Sökning: "Drug classification"

Visar resultat 1 - 5 av 45 uppsatser innehållade orden Drug classification.

1. 1. Combining Cell Painting, Gene Expression and Structure-Activity Data for Mechanism of Action Prediction

   Master-uppsats, Uppsala universitet/Nationellt resurscentrum för biologi och bioteknik

   Författare :Erik Everett Palm; [2023]
   Nyckelord :bioinformatics; deep learning; machine learning; joint model; tabular data; image data;

   Sammanfattning : The rapid progress in high-throughput omics methods and high-resolution morphological profiling, coupled with the significant advances in machine learning (ML) and deep learning (DL), has opened new avenues for tackling the notoriously difficult problem of predicting the Mechanism of Action (MoA) for a drug of clinical interest. Understanding a drug's MoA can enrich our knowledge of its biological activity, shed light on potential side effects, and serve as a predictor of clinical success. LÄS MER

3. 2. Intersecting Graph Representation Learning and Cell Profiling : A Novel Approach to Analyzing Complex Biomedical Data

   Master-uppsats, Uppsala universitet/Institutionen för farmaceutisk biovetenskap

   Författare :Nima Chamyani; [2023]
   Nyckelord :Graph representation learning; Cell profiling; Biological systems; Network medicine; Graphs; Machine learning techniques; Graph neural networks GNNs ; Protein-Compound-Pathway interactions; Biomarkers; Drug discovery;

   Sammanfattning : In recent biomedical research, graph representation learning and cell profiling techniques have emerged as transformative tools for analyzing high-dimensional biological data. The integration of these methods, as investigated in this study, has facilitated an enhanced understanding of complex biological systems, consequently improving drug discovery. LÄS MER

4. 3. Learning the shapes of protein pockets

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Alejandro Corrochano; Yossra Gharbi; [2022-10-14]
   Nyckelord :Protein; cavity; ligand-binding; 3D-equivariance; shape; latent space; e3nn; Fpocket; sc-PDB;

   Sammanfattning : The comparison of protein pockets plays an important role in drug discovery. Through the identification of binding sites with similar structures, we can assist in finding hits and characterizing the function of proteins. Traditionally, the geometry of cavities has been described with scalar features, which are not fully representative of the shape. LÄS MER

5. 4. Benchmarking Machine Learning Methods for Peptide Activity Predictions

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Boel Knutson; Lida Meskini Moudi; [2022-10-14]
   Nyckelord :Drug discovery; peptide; classification; molecular representation; Z-scales; pseudo amino acid composition; one-hot representation; random forests; support vector machines;

   Sammanfattning : One of the main challenges in the drug discovery process is to find a suitable compound for further analysis. The compound must affect the target relevant for the specific disease, while at the same time have desired properties to make it a safe and efficient drug candidate. LÄS MER

7. 5. Kartläggning av svenska terapirekommendationer vid depression.

   Kandidat-uppsats, Uppsala universitet/Institutionen för farmaci

   Författare :Helena Nikkhah; [2022]
   Nyckelord :Depression; Sertralin; SSRI; SNRI; Antidepressants; Depression; Sertralin; SSRI; SNRI; Antidepressiva läkemedel;

   Sammanfattning : Bakgrund: Globalt drabbas 300 miljoner av depression och sjukdomen kan idag behandlas med både antidepressiva läkemedel och psykoterapi. Depression diagnosticeras utifrån klassificeringssystemen ICD-10 och DSM-5 och självskattats utifrån MADRS-skala. LÄS MER