Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Bioinformatics
Title: MIiSR: Molecular Interactions in Super-Resolution Imaging Enables the Analysis of Protein Interactions, Dynamics and Formation of Multi-protein Structures
Abstract: Our current understanding of the molecular mechanisms which regulate cellular processes such as vesicular trafficking has been enabled by conventional biochemical and microscopy techniques. However, these methods often obscure the heterogeneity of the cellular environment, thus precluding a quantitative assessment of the molecular interactions regulating these processes. Herein, we present Molecular Interactions in Super Resolution (MIiSR) software which provides quantitative analysis tools for use with super-resolution images. MIiSR combines multiple tools for analyzing intermolecular interactions, molecular clustering and image segmentation. These tools enable quantification, in the native environment of the cell, of molecular interactions and the formation of higher-order molecular complexes. The capabilities and limitations of these analytical tools are demonstrated using both modeled data and examples derived from the vesicular trafficking system, thereby providing an established and validated experimental workflow capable of quantitatively assessing molecular interactions and molecular complex formation within the heterogeneous environment of the cell.
Extracted statement — creates: Molecular Interactions in Super Resolution
Research field: Bioinformatics
Title: PeroxisomeDB: a database for the peroxisomal proteome, functional genomics and disease
Abstract: Peroxisomes are essential organelles of eukaryotic origin, ubiquitously distributed in cells and organisms, playing key roles in lipid and antioxidant metabolism. Loss or malfunction of peroxisomes causes more than 20 fatal inherited conditions. We have created a peroxisomal database () that includes the complete peroxisomal proteome of Homo sapiens and Saccharomyces cerevisiae, by gathering, updating and integrating the available genetic and functional information on peroxisomal genes. PeroxisomeDB is structured in interrelated sections ‘Genes’, ‘Functions’, ‘Metabolic pathways’ and ‘Diseases’, that include hyperlinks to selected features of NCBI, ENSEMBL and UCSC databases. We have designed graphical depictions of the main peroxisomal metabolic routes and have included updated flow charts for diagnosis. Precomputed BLAST, PSI-BLAST, multiple sequence alignment (MUSCLE) and phylogenetic trees are provided to assist in direct multispecies comparison to study evolutionary conserved functions and pathways. Highlights of the PeroxisomeDB include new tools developed for facilitating (i) identification of novel peroxisomal proteins, by means of identifying proteins carrying peroxisome targeting signal (PTS) motifs, (ii) detection of peroxisomes in silico, particularly useful for screening the deluge of newly sequenced genomes. PeroxisomeDB should contribute to the systematic characterization of the peroxisomal proteome and facilitate system biology approaches on the organelle.
Extracted statement — Has method: Multiple Sequence Alignment
Research field: Bioinformatics
Title: Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network
Abstract: Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms.
Extracted statement — Used models: naive Bayes
Research field: Bioinformatics
Title: LINNAEUS: A species name identification system for biomedical literature
Abstract: Abstract Background The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles. Results In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers. Conclusions LINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://linnaeus.sourceforge.net/" ext-link-type="uri">http://linnaeus.sourceforge.net/</jats:ext-link>.
Extracted statement — Other resources: NCBI Taxonomy
Research field: Bioinformatics
Title: Implementing LOINC – Current Status and Ongoing Work at a Medical University
Abstract: The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.
Extracted statement — Data: our challenges and the lessons
Research field: Bioinformatics
Title: DCNN and DNN based multi-modal depression recognition
Abstract: In this paper, we propose an audio visual multimodal depression recognition framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models. For each modality, corresponding feature descriptors are input into a DCNN to learn high-level global features with compact dynamic information, which are then fed into a DNN to predict the PHQ-8 score. For multi-modal depression recognition, the predicted PHQ-8 scores from each modality are integrated in a DNN for the final prediction. In addition, we propose the Histogram of Displacement Range as a novel global visual descriptor to quantify the range and speed of the facial landmarks' displacements. Experiments have been carried out on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset for the Depression Sub-challenge of the Audio-Visual Emotion Challenge (AVEC 2016), results show that the proposed multi-modal depression recognition framework obtains very promising results on both the development set and test set, which outperforms the state-of-the-art results.
