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2020 – today
- 2024
[c28]Hande Kastan, Kerem Kasikci, Remziye Günes, Melih Güven, Murat Koras, Baris Akgün, Mehmet Gönen:
Location-Based Performance Management of Sales Teams. SIU 2024: 1-4- 2023
- [j27]Oguz C. Binatli, Mehmet Gönen:
MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding. BMC Bioinform. 24(1): 276 (2023) - [c27]Erdem Ünal, Ugur Aydin, Murat Koras, Baris Akgün, Mehmet Gönen
:
Geolocation Risk Scores for Credit Scoring Models. LOD (2) 2023: 34-44 - [c26]Attila Karaahmetoglu, Ugur Yigitoglu, Elif Vardarli, Erdem Ünal, Ugur Aydin, Murat Koras, Mehmet Gönen, Baris Akgün:
A Hybrid Text Classification Approach for Chatbots. SIU 2023: 1-4 - [c25]Ibrahim Tozlu, Elif Vardarli, Mert Pekey, Mehmet Gönen, Baris Akgün:
A Reinforcement Learning based Collection Approach. SIU 2023: 1-4 - 2022
- [j26]Ayyüce Begüm Bektas, Çigdem Ak, Mehmet Gönen:
Fast and interpretable genomic data analysis using multiple approximate kernel learning. Bioinform. 38(Supplement_1): i77-i83 (2022) - [j25]Çigdem Ak, Alex D. Chitsazan, Mehmet Gönen, Ruth Etzioni, Aaron J. Grossberg:
Spatial Prediction of COVID-19 Pandemic Dynamics in the United States. ISPRS Int. J. Geo Inf. 11(9): 470 (2022) - [j24]Arezou Rahimi, Mehmet Gönen:
Efficient Multitask Multiple Kernel Learning With Application to Cancer Research. IEEE Trans. Cybern. 52(9): 8716-8728 (2022) - [c24]Marzieh Soleimanpoor, Milad Mokhtaridoost, Mehmet Gönen:
A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages. LOD (1) 2022: 62-77 - [c23]Emre Atan, Ali Duymaz, Funda Sarisözen, Ugur Aydin, Murat Koras, Baris Akgün, Mehmet Gönen:
Corporate Network Analysis Based on Graph Learning. LOD (1) 2022: 268-278 - [c22]Veli Oguzalp Bakir, Ilhan Cagatay, Melih Güven, Murat Koras, Mehmet Gönen, Baris Akgün:
Banking Order Classification and Information Extraction. SIU 2022: 1-4 - 2021
- [j23]Ayyüce Begüm Bektas, Mehmet Gönen:
PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning. BMC Bioinform. 22(1): 537 (2021) - [c21]Hazal Hasret Yurdakul, Kerem Kasikci, Ilhan Cagatay, Melih Güven, Murat Koras, Baris Akgün, Mehmet Gönen:
ATM Allocation Using Decision Tree-Based Algorithms. SIU 2021: 1-4 - 2020
- [j22]Arezou Rahimi, Mehmet Gönen:
A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers. Bioinform. 36(12): 3766-3772 (2020) - [j21]Milad Mokhtaridoost, Mehmet Gönen:
An efficient framework to identify key miRNA-mRNA regulatory modules in cancer. Bioinform. 36(Supplement-2): i592-i600 (2020) - [c20]Baris Bayram, Bilge Köroglu, Mehmet Gönen:
Improving Fraud Detection and Concept Drift Adaptation in Credit Card Transactions Using Incremental Gradient Boosting Trees. ICMLA 2020: 545-550 - [c19]Milad Mokhtaridoost, Mehmet Gönen:
Identifying Key miRNA-mRNA Regulatory Modules in Cancer Using Sparse Multivariate Factor Regression. LOD (1) 2020: 422-433
2010 – 2019
- 2019
- [j20]Onur Dereli, Ceyda Oguz, Mehmet Gönen:
Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning. Bioinform. 35(24): 5137-5145 (2019) - [c18]Onur Dereli, Ceyda Oguz, Mehmet Gönen:
