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  1. Article ; Online: A Computational Software for Training Robust Drug-Target Affinity Prediction Models: pydebiaseddta.

    Barsbey, Melİh / ÖZçelİk, Riza / Bağ, Alperen / Atil, Berk / ÖZgür, Arzucan / Ozkirimli, Elif

    Journal of computational biology : a journal of computational molecular cell biology

    2023  Volume 30, Issue 11, Page(s) 1240–1245

    MeSH term(s) Software ; Programming Languages ; Proteins ; Drug Discovery
    Chemical Substances Proteins
    Language English
    Publishing date 2023-11-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2030900-4
    ISSN 1557-8666 ; 1066-5277
    ISSN (online) 1557-8666
    ISSN 1066-5277
    DOI 10.1089/cmb.2023.0194
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models.

    ÖZçelİk, Riza / Bağ, Alperen / Atil, Berk / Barsbey, Melİh / ÖZgür, Arzucan / Ozkirimli, Elif

    Journal of computational biology : a journal of computational molecular cell biology

    2023  Volume 30, Issue 11, Page(s) 1226–1239

    MeSH term(s) Ligands ; Proteins/chemistry ; Models, Statistical ; Drug Discovery
    Chemical Substances Ligands ; Proteins
    Language English
    Publishing date 2023-11-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2030900-4
    ISSN 1557-8666 ; 1066-5277
    ISSN (online) 1557-8666
    ISSN 1066-5277
    DOI 10.1089/cmb.2023.0208
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares

    Raj, Anant / Barsbey, Melih / Gürbüzbalaban, Mert / Zhu, Lingjiong / Şimşekli, Umut

    2022  

    Abstract: Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error. While these studies have shed light on interesting aspects of the generalization behavior in ... ...

    Abstract Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error. While these studies have shed light on interesting aspects of the generalization behavior in modern settings, they relied on strong topological and statistical regularity assumptions, which are hard to verify in practice. Furthermore, it has been empirically illustrated that the relation between heavy tails and generalization might not always be monotonic in practice, contrary to the conclusions of existing theory. In this study, we establish novel links between the tail behavior and generalization properties of stochastic gradient descent (SGD), through the lens of algorithmic stability. We consider a quadratic optimization problem and use a heavy-tailed stochastic differential equation (and its Euler discretization) as a proxy for modeling the heavy-tailed behavior emerging in SGD. We then prove uniform stability bounds, which reveal the following outcomes: (i) Without making any exotic assumptions, we show that SGD will not be stable if the stability is measured with the squared-loss $x\mapsto x^2$, whereas it in turn becomes stable if the stability is instead measured with a surrogate loss $x\mapsto |x|^p$ with some $p<2$. (ii) Depending on the variance of the data, there exists a \emph{`threshold of heavy-tailedness'} such that the generalization error decreases as the tails become heavier, as long as the tails are lighter than this threshold. This suggests that the relation between heavy tails and generalization is not globally monotonic. (iii) We prove matching lower-bounds on uniform stability, implying that our bounds are tight in terms of the heaviness of the tails. We support our theory with synthetic and real neural network experiments.

    Comment: 50 pages
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 510
    Publishing date 2022-06-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: DebiasedDTA

    Özçelik, Rıza / Bağ, Alperen / Atıl, Berk / Barsbey, Melih / Özgür, Arzucan / Özkırımlı, Elif

    A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models

    2021  

    Abstract: Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available protein-chemical interaction datasets, which may contain dataset biases that ... ...

    Abstract Computational models that accurately predict the binding affinity of an input protein-chemical pair can accelerate drug discovery studies. These models are trained on available protein-chemical interaction datasets, which may contain dataset biases that may lead the model to learn dataset-specific patterns, instead of generalizable relationships. As a result, the prediction performance of models drops for previously unseen biomolecules, $\textit{i.e.}$ the prediction models cannot generalize to biomolecules outside of the dataset. The latest approaches that aim to improve model generalizability either have limited applicability or introduce the risk of degrading prediction performance. Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models. DebiasedDTA reweights the training samples to mitigate the effect of dataset biases and is applicable to most DTA prediction models. The results suggest that models trained in the DebiasedDTA framework can achieve improved generalizability in predicting the interactions of the previously unseen biomolecules, as well as performance improvements on those previously seen. Extensive experiments with different biomolecule representations, model architectures, and datasets demonstrate that DebiasedDTA can upgrade DTA prediction models irrespective of the biomolecule representation, model architecture, and training dataset. Last but not least, we release DebiasedDTA as an open-source python library to enable other researchers to debias their own predictors and/or develop their own debiasing methods. We believe that this python library will corroborate and foster research to develop more generalizable DTA prediction models.
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-07-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Bayesian Allocation Model

    Cemgil, Ali Taylan / Kurutmaz, Mehmet Burak / Yildirim, Sinan / Barsbey, Melih / Simsekli, Umut

    Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns

    2019  

    Abstract: We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the ...

    Abstract We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation. BAM is based on a Poisson process, whose events are marked by using a Bayesian network, where the conditional probability tables of this network are then integrated out analytically. We show that the resulting marginal process turns out to be a Polya urn, an integer valued self-reinforcing process. This urn processes, which we name a Polya-Bayes process, obey certain conditional independence properties that provide further insight about the nature of NTF. These insights also let us develop space efficient simulation algorithms that respect the potential sparsity of data: we propose a class of sequential importance sampling algorithms for computing NTF and approximating their marginal likelihood, which would be useful for model selection. The resulting methods can also be viewed as a model scoring method for topic models and discrete Bayesian networks with hidden variables. The new algorithms have favourable properties in the sparse data regime when contrasted with variational algorithms that become more accurate when the total sum of the elements of the observed tensor goes to infinity. We illustrate the performance on several examples and numerically study the behaviour of the algorithms for various data regimes.

    Comment: 70 pages, 16 figures
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning ; Statistics - Computation ; Statistics - Methodology
    Subject code 006
    Publishing date 2019-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians.

    Dvijotham, Krishnamurthy Dj / Winkens, Jim / Barsbey, Melih / Ghaisas, Sumedh / Stanforth, Robert / Pawlowski, Nick / Strachan, Patricia / Ahmed, Zahra / Azizi, Shekoofeh / Bachrach, Yoram / Culp, Laura / Daswani, Mayank / Freyberg, Jan / Kelly, Christopher / Kiraly, Atilla / Kohlberger, Timo / McKinney, Scott / Mustafa, Basil / Natarajan, Vivek /
    Geras, Krzysztof / Witowski, Jan / Qin, Zhi Zhen / Creswell, Jacob / Shetty, Shravya / Sieniek, Marcin / Spitz, Terry / Corrado, Greg / Kohli, Pushmeet / Cemgil, Taylan / Karthikesalingam, Alan

    Nature medicine

    2023  Volume 29, Issue 7, Page(s) 1814–1820

    Abstract: Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice versa. ...

    Abstract Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice versa. We developed Complementarity-Driven Deferral to Clinical Workflow (CoDoC), a system that can learn to decide between the opinion of a predictive AI model and a clinical workflow. CoDoC enhances accuracy relative to clinician-only or AI-only baselines in clinical workflows that screen for breast cancer or tuberculosis (TB). For breast cancer screening, compared to double reading with arbitration in a screening program in the UK, CoDoC reduced false positives by 25% at the same false-negative rate, while achieving a 66% reduction in clinician workload. For TB triaging, compared to standalone AI and clinical workflows, CoDoC achieved a 5-15% reduction in false positives at the same false-negative rate for three of five commercially available predictive AI systems. To facilitate the deployment of CoDoC in novel futuristic clinical settings, we present results showing that CoDoC's performance gains are sustained across several axes of variation (imaging modality, clinical setting and predictive AI system) and discuss the limitations of our evaluation and where further validation would be needed. We provide an open-source implementation to encourage further research and application.
    MeSH term(s) Artificial Intelligence ; Reproducibility of Results ; Triage ; Workflow ; Humans
    Language English
    Publishing date 2023-07-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-023-02437-x
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4212: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
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  7. Book ; Online: Evaluating AI systems under uncertain ground truth

    Stutz, David / Cemgil, Ali Taylan / Roy, Abhijit Guha / Matejovicova, Tatiana / Barsbey, Melih / Strachan, Patricia / Schaekermann, Mike / Freyberg, Jan / Rikhye, Rajeev / Freeman, Beverly / Matos, Javier Perez / Telang, Umesh / Webster, Dale R. / Liu, Yuan / Corrado, Greg S. / Matias, Yossi / Kohli, Pushmeet / Liu, Yun / Doucet, Arnaud /
    Karthikesalingam, Alan

    a case study in dermatology

    2023  

    Abstract: For safety, AI systems in health undergo thorough evaluations before deployment, validating their predictions against a ground truth that is assumed certain. However, this is actually not the case and the ground truth may be uncertain. Unfortunately, ... ...

    Abstract For safety, AI systems in health undergo thorough evaluations before deployment, validating their predictions against a ground truth that is assumed certain. However, this is actually not the case and the ground truth may be uncertain. Unfortunately, this is largely ignored in standard evaluation of AI models but can have severe consequences such as overestimating the future performance. To avoid this, we measure the effects of ground truth uncertainty, which we assume decomposes into two main components: annotation uncertainty which stems from the lack of reliable annotations, and inherent uncertainty due to limited observational information. This ground truth uncertainty is ignored when estimating the ground truth by deterministically aggregating annotations, e.g., by majority voting or averaging. In contrast, we propose a framework where aggregation is done using a statistical model. Specifically, we frame aggregation of annotations as posterior inference of so-called plausibilities, representing distributions over classes in a classification setting, subject to a hyper-parameter encoding annotator reliability. Based on this model, we propose a metric for measuring annotation uncertainty and provide uncertainty-adjusted metrics for performance evaluation. We present a case study applying our framework to skin condition classification from images where annotations are provided in the form of differential diagnoses. The deterministic adjudication process called inverse rank normalization (IRN) from previous work ignores ground truth uncertainty in evaluation. Instead, we present two alternative statistical models: a probabilistic version of IRN and a Plackett-Luce-based model. We find that a large portion of the dataset exhibits significant ground truth uncertainty and standard IRN-based evaluation severely over-estimates performance without providing uncertainty estimates.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Methodology ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-07-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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