Extracted statement — Outcome assessment: PHQ-8 score
Research field: Bioinformatics
Title: FIMTrack: An open source tracking and locomotion analysis software for small animals
Abstract: Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at https://github.com/i-git/FIMTrack and pre-compiled binaries for Windows and Mac are available at http://fim.uni-muenster.de.
Extracted statement — deposits: pre-compiled binaries
Research field: Bioinformatics
Title: Biomedical named entity recognition and linking datasets: survey and our recent development
Abstract: Natural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein-protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. In this study, we first review commonlyused BNER datasets and their potential annotation problems such as inconsistency and low portability. Then, we introduce a revised version of the JNLPBA dataset that solves potential problems in the original and use state-of-the-art named entity recognition systems to evaluate its portability to different kinds of biomedical literature, including protein-protein interaction and biology events. Lastly, we introduce an ensembled biomedical entity dataset (EBED) by extending the revised JNLPBA dataset with PubMed Central full-text paragraphs, figure captions and patent abstracts. This EBED is a multi-task dataset that covers annotations including gene, disease and chemical entities. In total, it contains 85000 entity mentions, 25000 entity mentions with database identifiers and 5000 attribute tags. To demonstrate the usage of the EBED, we review the BNER track from the AI CUP Biomedical Paper Analysis challenge. Availability: The revised JNLPBA dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/Re vised_JNLPBA.zip. The EBED dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/AICUP _EBED_dataset.rar. Contact: Email: thtsai@g.ncu.edu.tw, Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: hsu@iis.sinica.edu.tw, Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.
Extracted statement — Data domains: Protein-Protein Interaction Extraction (PPIE)
Research field: Bioinformatics
Title: Comprehensive functional genomic resource and integrative model for the human brain
Abstract: <jats:p>Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with &gt;88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.</jats:p>
Extracted statement — Data: Regulatory network
Research field: Bioinformatics
Title: Estimating genetic kin relationships in prehistoric populations
Abstract: Archaeogenomic research has proven to be a valuable tool to trace migrations of historic and prehistoric individuals and groups, whereas relationships within a group or burial site have not been investigated to a large extent. Knowing the genetic kinship of historic and prehistoric individuals would give important insights into social structures of ancient and historic cultures. Most archaeogenetic research concerning kinship has been restricted to uniparental markers, while studies using genome-wide information were mainly focused on comparisons between populations. Applications which infer the degree of relationship based on modern-day DNA information typically require diploid genotype data. Low concentration of endogenous DNA, fragmentation and other post-mortem damage to ancient DNA (aDNA) makes the application of such tools unfeasible for most archaeological samples. To infer family relationships for degraded samples, we developed the software READ (Relationship Estimation from Ancient DNA). We show that our heuristic approach can successfully infer up to second degree relationships with as little as 0.1x shotgun coverage per genome for pairs of individuals. We uncover previously unknown relationships among prehistoric individuals by applying READ to published aDNA data from several human remains excavated from different cultural contexts. In particular, we find a group of five closely related males from the same Corded Ware culture site in modern-day Germany, suggesting patrilocality, which highlights the possibility to uncover social structures of ancient populations by applying READ to genome-wide aDNA data. READ is publicly available from https://bitbucket.org/tguenther/read.
