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    PublicationOpen Access
    PrognosiT: pathway/gene set-based tumour volume prediction using multiple kernel learning
    (BioMed Central, 2021) Department of Industrial Engineering; N/A; Gönen, Mehmet; Bektaş, Ayyüce Begüm; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; Graduate School of Sciences and Engineering; 237468; N/A
    Background: identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. Results: in this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. Conclusions: PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.
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    Toward interdisciplinary synergies in molecular communications: perspectives from synthetic biology, nanotechnology, communications engineering and philosophy of science
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Egan, Malcolm; Barros, Michael Taynnan; Booth, Michael; Llopis-Lorente, Antoni; Magarini, Maurizio; Martins, Daniel P.; Schäfer, Maximilian; Stano, Pasquale; Department of Electrical and Electronics Engineering; Kuşcu, Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 316349
    Within many chemical and biological systems, both synthetic and natural, communication via chemical messengers is widely viewed as a key feature. Often known as molecular communication, such communication has been a concern in the fields of synthetic biologists, nanotechnologists, communications engineers, and philosophers of science. However, interactions between these fields are currently limited. Nevertheless, the fact that the same basic phenomenon is studied by all of these fields raises the question of whether there are unexploited interdisciplinary synergies. In this paper, we summarize the perspectives of each field on molecular communications, highlight potential synergies, discuss ongoing challenges to exploit these synergies, and present future perspectives for interdisciplinary efforts in this area.