Essays about: "genetic advantage"
Showing result 1 - 5 of 28 essays containing the words genetic advantage.
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1. Growth and quality of silver birch with different improvement levels
University essay from SLU/Southern Swedish Forest Research CentreAbstract : Silver birch is the most important broadleaf tree species in the context of wood production in Fennoscandia stating 12.9% of the total volume in Sweden (Nilsson et al., 2021). READ MORE
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2. Predicting Biomarkers/ Candidate Genes involved in iALL, using Rough Sets based Interpretable Machine Learning Model.
University essay from Uppsala universitet/Institutionen för biologisk grundutbildningAbstract : Acute lymphoblastic leukemia is a hematological malignancy that gains a proliferative advantage and originates in the bone marrow. One of the more common genetic alterations in ALL is KMT2A-rearrangement which constitutes 80% of the cases of ALL in infants. READ MORE
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3. Developing a reproducible bioinformatics workflow for canine inherited retinal disease
University essay from Uppsala universitet/Institutionen för biologisk grundutbildningAbstract : Inherited Retinal Degenerations (IRDs) are a heterogenous group of diseases which lead to vision impairment and can be found both in humans and in dogs. About 1 in 1,380 humans is estimated to suffer from an autosomal recessive IRD, which would be 5.5 million people worldwide, and many more are estimated to be unaffected carriers. READ MORE
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4. Evaluation of the impact of mitochondrial variation in the estimation of breeding values for dairy cattle
University essay from SLU/Dept. of Animal Breeding and GeneticsAbstract : Mitochondria are independent cellular components responsible for cellular respiration. Through oxidative phosphorylation they convert Adenosine diphosphate and inorganic phosphate into Adenosine Triphosphate, ATP, the essential molecule sourced by all intracellular metabolic processes. READ MORE
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5. A scalable species-based genetic algorithm for reinforcement learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Existing methods in Reinforcement Learning (RL) that rely on gradient estimates suffer from the slow rate of convergence, poor sample efficiency, and computationally expensive training, especially when dealing with complex real-world problems with a sizable dimensionality of the state and action space. In this work, we attempt to leverage the benefits of evolutionary computation as a competitive, scalable, and gradient-free alternative to training deep neural networks for RL-specific problems. READ MORE
