Sökning: "evolutionary machine learning"

Visar resultat 1 - 5 av 19 avhandlingar innehållade orden evolutionary machine 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. Advancing Evolutionary Biology: Genomics, Bayesian Statistics, and Machine Learning

   Författare :Tobias Andermann; Göteborgs universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; computational biology; bioinformatics; phylogenetics; neural networks; NGS; target capture; Illumina sequencing; fossils; IUCN conservation status; extinction rates;

   Sammanfattning : During the recent decades the field of evolutionary biology has entered the era of big data, which has transformed the field into an increasingly computational discipline. In this thesis I present novel computational method developments, including their application in empirical case studies. LÄS MER

5. 4. Combining Evolution and Physics through Machine Learning to Decipher Molecular Mechanisms

   Författare :Darko Mitrovic; Lucie Delemotte; Gerhard Hummer; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Molecular Dynamics Simulation; Evolution; Enhanced Sampling; Machine Learning; Molecular Mechanism; Molekyldynamiksimuleringar; Evolution; Accelererad utforskning; Maskininlärning; Molekylära mekanismer; Biologisk fysik; Biological Physics;

   Sammanfattning : From E.coli to elephants, the cells of all living organisms are surrounded by a near impenetrable wall of lipids. The windows through the walls are membrane proteins - receptors, transporters and channels that confer communication, information and metabolites through the membrane. LÄS MER

7. 5. 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