Staff

Dr. Marcus Oswald

(post-doctoral researcher)

Topic:Specialist for propensity score matching, modelling and solving Mixed-Integer-Programs which arise in bioinformatic problems of the group, especially on networks and machine learning, statistics.

Research project: Developing individual treatment rules for antibiotics therapy for patients hospitalised with moderate community-acquired pneumonia (CAP)

The role of macrolides, β-lactams and fluoroquinolones in CAP is controversially discussed. We aim to find patient characteristics such that patients can be identified benefitting from a specific antibiotic therapy to implement this later into the clinical routine. We apply machine learning/artificial intelligence concepts to data available on admission of hospitalised patients from the observational, prospective, multinational CAPNETZ study to investigate patient variables of all relevant aspects for CAP treatment and management based on appr. 12,000 patients.

Alicia Hiemisch M.Sc.

(doctoral candidate)

Topic: Identifying host factors as drug targets for SARS-CoV-2 from genome-scale knockout and knockdown screens

Research project: SARS-CoV-2 caused a global pandemic in 2020. Patients with COVID-19 can worsen in a short time-lapse and die of multiple organ failure. Influenza causes millions of deaths every year. Instead of focusing solely on antiviral drugs, a promising approach is to repurpose clinically approved drugs that target human proteins. The objective of this project is to utilize omics data from genome-wide knockdown and knockout screens, transcription and proteomics profiles of such virus infected cells, and other relevant data to identify host dependency factors essential for virus entry, replication, and spread within the cell.

Mohadese Naseri M.Sc.

(doctoral candidate)

Topic: Applying machine learning models to identify host factors for viruses and analyze clinical data of patients with community aquired pneumonia

Research project: For emerging and re-emerging viruses, we aim to identify host dependency factors required for virus entry into the cell, replication and spread, particularly host factor and cellular pathways being essential for the virus, but not for the host cell. For this, we employ machine learning based on knockout screening data, protein interaction, single cell sequencing and genomics data. In addition, we apply similar machine learning concepts to identify rules for antibiotics treatment of patients with moderate community acquired pneumonia (CAP).

Fionn Daire Keogh B.Sc.

(Master Student)

Topic: Identification of transcription factors from RNAseq data as potential targets for therapeutic intervention of Respiratory Syncytial Virus infection

Research project: RSV (Respiratory Syncytial Virus) poses a significant threat to vulnerable populations such as infants, the elderly, and immunocompromised individuals, often resulting in mortality. Currently, supportive care remains the primary therapeutic approach, as no safe and effective RSV vaccine is available. The primary objective of this project is to identify transcription factors that can effectively regulate host-factors associated with RSV infection. By targeting these specific transcription factors, we aim to initiate a cascade of events that will ultimately reprogram the expression of host-factors crucial for RSV replication and propagation. This endeavor will serve as a starting point for further investigations towards the development of novel drugs capable of combating RSV infection in a safe and effective manner.

Adrian Schoondermark

(cand. med.)

Topic: Developing an individualised treatment approach for antibiotics therapy for patients hospitalised with moderate community acquired pneumonia (CAP)

Research project: Identifying subgroup specific treatment rules for antibiotics treatemt of patients with moderate community aquired pneumonia, based on several tested established propensity matching and balacning methods.