Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Knowledge-Based Training of Learning Architectures Under Input Sensitivity Constraints for Improved Explainability
    (Pergamon-Elsevier Science Ltd, 2026) Sildir, Hasan; Erturk, Emrullah; Edizer, Deniz Tuna; Deliismail, Ozgun; Durna, Yusuf Muhammed; Hamit, Bahtiyar
    The traditional machine learning (ML) training problem is unconstrained and lacks an explicit formulation of the underlying driving phenomena. Such a formulation, based solely on experimental data, does not ensure the delivery of qualitative knowledge among variables due to many theoretical issues in the optimization task. This study further tightens Artificial Neural Networks (ANNs) training by including input sensitivities as additional constraints and applies to regression and classification tasks based on literature data. In theory, such sensitivity represents the change direction of the target variable per change in measurements from indicators. The resulting nonlinear optimization problem is solved th rough a rigorous solver and includes the sensitivity expressions through algorithmic differentiation. Compared to traditional methods, with an acceptable decrease in the prediction capability, the proposed model delivers more intuitive, explainable, and experimentally verifiable predictions under input variable variations, under robustness to overfitting, while serving robust identification tasks. A classification case study includes a patient-oriented clinical decision support system development based on the impact of cancer-indicating variables. A competitive test prediction accuracy is obtained compared to commonly used algorithms despite 10 % decrease in the training. The regression case is built upon the energy load estimation to account for prominent considerations to obtain desired sensitivity patterns and proposed methodology delivers significant accuracy drop compared to some formulations to address knowledge patterns. The approach delivers a compatible pattern with practitioner expertise and is compared to widely used machine learning algorithms, whose performances are evaluated through common statistics in addition to multi-variable response graphs.
  • Article
    Citation - WoS: 19
    Citation - Scopus: 20
    Use of Micrornas in Personalized Medicine
    (Humana Press Inc., 2014) Avci, C.B.; Baran, Y.
    Personalized medicine comprises the genetic information together with the phenotypic and environmental factors to yield healthcare tailored to an individual and removes the limitations of the "one-size-fits-all" therapy approach. This provides the opportunity to translate therapies from bench to clinic, to diagnose and predict disease, and to improve patient-tailored treatments based on the unique signatures of a patient's disease and further to identify novel treatment schedules. Nowadays, tiny noncoding RNAs, called microRNAs, have captured the spotlight in molecular biology with highlights like their involvement in DNA translational control, their impression on mRNA and protein expression levels, and their ability to reprogram molecular signaling pathways in cancer. Realizing their pivotal roles in drug resistance, they emerged as diagnostic targets orchestrating drug response in individualized therapy examples. It is not premature to think that researchers could have the US Food and Drug Administration (FDA)-approved kit-based assays for miRNA analysis in the near future. We think that miRNAs are ready for prime time. © Springer Science+Business Media New York 2014.