of physical activities'''
@@ Line 25: / Line 31: @@
 * '''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.
+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, and 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.

← Older revision		Revision as of 13:28, 15 December 2020
Line 1:		Line 1:
	List of the projects		List of the projects
		+	* '''Brain-computer interface with Functional data analysis''' Human behavioral analysis and forecasting requires models, which have to predict target variables of complex structure. We develop PLS and CCA (Projection to latent space and Canonic correlation analysis) methods towards the Multiview with continuous-time data representation. [[Projects/BCI-FDA: Brain-computer interface with Functional data analysis]]

	* '''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. [[Projects/MASSIV: Alternative splicing-inspired protein development\|see details.]]		* '''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. [[Projects/MASSIV: Alternative splicing-inspired protein development\|see details.]]

@@ Line 3: / Line 3: @@
 * '''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. [[Projects/MASSIV: Alternative splicing-inspired protein development|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. [[Projects/READY-GO: Prediction of protein functional from peptide sequences| see details.]]
+* '''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. [[Projects/READY-GO: Prediction of protein functional from peptide sequences| see details.]]
 * '''CASF: Comparative assessment of scoring functions for docking problem'''

← Older revision		Revision as of 16:50, 2 October 2020
Line 1:		Line 1:
−	List of the ~~proposed~~ projects	+	List of the 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. [[Projects/MASSIV: Alternative splicing-inspired protein development\|see details.]]		* '''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. [[Projects/MASSIV: Alternative splicing-inspired protein development\|see details.]]

@@ Line 1: / Line 1: @@
 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.
+* '''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. [[Projects/MASSIV: Alternative splicing-inspired protein development|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. [[Projects/READY-GO: Prediction of protein functional from peptide sequences| see details.]]

← Older revision		Revision as of 15:14, 24 September 2020
Line 1:		Line 1:
	List of the proposed projects		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.
		+	[[Projects/MASSIV: Alternative splicing-inspired protein development\|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. [[Projects/READY-GO: Prediction of protein functional from peptide sequences\| see details.]]

	* '''CASF: Comparative assessment of scoring functions for docking problem'''		* '''CASF: Comparative assessment of scoring functions for docking problem'''

@@ Line 2: / Line 2: @@
 * '''CASF: Comparison analysis 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.
+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, [[Projects/CASF: Comparison analysis scoring functions|see details.]]
 * '''3D-image reconstruction for the lens-free microscopy'''