• MeSH
• kybernetika MeSH
• modely neurologické MeSH
• neurom fyziologie   MeSH
• neuronové sítě MeSH
• Publikační typ
• abstrakty MeSH
• kongresy MeSH

• Konspekt
• Knihovnictví. Informatika
• NLK Obory
• knihovnictví, informační věda a muzeologie
• neurovědy

• MeSH
• neuronové sítě MeSH
• Publikační typ
• kongresy MeSH

• Konspekt
• Patologie. Klinická medicína
• NLK Obory
• neurovědy

• MeSH
• Haplorrhini MeSH
• informační teorie využití   MeSH
• kočky MeSH
• nervový přenos genetika   MeSH
• neurony fyziologie   MeSH
• zraková percepce genetika   MeSH
• zvířata MeSH
• Check Tag
• kočky MeSH
• zvířata MeSH

Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.

• MeSH
• algoritmy MeSH
• genomika metody   MeSH
• lidé MeSH
• malá jadérková RNA genetika   MeSH
• mikro RNA genetika   MeSH
• myši MeSH
• nekódující RNA genetika   MeSH
• neuronové sítě MeSH
• software MeSH
• výpočetní biologie metody   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

• MeSH
• nervové vedení MeSH
• počítačová simulace MeSH
• synapse fyziologie   MeSH

• MeSH
• ganglia fyziologie   MeSH
• periferní nervy fyziologie   MeSH
• počítačová simulace MeSH

• MeSH
• bionika MeSH
• nervový přenos genetika   MeSH
• nervový systém - fyziologické jevy MeSH
• nervový systém MeSH

• MeSH
• pohyby očí MeSH
• psychomotorické poruchy MeSH
• sakadické oční pohyby MeSH

• Konspekt
• Psychologie
• NLK Obory
• oftalmologie
• psychologie, klinická psychologie
• NLK Publikační typ
• kolektivní monografie

Several studies have demonstrated the important role of non-coding RNAs as regulators of posttranscriptional processes, including stem cells self-renewal and neural differentiation. Human embryonic stem cells (hESCs) and induced pluripotent stem cells (ihPSCs) show enormous potential in regenerative medicine due to their capacity to differentiate to virtually any type of cells of human body. Deciphering the role of non-coding RNAs in pluripotency, self-renewal and neural differentiation will reveal new molecular mechanisms involved in induction and maintenances of pluripotent state as well as triggering these cells toward clinically relevant cells for transplantation. In this brief review we will summarize recently published studies which reveal the role of non-coding RNAs in pluripotency and neural differentiation of hESCs and ihPSC.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

• MeSH
• časové faktory MeSH
• nervový přenos genetika   MeSH
• neurony fyziologie   MeSH