Publications – Machine Learning

1.

Koyuncu, Batuhan; Melek, Ahmet; Yilmaz, Defne; Tuzer, Mert; Unlu, Mehmet Burcin

Chemotherapy Response Prediction with Diffuser Elapser Network Journal Article

In: Scientific Reports, vol. 12, iss. 1, pp. 1-13, 2022.

@article{,

title = {Chemotherapy Response Prediction with Diffuser Elapser Network},

author = {Batuhan Koyuncu and Ahmet Melek and Defne Yilmaz and Mert Tuzer and Mehmet Burcin Unlu},

url = {https://www.nature.com/articles/s41598-022-05460-z.pdf},

doi = {https://doi.org/10.1038/s41598-022-05460-z},

year  = {2022},

date = {2022-01-31},

urldate = {2022-01-31},

journal = {Scientific Reports},

volume = {12},

issue = {1},

pages = {1-13},

abstract = {In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof of concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The  model  predicts  tumor  microenvironment  maps  seven  days  ahead  with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of $2.292pm1.820$.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof of concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The model predicts tumor microenvironment maps seven days ahead with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of $2.292pm1.820$.

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2.

Rateike, Miriam; Majumdar, Ayan; Mineeva, Olga; Gummadi, Krishna P.; Valera, Isabel

Don’t Throw It Away! The Utility of Unlabeled Data in Fair Decision Making Inproceedings

In: 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 1421–1433, Association for Computing Machinery, Seoul, Republic of Korea, 2022, ISBN: 9781450393522.

Abstract | Links | BibTeX

@inproceedings{10.1145/3531146.3533199,

title = {Don’t Throw It Away! The Utility of Unlabeled Data in Fair Decision Making},

author = {Miriam Rateike and Ayan Majumdar and Olga Mineeva and Krishna P. Gummadi and Isabel Valera},

url = {https://doi.org/10.1145/3531146.3533199},

doi = {10.1145/3531146.3533199},

isbn = {9781450393522},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 ACM Conference on Fairness, Accountability, and Transparency},

pages = {1421–1433},

publisher = {Association for Computing Machinery},

address = {Seoul, Republic of Korea},

series = {FAccT '22},

abstract = {Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take decisions based on some ground-truth target that is assumed or expected to be unbiased, i.e., equally distributed across socially salient groups. In many practical settings, the ground-truth cannot be directly observed, and instead, we have to rely on a biased proxy measure of the ground-truth, i.e., biased labels, in the data. In addition, data is often selectively labeled, i.e., even the biased labels are only observed for a small fraction of the data that received a positive decision. To overcome label and selection biases, recent work proposes to learn stochastic, exploring decision policies via i) online training of new policies at each time-step and ii) enforcing fairness as a constraint on performance. However, the existing approach uses only labeled data, disregarding a large amount of unlabeled data, and thereby suffers from high instability and variance in the learned decision policies at different times. In this paper, we propose a novel method based on a variational autoencoder for practical fair decision-making. Our method learns an unbiased data representation leveraging both labeled and unlabeled data and uses the representations to learn a policy in an online process. Using synthetic data, we empirically validate that our method converges to the optimal (fair) policy according to the ground-truth with low variance. In real-world experiments, we further show that our training approach not only offers a more stable learning process but also yields policies with higher fairness as well as utility than previous approaches.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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3.

Javaloy, Adrián; Meghdadi, Maryam; Valera, Isabel

Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization Inproceedings

In: ICML, 2022.

Machine Learning – Publications