Search results for "ENCODE"
showing 10 items of 91 documents
YJL159w does encode Pir2/Hsp150
2001
In this paper we compare the sequence of the gene HSP150/PIR2, independently determined by two different groups, with that present in the yeast database as YJL159w, determined within the Yeast Sequencing Project. Although YJL159w is believed to encode Hsp150/Pir2, there are important differences between the amino acid sequence coded by this ORF and that of HSP150/PIR2. To find out if this divergence is due to strain polymorphism or to a possible sequencing error, we have analysed the diverging zone of this ORF in three strains and have found it entirely consistent with the sequence reported as HSP150/PIR2, concluding that the divergence is probably due to a sequencing error in YJL159w. Copy…
On the Non-uniform Redundancy of Representations for Grammatical Evolution: The Influence of Grammars
2018
The representation used in grammatical evolution (GE) is non-uniformly redundant as some phenotypes are represented by more genotypes than others. This article studies how the non-uniform redundancy of the GE representation depends on various types of grammars. When constructing the phenotype tree from a genotype, the used grammar determines Bavg, the average branching factor. Bavg measures the expected number of non-terminals chosen when mapping one genotype codon to a phenotype tree node. First, the paper illustrates that the GE representation induces a bias towards small trees. This bias gets stronger with lower Bavg. For example, when using a grammar with Bavg = 0.5, 75% of all genotype…
A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks
2019
New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in la…
A Tsetlin Machine with Multigranular Clauses
2019
The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. In this paper, we introduce the Multigranular Tsetlin Machine (MTM). The MTM eliminates the specificity hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns. Instead of using a fixed global specificity, we encode varying specificity as part of the clauses, rendering the clauses multigranular. This makes it easier to configure the TM because the dimensionality of the hyperparameter search space is reduced to only two dimensions. Indeed, it tur…
Towards safe reinforcement-learning in industrial grid-warehousing
2020
Abstract Reinforcement learning has shown to be profoundly successful at learning optimal policies for simulated environments using distributed training with extensive compute capacity. Model-free reinforcement learning uses the notion of trial and error, where the error is a vital part of learning the agent to behave optimally. In mission-critical, real-world environments, there is little tolerance for failure and can cause damaging effects on humans and equipment. In these environments, current state-of-the-art reinforcement learning approaches are not sufficient to learn optimal control policies safely. On the other hand, model-based reinforcement learning tries to encode environment tra…
Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation
2022
U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datase…
NBEA : developmental disease gene with early generalized epilepsy phenotypes
2018
Abstract: NBEA is a candidate gene for autism, and de novo variants have been reported in neurodevelopmental disease (NDD) cohorts. However, NBEA has not been rigorously evaluated as a disease gene, and associated phenotypes have not been delineated. We identified 24 de novo NBEA variants in patients with NDD, establishing NBEA as an NDD gene. Most patients had epilepsy with onset in the first few years of life, often characterized by generalized seizure types, including myoclonic and atonic seizures. Our data show a broader phenotypic spectrum than previously described, including a myoclonic-astatic epilepsy-like phenotype in a subset of patients. Ann Neurol 2018;84:796-803
Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI
2020
In this paper, we present an evaluation of four encoder&ndash
Dynamics of Quadriceps Muscles during Isometric Contractions : Velocity-Encoded Phase Contrast MRI Study
2021
Objective: To quantify the spatial heterogeneity of displacement during voluntary isometric contraction within and between the different compartments of the quadriceps. Methods: The thigh muscles of seven subjects were imaged on an MRI scanner while performing isometric knee extensions at 40% maximal voluntary contraction. A gated velocity-encoded phase contrast MRI sequence in axial orientations yielded tissue velocity-encoded dynamic images of the four different compartments of the thigh muscles (vastus lateralis (VL), vastus medialis (VM), vastus intermedius (VI), and rectus femoris (RF)) at three longitudinal locations of the proximal–distal length: 17.5% (proximal), 50% (middle), and 7…
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
2018
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and plannin…