Advanced AI for Biological Data A1F
University of Skövde
Skövde
Startdatum:
Slutdatum:
Studietakt: 50 %
Publicerad utbildningskatalog
Utbildningsinformation från den publicerade källan. Utbildningen och dess tidsbundna tillfällen hålls åtskilda.
Kod: BI765A
<p>Machine learning plays an increasingly important role in biological research. Advanced algorithms are used to interpret and predict complex biological processes. These methods help us gain a better understanding of genetic mechanisms and protein structures. By applying machine learning techniques to unimodal data, researchers can analyze specific biological systems in detail. The developed methods enable thorough evaluation of the results, leading to more accurate and robust analyses. However, when data from different sources is combined, more sophisticated techniques are required to integrate the information. Researchers critically reflect on how well machine learning methods work for integrating multimodal biological data. One challenge is to ensure that the integrated models are both reliable and transparent. Therefore, explainable AI techniques are used to clearly demonstrate how decisions within the models are made. By combining advanced machine learning with explainable AI, pathways are paved for more insightful and secure biological discoveries.</p>
The course has the following entry requirements: attended BI763A Introduction to AI in Bioinformatics A1N and attended BI764A Bioinformatics Analysis Pipelines for Large-Scale Sequence Data A1N (or the equivalent).A further requirement is proof of skills in English equivalent of studies at upper secondary level in Sweden, known as the Swedish course English 6 or English level 2. This is normally demonstrated by means of an internationally recognized language test, e.g. IELTS or TOEFL or the equivalent.
Varje tillfälle har egna datum och villkor. Avslutade tillfällen behålls som historik och innebär inte att en ny ansökan är öppen.
University of Skövde
Skövde
Startdatum:
Slutdatum:
Studietakt: 50 %
Hämtad: .
Publicerad: .
Publiceringsversion: 8e217193-f5fa-4778-b085-a4521fd03e8d
Kontrollsumma: 1b0dc54c0fc8a359f83ba9dc8f9d468479ce432de4c03bce3b8b33dd67fe3f6c
Senast ändrad enligt källan: 2026-08-20T13:26:52