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The United States COVID-19 Forecast Hub dataset

Abstract

Not specified

Authors: E. Y. Cramer, Y. Huang, Yijin Wang, Evan L. Ray, Matthew Cornell, Johannes Bracher, A. Brennen, A. J. Castro Rivadeneira, A. Gerding, Katie House, Dasuni Jayawardena, Abdul Hannan Kanji, Ayush Khandelwal, Khoa Le, Vidhi Mody, Vrushti Mody, Jarad Niemi, Ariane Stark, Apurv Shah, Nutcha Wattanchit, Matha W Zorn, Nicholas G. Reich, US COVID-19 Forecast Hub Consortium

Date Published: 1st Aug 2022

Publication Type: Journal

More findability, more interoperability for SABIO-RK, the curated database for biochemical reaction kinetics

Abstract (Expand)

SABIO-RK represents a repository for structured, curated, and annotated data on reactions and their kinetics. The data are manually extracted from the scientific literature and stored in a relational … database. The content comprises both naturally occurring and alternatively measured biochemical reactions, and the data are made available to the public via a web-based search interface as well as easy-to-use JSON web services for programmatic access. Data are highly interlinked to external databases, ontologies, and controlled vocabularies. This includes cross-references with eg Uniprot, ChEBI, KEGG, BRENDA, Biomodels, and MetaNetX. In the past year we have worked on improving findability of SABIO-RK data as well as interoperability: SABIO-RK was extended to read the additional annotations in the EnzymeML data exchange format to allow the direct import of enzymology data from EnzymeML documents. SABIO-RK is part of the EnzymeML workflow to support the data transfer between experimental platforms, modelling tools and databases (Range et al. FEBS J 2021). In the BMBF-funded project SABIO-VIS we focused on visualizing SABIORK data for the purpose of interactive search and search refinement.

Authors: Andreas Weidemann, Dorotea Dudas, Maja Rey, Ulrike Wittig, Wolfgang Müller

Date Published: 1st Aug 2022

Publication Type: InCollection

Graphene BioFET sensors for SARS-CoV-2 detection: a multiscale simulation approach

Abstract (Expand)

Two-dimensional (2D) materials BioFETs have already demonstrated their potential for detecting low amounts of molecules. Here, we present a multiscale simulation platform in the context of Graphene …xt of Graphene BioFET for the detection of SARS-CoV-2.

Authors: A. Toral-Lopez, D. B. Kokh, E. G. Marin, R. C. Wade, A. Godoy

Date Published: 15th Jul 2022

Publication Type: Journal

Multitarget, Selective Compound Design Yields Potent Inhibitors of a Kinetoplastid Pteridine Reductase 1

Abstract

Not specified

Authors: Ina Pöhner, Antonio Quotadamo, Joanna Panecka-Hofman, Rosaria Luciani, Matteo Santucci, Pasquale Linciano, Giacomo Landi, Flavio Di Pisa, Lucia Dello Iacono, Cecilia Pozzi, Stefano Mangani, Sheraz Gul, Gesa Witt, Bernhard Ellinger, Maria Kuzikov, Nuno Santarem, Anabela Cordeiro-da-Silva, Maria P. Costi, Alberto Venturelli, Rebecca C. Wade

Date Published: 14th Jul 2022

Publication Type: Journal

Fine-tuning BERT Models for Summarizing German Radiology Findings

Abstract (Expand)

Writing the conclusion section of radiology reports is essential for communicating the radiology findings and its assessment to physician in a condensed form. In this work, we employ a transformer-based … Seq2Seq model for generating the conclusion section of German radiology reports. The model is initialized with the pretrained parameters of a German BERT model and fine-tuned in our downstream task on our domain data. We proposed two strategies to improve the factual correctness of the model. In the first method, next to the abstractive learning objective, we introduce an extraction learning objective to train the decoder in the model to both generate one summary sequence and extract the key findings from the source input. The second approach is to integrate the pointer mechanism into the transformer-based Seq2Seq model. The pointer network helps the Seq2Seq model to choose between generating tokens from the vocabulary or copying parts from the source input during generation. The results of the automatic and human evaluations show that the enhanced Seq2Seq model is capable of generating human-like radiology conclusions and that the improved models effectively reduce the factual errors in the generations despite the small amount of training data.

Authors: Siting Liang, Klaus Kades, Matthias Fink, Peter Full, Tim Weber, Jens Kleesiek, Michael Strube, Klaus Maier-Hein

Date Published: 14th Jul 2022

Publication Type: InProceedings

The Free Lunch is not over yet – Systematic Exploration of Numerical Thresholds in Phylogenetic Inference

Abstract

Not specified

Authors: Julia Haag, Lukas Hübner, Alexey M. Kozlov, Alexandros Stamatakis

Date Published: 14th Jul 2022

Publication Type: Journal

Incorporating Centering Theory into Neural Coreference Resolution

Abstract (Expand)

In recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. However, how coreference resolvers can benefit from discourse coherence is … still an open question. In this paper, we propose to incorporate centering transitions derived from centering theory in the form of a graph into a neural coreference model. Our method improves the performance over the SOTA baselines, especially on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions.

Authors: Haixia Chai, Michael Strube

Date Published: 10th Jul 2022

Publication Type: InProceedings

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