Sökning: "evolutionary deep learning"

Visar resultat 1 - 5 av 11 avhandlingar innehållade orden evolutionary deep learning.

1. 1. Evolving intelligence : Overcoming challenges for Evolutionary Deep Learning

   Författare :Mohammed Ghaith Altarabichi; Sławomir Nowaczyk; Sepideh Pashami; Peyman Sheikholharam Mashhadi; Niklas Lavesson; Högskolan i Halmstad; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; neural networks; evolutionary deep learning; evolutionary machine learning; feature selection; hyperparameter optimization; evolutionary computation; particle swarm optimization; genetic algorithm;

   Sammanfattning : Deep Learning (DL) has achieved remarkable results in both academic and industrial fields over the last few years. However, DL models are often hard to design and require proper selection of features and tuning of hyper-parameters to achieve high performance. These selections are tedious for human experts and require substantial time and resources. LÄS MER

3. 2. Advancing systems biology of yeast through machine learning and comparative genomics

   Författare :Le Yuan; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; enzyme turnover number; gene essentiality; horizontal gene transfer; machine learning; deep learning; yeast species;

   Sammanfattning : Synthetic biology has played a pivotal role in accomplishing the production of high value commodities, pharmaceuticals, and bulk chemicals. Fueled by the breakthrough of synthetic biology and metabolic engineering, Saccharomyces cerevisiae and various other yeasts (such as Yarrowia lipolytica , Pichia pastoris ) have been proven to be promising microbial cell factories and are frequently used in scientific studies. LÄS MER

4. 3. Visualizing the abyss of time : Students’ interpretation of visualized deep evolutionary time

   Författare :Jörgen Stenlund; Konrad Schönborn; Lena Tibell; Gunnar Höst; Camillia Matuk; Linköpings universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Deep evolutionary time; Visualization; Evolution; Science Education; Djup evolutionär tid; Visualiseringar; Naturvetenskapernas didaktik;

   Sammanfattning : The immense time scales involved in Deep evolutionary time (DET) is a threshold concept in biology and interpreting temporal aspects of DET is demanding. DET is communicated through various visualizations that include static two-dimensional representations, low interactivity animations, as well as high interactivity interfaces. LÄS MER

5. 4. Mapping the proteome with data-driven methods: A cycle of measurement, modeling, hypothesis generation, and engineering

   Författare :Filip Buric; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; tensor factorization; mass spectrometry; model interpretation; sequence feature engineering; deep learning; proteomics; machine learning; data-independent acquisition; feature learning;

   Sammanfattning : The living cell exhibits emergence of complex behavior and its modeling requires a systemic, integrative approach if we are to thoroughly understand and harness it. The work in this thesis has had the more narrow aim of quantitatively characterizing and mapping the proteome using data-driven methods, as proteins perform most functional and structural roles within the cell. LÄS MER

7. 5. Methodology and Infrastructure for Statistical Computing in Genomics : Applications for Ancient DNA

   Författare :Kristiina Ausmees; Carl Nettelblad; Mattias Jakobsson; Flora Jay; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; statistical computing; genotype imputation; ancient DNA; deep learning; dimensionality reduction; genetic clustering; distributed computing; Scientific Computing; Beräkningsvetenskap;

   Sammanfattning : This thesis concerns the development and evaluation of computational methods for analysis of genetic data. A particular focus is on ancient DNA recovered from archaeological finds, the analysis of which has contributed to novel insights into human evolutionary and demographic history, while also introducing new challenges and the demand for specialized methods. LÄS MER