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology. ICML 2019: 1576-1585 - 2018
- [j19]Arezou Rahimi, Mehmet Gönen:
Discriminating early- and late-stage cancers using multiple kernel learning on gene sets. Bioinform. 34(13): i412-i421 (2018) - [c17]Çigdem Ak, Önder Ergönül, Mehmet Gönen:
Structured Gaussian Processes with Twin Multiple Kernel Learning. ACML 2018: 65-80 - 2017
- [j18]Olga Nikolova, Russell Moser, Christopher Kemp, Mehmet Gönen, Adam A. Margolin:
Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies. Bioinform. 33(9): 1362-1369 (2017) - 2016
- [j17]Mehmet Gönen:
Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation. BMC Bioinform. 17(S-16): 123-135 (2016) - [c16]Mehmet Gönen:
AUC Maximization in Bayesian Hierarchical Models. ECAI 2016: 21-27 - [i2]Nathan H. Lazar, Mehmet Gönen, Kemal Sönmez:
BaTFLED: Bayesian Tensor Factorization Linked to External Data. CoRR abs/1612.02965 (2016) - 2015
- [j16]He Zhang, Mehmet Gönen, Zhirong Yang, Erkki Oja:
Understanding emotional impact of images using Bayesian multiple kernel learning. Neurocomputing 165: 3-13 (2015) - [i1]Thomas Cokelaer, Mukesh Bansal, Christopher Bare, Erhan Bilal, Brian M. Bot, Elias Chaibub Neto, Federica Eduati, Mehmet Gönen, Steven M. Hill, Bruce R. Hoff, Jonathan R. Karr, Robert Küffner, Michael P. Menden, Pablo Meyer, Raquel Norel, Abhishek Pratap, Robert J. Prill, Matthew T. Weirauch, James C. Costello, Gustavo Stolovitzky, Julio Saez-Rodriguez:
DREAMTools: a Python package for scoring collaborative challenges. F1000Research 4: 1030 (2015) - 2014
- [j15]Mehmet Gönen, Adam A. Margolin:
Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning. Bioinform. 30(17): 556-563 (2014) - [j14]Melih Kandemir, Akos Vetek, Mehmet Gönen, Arto Klami, Samuel Kaski:
Multi-task and multi-view learning of user state. Neurocomputing 139: 97-106 (2014) - [j13]Muhammad Ammad-ud-din, Elisabeth Georgii, Mehmet Gönen, Tuomo Laitinen, Olli-P. Kallioniemi, Krister Wennerberg, Antti Poso, Samuel Kaski:
Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization. J. Chem. Inf. Model. 54(8): 2347-2359 (2014) - [j12]Mehmet Gönen, Samuel Kaski:
Kernelized Bayesian Matrix Factorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(10): 2047-2060 (2014) - [j11]Mehmet Gönen:
Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning. Pattern Recognit. Lett. 38: 132-141 (2014) - [c15]Mehmet Gönen, Adam A. Margolin:
Kernelized Bayesian Transfer Learning. AAAI 2014: 1831-1839 - [c14]Mehmet Gönen:
Embedding Heterogeneous Data by Preserving Multiple Kernels. ECAI 2014: 381-386 - [c13]Mehmet Gönen, Gülefsan Bozkurt Gönen, Fikret S. Gürgen:
Bayesian Multiview Dimensionality Reduction for Learning Predictive Subspaces. ECAI 2014: 387-392 - [c12]Mehmet Gönen, Adam A. Margolin:
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology. NIPS 2014: 1305-1313 - 2013
- [j10]Mehmet Gönen, Ethem Alpaydin:
Localized algorithms for multiple kernel learning. Pattern Recognit. 46(3): 795-807 (2013) - [j9]Mehmet Gönen:
