Filter and export

Publications

What is a Publication?
1703 Publications visible to you, out of a total of 1703
EU-wide mapping report on good practice examples for integrating phenotype and large scale data

Abstract (Expand)

The report focusses on national and EU-case studies (good practice examples) for integrating patient derived data, such as phenotype and large scale data, for in silico modelling in personalized medicine. …Not specified

Authors: Martin Golebiewski, Marc Kirschner, Sylvia Krobitsch, EU-STANDS4PM consortium

Date Published: 15th Dec 2022

Publication Type: Tech report

Constrained density matching and modeling for cross-lingual alignment of contextualized representations

Abstract (Expand)

Multilingual representations pre-trained with monolingual data exhibit considerably unequal task performances across languages. Previous studies address this challenge with resource-intensive contextualized … alignment, which assumes the availability of large parallel data, thereby leaving under-represented language communities behind. In this work, we attribute the data hungriness of previous alignment techniques to two limitations: (i) the inability to sufficiently leverage data and (ii) these techniques are not trained properly. To address these issues, we introduce supervised and unsupervised density-based approaches named Real-NVP and GAN-Real-NVP, driven by Normalizing Flow, to perform alignment, both dissecting the alignment of multilingual subspaces into density matching and density modeling. We complement these approaches with our validation criteria in order to guide the training process. Our experiments encompass 16 alignments, including our approaches, evaluated across 6 language pairs, synthetic data and 5 NLP tasks. We demonstrate the effectiveness of our approaches in the scenarios of limited and no parallel data. First, our supervised approach trained on 20k parallel data (sentences) mostly surpasses Joint-Align and InfoXLM trained on over 100k parallel sentences. Second, parallel data can be removed without sacrificing performance when integrating our unsupervised approach in our bootstrapping procedure, which is theoretically motivated to enforce equality of multilingual subspaces. Moreover, we demonstrate the advantages of validation criteria over validation data for guiding supervised training.

Authors: Wei Zhao, Steffen Eger

Date Published: 12th Dec 2022

Publication Type: InProceedings

Computational Investigation of the Structure Kinetics Relationship of Small-Molecule Compounds Binding the Histamine-1-Receptor

Abstract

Not specified

Author: Melanie Kaeser

Date Published: 9th Dec 2022

Publication Type: Master's Thesis

Destabilizers of the thymidylate synthase homodimer accelerate its proteasomal degradation and inhibit cancer growth.

Abstract (Expand)

Drugs that target human thymidylate synthase (hTS), a dimeric enzyme, are widely used in anticancer therapy. However, treatment with classical substrate-site-directed TS inhibitors induces over-expression … of this protein and development of drug resistance. We thus pursued an alternative strategy that led us to the discovery of TS-dimer destabilizers. These compounds bind at the monomer-monomer interface and shift the dimerization equilibrium of both the recombinant and the intracellular protein toward the inactive monomers. A structural, spectroscopic, and kinetic investigation has provided evidence and quantitative information on the effects of the interaction of these small molecules with hTS. Focusing on the best among them, E7, we have shown that it inhibits hTS in cancer cells and accelerates its proteasomal degradation, thus causing a decrease in the enzyme intracellular level. E7 also showed a superior anticancer profile to fluorouracil in a mouse model of human pancreatic and ovarian cancer. Thus, over sixty years after the discovery of the first TS prodrug inhibitor, fluorouracil, E7 breaks the link between TS inhibition and enhanced expression in response, providing a strategy to fight drug-resistant cancers.

Authors: L. Costantino, S. Ferrari, M. Santucci, O. M. H. Salo-Ahen, E. Carosati, S. Franchini, A. Lauriola, C. Pozzi, M. Trande, G. Gozzi, P. Saxena, G. Cannazza, L. Losi, D. Cardinale, A. Venturelli, A. Quotadamo, P. Linciano, L. Tagliazucchi, M. G. Moschella, R. Guerrini, S. Pacifico, R. Luciani, F. Genovese, S. Henrich, S. Alboni, N. Santarem, A. da Silva Cordeiro, E. Giovannetti, G. J. Peters, P. Pinton, A. Rimessi, G. Cruciani, R. M. Stroud, R. C. Wade, S. Mangani, G. Marverti, D. D'Arca, G. Ponterini, M. P. Costi

Date Published: 7th Dec 2022

Publication Type: Journal

Review of Deep Learning Methods for Individual Treatment Effect Estimation with Automatic Hyperparameter Optimization

Abstract (Expand)

<p>Abstract—Estimation of individual treatment effect (ITE) for different types of treatment is a common challenge in therapy assessments, clinical trials and diagnosis. Deep learning methods, …ing methods, namely representation based, adversarial, and variational, have shown promising potential in ITE estimation. However, it was unclear whether the hyperparameters of the originally proposed methods were well optimized for different benchmark datasets. To solve these problems, we created a public code library containing representation-based, adversarial, and variational methods written in TensorFlow. In order to have a broader collection of ITE estimation methods, we have also included neural network based meta-learners. The code library is made accessible for reproducibility and facilitating future works in the field of causal inference. Our results demonstrate that performance of most methods can be improved using automatic hyperparameter optimization. Additionally, we review the methods and compare the performance of the optimized models from our library on publicly available datasets. The potential of hyperparameter optimization may encourage researchers to focus on this aspect when creating new methods for inferring individual treatment effect.</p>

Authors: Andrei Sirazitdinov, Marcus Buchwald, Jürgen Hesser, Vincent Heuveline

Date Published: 6th Dec 2022

Publication Type: Misc

Bond dissociation energies of X–H bonds in proteins

Abstract (Expand)

Knowledge of reliable X−H bond dissociation energies (X = C, N, O, S) for amino acids in proteins is key for studying the radical chemistry of proteins. X−H bond dissociation energies of model dipeptides … were computed using the isodesmic reaction method at the BMK/6-31+G(2df,p) and G4(MP2)-6X levels of theory. The density functional theory values agree well with the composite- level calculations. By this high level of theory, combined with a careful choice of reference compounds and peptide model systems, our work provides a highly valuable data set of bond dissociation energies with unprecedented accuracy and comprehensiveness. It will likely prove useful to predict protein biochemistry involving radicals, e.g., by machine learning.

Authors: Authors Wojtek Treyde, Kai Riedmiller, Frauke Gräter

Date Published: 1st Dec 2022

Publication Type: Journal

Identification of antiparasitic drug targets using a multi-omics workflow in the acanthocephalan model

Abstract (Expand)

Abstract Background With the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated …er, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans ( Pomphorhynchus laevis, Neoechinorhynchus agilis , Neoechinorhynchus buttnerae ) from four fish species (common barbel, European eel, thinlip mullet, tambaqui). Results The workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel. Conclusions The combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7 .

Authors: Hanno Schmidt, Katharina Mauer, Manuel Glaser, Bahram Sayyaf Dezfuli, Sören Lukas Hellmann, Ana Lúcia Silva Gomes, Falk Butter, Rebecca C. Wade, Thomas Hankeln, Holger Herlyn

Date Published: 1st Dec 2022

Publication Type: Journal

Powered by
 (v.1.16.0)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH