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  1. Article ; Online: ADMET-PrInt: Evaluation of ADMET Properties: Prediction and Interpretation.

    Jamrozik, Ewelina / Śmieja, Marek / Podlewska, Sabina

    Journal of chemical information and modeling

    2024  Volume 64, Issue 5, Page(s) 1425–1432

    Abstract: Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus ... ...

    Abstract Great progress in the development of computational strategies for drug design applications has revolutionized the process of searching for new drugs. Although the focus of
    MeSH term(s) Drug Design ; Solubility
    Language English
    Publishing date 2024-02-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c02038
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1230: Show issues Location:
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  2. Article ; Online: Multi-Label Conditional Generation From Pre-Trained Models.

    Proszewska, Magdalena / Wolczyk, Maciej / Zieba, Maciej / Wielopolski, Patryk / Maziarka, Lukasz / Smieja, Marek

    IEEE transactions on pattern analysis and machine intelligence

    2024  Volume PP

    Abstract: Although modern generative models achieve excellent quality in a variety of tasks, they often lack the essential ability to generate examples with requested properties, such as the age of the person in the photo or the weight of the generated molecule. ... ...

    Abstract Although modern generative models achieve excellent quality in a variety of tasks, they often lack the essential ability to generate examples with requested properties, such as the age of the person in the photo or the weight of the generated molecule. To overcome these limitations we propose PluGeN (Plugin Generative Network), a simple yet effective generative technique that can be used as a plugin for pre-trained generative models. The idea behind our approach is to transform the entangled latent representation using a flow-based module into a multi-dimensional space where the values of each attribute are modeled as an independent one-dimensional distribution. In consequence, PluGeN can generate new samples with desired attributes as well as manipulate labeled attributes of existing examples. Due to the disentangling of the latent representation, we are even able to generate samples with rare or unseen combinations of attributes in the dataset, such as a young person with gray hair, men with make-up, or women with beards. In contrast to competitive approaches, PluGeN can be trained on partially labeled data. We combined PluGeN with GAN and VAE models and applied it to conditional generation and manipulation of images, chemical molecule modeling and 3D point clouds generation.
    Language English
    Publishing date 2024-03-26
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2024.3382008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: HyperTab

    Wydmański, Witold / Bulenok, Oleksii / Śmieja, Marek

    Hypernetwork Approach for Deep Learning on Small Tabular Datasets

    2023  

    Abstract: Deep learning has achieved impressive performance in many domains, such as computer vision and natural language processing, but its advantage over classical shallow methods on tabular datasets remains questionable. It is especially challenging to surpass ...

    Abstract Deep learning has achieved impressive performance in many domains, such as computer vision and natural language processing, but its advantage over classical shallow methods on tabular datasets remains questionable. It is especially challenging to surpass the performance of tree-like ensembles, such as XGBoost or Random Forests, on small-sized datasets (less than 1k samples). To tackle this challenge, we introduce HyperTab, a hypernetwork-based approach to solving small sample problems on tabular datasets. By combining the advantages of Random Forests and neural networks, HyperTab generates an ensemble of neural networks, where each target model is specialized to process a specific lower-dimensional view of the data. Since each view plays the role of data augmentation, we virtually increase the number of training samples while keeping the number of trainable parameters unchanged, which prevents model overfitting. We evaluated HyperTab on more than 40 tabular datasets of a varying number of samples and domains of origin, and compared its performance with shallow and deep learning models representing the current state-of-the-art. We show that HyperTab consistently outranks other methods on small data (with a statistically significant difference) and scores comparable to them on larger datasets. We make a python package with the code available to download at https://pypi.org/project/hypertab/
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-04-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: r-softmax

    Bałazy, Klaudia / Struski, Łukasz / Śmieja, Marek / Tabor, Jacek

    Generalized Softmax with Controllable Sparsity Rate

    2023  

    Abstract: Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution are the inseparable aspect of deep learning solutions. Although softmax ... ...

    Abstract Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution are the inseparable aspect of deep learning solutions. Although softmax is a commonly accepted probability mapping function in the machine learning community, it cannot return sparse outputs and always spreads the positive probability to all positions. In this paper, we propose r-softmax, a modification of the softmax, outputting sparse probability distribution with controllable sparsity rate. In contrast to the existing sparse probability mapping functions, we provide an intuitive mechanism for controlling the output sparsity level. We show on several multi-label datasets that r-softmax outperforms other sparse alternatives to softmax and is highly competitive with the original softmax. We also apply r-softmax to the self-attention module of a pre-trained transformer language model and demonstrate that it leads to improved performance when fine-tuning the model on different natural language processing tasks.
    Keywords Computer Science - Machine Learning
    Subject code 519
    Publishing date 2023-04-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: ChiENN

    Gaiński, Piotr / Koziarski, Michał / Tabor, Jacek / Śmieja, Marek

    Embracing Molecular Chirality with Graph Neural Networks

    2023  

    Abstract: Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish between the 3D graph of a chemical ... ...

    Abstract Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish between the 3D graph of a chemical compound and its mirror image (enantiomer). The ability to distinguish between enantiomers is important especially in drug discovery because enantiomers can have very distinct biochemical properties. In this paper, we propose a theoretically justified message-passing scheme, which makes GNNs sensitive to the order of node neighbors. We apply that general concept in the context of molecular chirality to construct Chiral Edge Neural Network (ChiENN) layer which can be appended to any GNN model to enable chirality-awareness. Our experiments show that adding ChiENN layers to a GNN outperforms current state-of-the-art methods in chiral-sensitive molecular property prediction tasks.
    Keywords Computer Science - Machine Learning ; Quantitative Biology - Quantitative Methods
    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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  6. Article ; Online: Chronic COVID-19 infection in an immunosuppressed patient shows changes in lineage over time: a case report.

    Baker, Sheridan J C / Nfonsam, Landry E / Leto, Daniela / Rutherford, Candy / Smieja, Marek / McArthur, Andrew G

    Virology journal

    2024  Volume 21, Issue 1, Page(s) 8

    Abstract: Background: The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, emerged in late 2019 and spready globally. Many effects of infection with this pathogen are still unknown, with both chronic and repeated COVID-19 ... ...

    Abstract Background: The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, emerged in late 2019 and spready globally. Many effects of infection with this pathogen are still unknown, with both chronic and repeated COVID-19 infection producing novel pathologies.
    Case presentation: An immunocompromised patient presented with chronic COVID-19 infection. The patient had history of Hodgkin's lymphoma, treated with chemotherapy and stem cell transplant. During the course of their treatment, eleven respiratory samples from the patient were analyzed by whole-genome sequencing followed by lineage identification. Whole-genome sequencing of the virus present in the patient over time revealed that the patient at various timepoints harboured three different lineages of the virus. The patient was initially infected with the B.1.1.176 lineage before coinfection with BA.1. When the patient was coinfected with both B.1.1.176 and BA.1, the viral populations were found in approximately equal proportions within the patient based on sequencing read abundance. Upon further sampling, the lineage present within the patient during the final two timepoints was found to be BA.2.9. The patient eventually developed respiratory failure and died.
    Conclusions: This case study shows an example of the changes that can happen within an immunocompromised patient who is infected with COVID-19 multiple times. Furthermore, this case demonstrates how simultaneous coinfection with two lineages of COVID-19 can lead to unclear lineage assignment by standard methods, which are resolved by further investigation. When analyzing chronic COVID-19 infection and reinfection cases, care must be taken to properly identify the lineages of the virus present.
    MeSH term(s) Humans ; COVID-19/complications ; Coinfection ; Pandemics ; SARS-CoV-2 ; Immunocompromised Host
    Language English
    Publishing date 2024-01-04
    Publishing country England
    Document type Case Reports ; Journal Article
    ZDB-ID 2160640-7
    ISSN 1743-422X ; 1743-422X
    ISSN (online) 1743-422X
    ISSN 1743-422X
    DOI 10.1186/s12985-023-02278-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Initial vancomycin versus metronidazole for the treatment of first-episode non-severe

    Zhang, Kevin / Beckett, Patricia / Abouanaser, Salaheddin / Smieja, Marek

    Antimicrobial stewardship & healthcare epidemiology : ASHE

    2021  Volume 1, Issue 1, Page(s) e27

    Abstract: Objective: Clostridioides difficile: Methods: We conducted a retrospective cohort study of all adult inpatients with first-episode CDI at our institution from January 2013 to May 2018. The initial vancomycin versus initial metronidazole cohorts were ... ...

