Y.I. Tepeli

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Dive into the research topics where Y.I. Tepeli is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

1 Similar Profiles

3 Preprint
2 Article
1 Dissertation (TU Delft)

Computational Tools for Optimizing Targeted Cancer Treatments and Addressing Bias
Tepeli, Y. I., 2025, 193 p.
Research output: Thesis › Dissertation (TU Delft)

Open Access
File
23 Downloads (Pure)
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning
Tepeli, Y. I. & P. Gonçalves, J., 2024, ArXiv.
Research output: Working paper/Preprint › Preprint

Open Access
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning
Tepeli, Y. I., de Wolf, M. J. & P. Gonçalves, J., 2024, ArXiv.
Research output: Working paper/Preprint › Preprint

Open Access
SNMF: Integrated Learning of Mutational Signatures and Prediction of DNA Repair Deficiencies
Goossens, A. C. H., Tepeli, Y. I., Seale, C. F. & P. Gonçalves, J., 2024.
Research output: Working paper/Preprint › Preprint

Open Access
ELISL: early-late integrated synthetic lethality prediction in cancer
Tepeli, Y. I., Seale, C. F. & P. Gonçalves, J., 2023, In: Bioinformatics. 40, 1, 12 p.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File
80 Downloads (Pure)

Data and code underlying the publication: Stratifying Oncogene-addicted Cohorts by Drug Response
Gonçalves, J. (Creator), Tepeli, Y. I. (Creator), Trastulla, . (Creator) & Iorio, F. (Creator), TU Delft - 4TU.ResearchData, 21 Nov 2024
DOI: 10.4121/C269140B-9D5F-411B-BAFB-72AB8DBA45DA
Dataset/Software: Dataset
Data and code underlying the publication: DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning
Gonçalves, J. (Creator) & Tepeli, Y. I. (Creator), TU Delft - 4TU.ResearchData, 25 Nov 2024
DOI: 10.4121/8648064E-AA7B-4A09-A755-7EB2D90BEF66
Dataset/Software: Dataset
Data and code underlying the publication: ELISL: Early-Late Integrated Synthetic Lethality Prediction in Cancer
Gonçalves, J. (Creator), Tepeli, Y. I. (Creator) & Seale, C. F. (Creator), TU Delft - 4TU.ResearchData, 25 Nov 2024
DOI: 10.4121/BF2BAEBA-7971-4954-ADE6-7097BE6492E9
Dataset/Software: Dataset
Data and code underlying the publication: Metric-DST: Mitigating Selection Bias Through Diversity-guided Semi Supervised Metric Learning
Gonçalves, J. (Creator), Tepeli, Y. I. (Creator) & de Wolf, M. (Creator), TU Delft - 4TU.ResearchData, 25 Nov 2024
DOI: 10.4121/206EF581-11A1-4A01-9595-576E7EC8B7C1
Dataset/Software: Dataset
Data and code underlying the publication: Overcoming Selection Bias in Synthetic Lethality Prediction
Gonçalves, J. (Creator), Seale, C. F. (Creator) & Tepeli, Y. I. (Creator), TU Delft - 4TU.ResearchData, 18 Feb 2025
DOI: 10.4121/07BA724A-330F-449F-8CE3-403A23F05D51
Dataset/Software: Software

Y.I. Tepeli, MSc

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

Computational Tools for Optimizing Targeted Cancer Treatments and Addressing Bias

DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning

Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric Learning

SNMF: Integrated Learning of Mutational Signatures and Prediction of DNA Repair Deficiencies

ELISL: early-late integrated synthetic lethality prediction in cancer

Data and code underlying the publication: Stratifying Oncogene-addicted Cohorts by Drug Response

Data and code underlying the publication: DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Learning

Data and code underlying the publication: ELISL: Early-Late Integrated Synthetic Lethality Prediction in Cancer

Data and code underlying the publication: Metric-DST: Mitigating Selection Bias Through Diversity-guided Semi Supervised Metric Learning

Data and code underlying the publication: Overcoming Selection Bias in Synthetic Lethality Prediction

Y.I. Tepeli, MSc

Fingerprint

Collaborations and top research areas from the last five years

Research output

Datasets