Bayesian Supervised Dimensionality Reduction. IEEE Trans. Cybern. 43(6): 2179-2189 (2013) - [j8]Mehmet Gönen:
Supervised Multiple Kernel Embedding for Learning Predictive Subspaces. IEEE Trans. Knowl. Data Eng. 25(10): 2381-2389 (2013) - [c11]Mehmet Gönen, Suleiman A. Khan, Samuel Kaski:
Kernelized Bayesian Matrix Factorization. ICML (3) 2013: 864-872 - [c10]He Zhang, Zhirong Yang, Mehmet Gönen, Markus Koskela, Jorma Laaksonen, Timo Honkela, Erkki Oja:
Affective Abstract Image Classification and Retrieval Using Multiple Kernel Learning. ICONIP (3) 2013: 166-175 - [c9]He Zhang, Mehmet Gönen, Zhirong Yang, Erkki Oja:
Predicting Emotional States of Images Using Bayesian Multiple Kernel Learning. ICONIP (3) 2013: 274-282 - 2012
- [j7]Mehmet Gönen:
Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinform. 28(18): 2304-2310 (2012) - [j6]Gülefsan Bozkurt Gönen, Mehmet Gönen, Fikret S. Gürgen:
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis. Expert Syst. Appl. 39(14): 11709-11717 (2012) - [c8]Mehmet Gönen:
A Bayesian Multiple Kernel Learning Framework for Single and Multiple Output Regression. ECAI 2012: 354-359 - [c7]Mehmet Gönen:
Bayesian Efficient Multiple Kernel Learning. ICML 2012 - [c6]Aydin Ulas, Mehmet Gönen, Umberto Castellani, Vittorio Murino, Marcella Bellani, Michele Tansella, Paolo Brambilla:
A Localized MKL Method for Brain Classification with Known Intra-class Variability. MLMI 2012: 152-159 - [c5]Mehmet Gönen:
Bayesian Supervised Multilabel Learning with Coupled Embedding and Classification. SDM 2012: 367-378 - 2011
- [j5]Mehmet Gönen, Ethem Alpaydin:
Multiple Kernel Learning Algorithms. J. Mach. Learn. Res. 12: 2211-2268 (2011) - [j4]Mehmet Gönen, Ethem Alpaydin:
Regularizing multiple kernel learning using response surface methodology. Pattern Recognit. 44(1): 159-171 (2011) - [c4]Mehmet Gönen, Melih Kandemir, Samuel Kaski:
Multitask Learning Using Regularized Multiple Kernel Learning. ICONIP (2) 2011: 500-509 - [c3]Mehmet Gönen, Aydin Ulas, Peter J. Schüffler, Umberto Castellani, Vittorio Murino:
Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma. SIMBAD 2011: 250-260 - 2010
- [b1]Mehmet Gönen:
Localized multiple kernel algorithms for machine learning (Yapay öğrenme için yerel çoklu çekirdek algoritmaları). Boğaziçi University, Turkey, 2010 - [j3]Mehmet Gönen, Ethem Alpaydin:
Supervised learning of local projection kernels. Neurocomputing 73(10-12): 1694-1703 (2010) - [j2]Mehmet Gönen, Ethem Alpaydin:
Cost-conscious multiple kernel learning. Pattern Recognit. Lett. 31(9): 959-965 (2010) - [c2]Mehmet Gönen, Ethem Alpaydin:
Localized Multiple Kernel Regression. ICPR 2010: 1425-1428
2000 – 2009
- 2008
- [j1]Mehmet Gönen, Ayse Gönül Tanugur, Ethem Alpaydin:
Multiclass Posterior Probability Support Vector Machines. IEEE Trans. Neural Networks 19(1): 130-139 (2008) - [c1]Mehmet Gönen, Ethem Alpaydin:
Localized multiple kernel learning. ICML 2008: 352-359
Coauthor Index
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