Computational Biology for Infection Research

The Department of “Computational Biology for Infection Research” studies the human microbiome, viral and bacterial pathogens, and human cell lineages within individual patients by analysis of large-scale biological and epidemiological data sets with computational techniques. Focusing on high throughput meta’omics, population genomic and single cell sequencing data, we produce testable hypotheses, such as sets of key sites or relevant genes associated with the presence of a disease, of antibiotic resistance or pathogenic evasion of immune defense. We interact with experimental collaborators to verify our findings and to promote their translation into medical treatment or diagnosis procedures. To achieve its research goals, the department also develops novel algorithms and software.

Leader

Seminar: Deep Learning for Molecular Biology

The seminar "Deep Learning for molecular biology" is hosted by the department "Computational Biology of Infection Research" at the HZI headed by Prof. Alice McHardy.

 

Kick-off Meeting: 04 April 2024, 10 a.m.
Room Kick-off Meeting: BRICS, room 207
Seminar Date: TBA
Room seminar: TBA
Max. number of participants: 10
Language: English
Modus: 30 minutes of presentation (including discussion) + 3-5 pages written summary
Designated for Bachelor and Master Students of Computer and Data Science
Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications)

In case you have questions about the seminar,  contact Mohammad Hadi Foroughmand Araabi.

Description: Recently, deep neural network models have revolutionized machine learning research and achieved state-of-the-art performance in almost every related research, including computer vision, natural language processing, and computational biology. The goal of this seminar is to teach the basic principles of deep learning along with some basic implementations in pytorch framework. We will explore the most fundamental neural architectures including convolutional neural networks, recurrent neural networks, and autoencoders as well as language-model based representation learning methods.

Topics:

  • Deep Neural Networks and back propagation
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Representation Learning
PrintSend per emailShare