
mgr inż. Michał Sieczczyński
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Budynek A Elektroniki
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Wybrane publikacje
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Platelet RNA Sequencing Data Through the Lens of Machine Learning
Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability...
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Neural Graph Collaborative Filtering: Analysis of Possibilities on Diverse Datasets
This paper continues the work by Wang et al. [17]. Its goal is to verify the robustness of the NGCF (Neural Graph Collaborative Filtering) technique by assessing its ability to generalize across different datasets. To achieve this, we first replicated the experiments conducted by Wang et al. [17] to ensure that their replication package is functional. We received sligthly better results for ndcg@20 and somewhat poorer results for...
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Impact of clinical factors on accuracy of ovarian cancer detection via platelet RNA profiling
Ovarian cancer (OC) presents a diagnostic challenge, often resulting in poor patient outcomes. Platelet RNA sequencing, which reflects host response to disease, shows promise for earlier OC detection. This study examines the impact of sex, age, platelet count, and the training on cancer types other than OC on classification accuracy achieved in the previous platelet-alone training data set. A total of 339 samples from healthy donors...
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