Extracted statement — creates: Relationship Estimation from Ancient DNA
Research field: Bioinformatics
Title: Efficacy and tolerability of short-term duloxetine treatment in adults with generalized anxiety disorder: A meta-analysis
Abstract: Objective To investigate the efficacy and tolerability of duloxetine during short-term treatment in adults with generalized anxiety disorder (GAD). Methods We conducted a comprehensive literature review of the PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and ClinicalTrials databases for randomized controlled trials(RCTs) comparing duloxetine or duloxetine plus other antipsychotics with placebo for the treatment of GAD in adults. Outcome measures were (1) efficacy, assessed by the Hospital Anxiety and Depression Scale(HADS) anxiety subscale score, the Hamilton Rating Scale for Anxiety(HAM-A) psychic and somatic anxiety factor scores, and response and remission rates based on total scores of HAM-A; (2) tolerability, assessed by discontinuation rate due to adverse events, the incidence of treatment emergent adverse events(TEAEs) and serious adverse events(SAEs). Review Manager 5.3 and Stata Version 12.0 software were used for all statistical analyses. Results The meta-analysis included 8 RCTs. Mean changes in the HADS anxiety subscale score [mean difference(MD) = 2.32, 95% confidence interval(CI) 1.77–2.88, P<0.00001] and HAM-A psychic anxiety factor score were significantly greater in patients with GAD that received duloxetine compared to those that received placebo (MD = 2.15, 95%CI 1.61–2.68, P<0.00001). However, there was no difference in mean change in the HAM-A somatic anxiety factor score (MD = 1.13, 95%CI 0.67–1.58, P<0.00001). Discontinuation rate due to AEs in the duloxetine group was significantly higher than the placebo group [odds ratio(OR) = 2.62, 95%CI 1.35–5.06, P = 0.004]. The incidence of any TEAE was significantly increased in patients that received duloxetine (OR = 1.76, 95%CI 1.36–2.28, P<0.0001), but there was no significant difference in the incidence of SAEs (OR = 1.13, 95%CI 0.52–2.47, P = 0.75). Conclusion Duloxetine resulted in a greater improvement in symptoms of psychic anxiety and similar changes in symptoms of somatic anxiety compared to placebo during short-term treatment in adults with GAD and its tolerability was acceptable.
Extracted statement — uses: Review Manager
Research field: Bioinformatics
Title: Implementing LOINC – Current Status and Ongoing Work at a Medical University
Abstract: The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.
Extracted statement — Data: routine laboratory data
Research field: Bioinformatics
Title: Detecting Stress Based on Social Interactions in Social Networks
Abstract: Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users’ stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model - a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 percent in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e., the number of social structures of sparse connections (i.e., with no delta connections) of stressed users is around 14 percent higher than that of non-stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed users.
Extracted statement — Data: social interactions
Research field: Bioinformatics
Title: FIMTrack: An open source tracking and locomotion analysis software for small animals
Abstract: Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at https://github.com/i-git/FIMTrack and pre-compiled binaries for Windows and Mac are available at http://fim.uni-muenster.de.
Extracted statement — deposits: source code
Research field: Bioinformatics
Title: Neurological manifestations in chronic hepatitis C patients receiving care in a reference hospital in sub-Saharan Africa: A cross-sectional study
Abstract: Background Chronic hepatitis C infection is a major public health concern, with a high burden in Sub-Saharan Africa. There is growing evidence that chronic hepatitis C virus (HCV) infection causes neurological complications. This study aimed at assessing the prevalence and factors associated with neurological manifestations in chronic hepatitis C patients. Methods Through a cross-sectional design, a semi-structured questionnaire was used to collect data from consecutive chronic HCV infected patients attending the outpatient gastroenterology unit of the Douala General Hospital (DGH). Data collection was by interview, patient record review (including HCV RNA quantification, HCV genotyping and the assessment of liver fibrosis and necroinflammatory activity), clinical examination complemented by 3 tools; Neuropathic pain diagnostic questionnaire, Brief peripheral neuropathy screen and mini mental state examination score. Data were analysed using Statistical package for social sciences version 20 for windows. Results Of the 121 chronic hepatitis C patients (51.2% males) recruited, 54.5% (95% Confidence interval: 46.3%, 62.8%) had at least one neurological manifestation, with peripheral nervous system manifestations being more common (50.4%). Age ≥ 55 years (Adjusted Odds Ratio: 4.82, 95%CI: 1.02–18.81, p = 0.02), longer duration of illness (AOR: 1.012, 95%CI: 1.00–1.02, p = 0.01) and high viral load (AOR: 3.40, 95% CI: 1.20–9.64, p = 0.02) were significantly associated with neurological manifestations. Peripheral neuropathy was the most common neurological manifestation (49.6%), presenting mainly as sensory neuropathy (47.9%). Age ≥ 55 years (AOR: 6.25, 95%CI: 1.33–29.08, p = 0.02) and longer duration of illness (AOR: 1.01, 1.00–1.02, p = 0.01) were significantly associated with peripheral neuropathy. Conclusion Over half of the patients with chronic hepatitis C attending the DGH have a neurological manifestation, mainly presenting as sensory peripheral neuropathy. Routine screening of chronic hepatitis C patients for peripheral neuropathy is therefore necessary, with prime focus on those with older age and longer duration of illness.
Extracted statement — uses: Statistical Package for Social Sciences