    Abstract Objective: Clostridioides difficile
    Methods: We conducted a retrospective cohort study of all adult inpatients with first-episode CDI at our institution from January 2013 to May 2018. The initial vancomycin versus initial metronidazole cohorts were examined using a multivariate logistic regression model.
    Results: The study cohort of 737 patients had a median age of 72.3 years, and 357 of these patients (48.4%) had hospital-acquired infection. Among 326 patients with non-severe CDI, recurrence, new incident infection, and 30-day mortality rates were 16.2%, 10.9%, and 5.3%, respectively, when treated with initial metronidazole, compared to 20.0%, 1.4%, and 10.0%, respectively, when treated with initial vancomycin. In an adjusted multivariable analysis, the use of initial vancomycin for the treatment of non-severe CDI was associated with a reduction in new incident infection (adjusted odds ratio [OR
    Conclusions: Initial vancomycin was associated with a reduced rate of new incident infection in the treatment of adult inpatients with first-episode non-severe CDI. These findings support the use of initial vancomycin for all inpatients with CDI, when fidaxomicin is unavailable.
    Language English
    Publishing date 2021-09-30
    Publishing country England
    Document type Journal Article
    ISSN 2732-494X
    ISSN (online) 2732-494X
    DOI 10.1017/ash.2021.194
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: ACP Journal Club. Review: oseltamivir relieves symptoms but does not reduce hospitalizations in influenza.

    Smieja, Marek

    Annals of internal medicine

    2012  Volume 157, Issue 6, Page(s) JC3–5

    Language English
    Publishing date 2012-09-18
    Publishing country United States
    Document type Comment ; Journal Article
    ZDB-ID 336-0
    ISSN 1539-3704 ; 0003-4819
    ISSN (online) 1539-3704
    ISSN 0003-4819
    DOI 10.7326/0003-4819-157-6-201209180-02005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Ua VI Zs.178/2: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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  9. Article ; Online: A classification-based approach to semi-supervised clustering with pairwise constraints.

    Śmieja, Marek / Struski, Łukasz / Figueiredo, Mário A T

    Neural networks : the official journal of the International Neural Network Society

    2020  Volume 127, Page(s) 193–203

    Abstract: In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: ...

    Abstract In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.
    MeSH term(s) Cluster Analysis ; Databases, Factual/trends ; Neural Networks, Computer ; Supervised Machine Learning/trends
    Language English
    Publishing date 2020-04-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 740542-x
    ISSN 1879-2782 ; 0893-6080
    ISSN (online) 1879-2782
    ISSN 0893-6080
    DOI 10.1016/j.neunet.2020.04.017
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2365: Show issues Location:
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  10. Book ; Online: Contrastive Hierarchical Clustering

    Znaleźniak, Michał / Rola, Przemysław / Kaszuba, Patryk / Tabor, Jacek / Śmieja, Marek

    2023  

    Abstract: Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat ... ...

    Abstract Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat partition is limited. In this paper, we introduce CoHiClust, a Contrastive Hierarchical Clustering model based on deep neural networks, which can be applied to typical image data. By employing a self-supervised learning approach, CoHiClust distills the base network into a binary tree without access to any labeled data. The hierarchical clustering structure can be used to analyze the relationship between clusters, as well as to measure the similarity between data points. Experiments demonstrate that CoHiClust generates a reasonable structure of clusters, which is consistent with our intuition and image semantics. Moreover, it obtains superior clustering accuracy on most of the image datasets compared to the state-of-the-art flat clustering models.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-03-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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