bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between Intra-Individual Neurocognitive Variability and Prospective Memory in Veterans with Mild Traumatic Brain Injury History and Posttraumatic Stress Disorder
Authors: Sheppard David P , Rau Holly K , Werhane Madeleine L , Fonseca Luciana Mascarenhas , Chaytor Naomi S , Peskind Elaine R , Pagulayan Kathleen F 
Published: April 2022

AbstractObjectiveMild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) frequently co-occur and are associated with neurocognitive intra-individual variability (IIV) and difficulty with prospective memory (PM). The current study aimed to examine associations between IIV and PM in this comorbid group.MethodFifty veterans with a history of blast mTBI and current comorbid PTSD completed a standardized neurocognitive battery to measure IIV, and the Memory for Intentions Screening Test measuring PM.ResultsAdjusting for age, education, and race, higher IIV was associated with poorer time-based PM (p &lt; .001, f2 = .34), but not event-based PM. In a subset of the sample with self-report data, higher IIV was associated with poorer self-reported retrospective memory, but not PM.ConclusionsCognitive variability on a standardized neuropsychological battery was associated with strategically demanding PM, which is an ecologically relevant ability and highlights the possible connection between subtle cognitive difficulties in-clinic and those experienced in daily life.
Neuropsychological Profile of Hereditary Ataxias: Study of 38 Patients
Authors: García Maitane , Rouco-Axpe Idoia , Amayra Imanol , Rodríguez-Antigüedad Alfredo , Catalli Claudio , Cabrera-Zubizarreta Alberto , Rodríguez Alicia Aurora , Pérez Manuel 
Published: April 2022

Abstract
Hereditary ataxias are a heterogeneous group of disorders characterized by degeneration of the cerebellum and its connections. It is known that patients with ataxia can manifest a broad spectrum of motor symptoms; however, current research has emphasized the relevance of cognitive disturbances. The aim of this study is to analyze the presence of cognitive impairment in a heterogeneous cohort of patients with hereditary ataxia (HA). A group of 38 patients with HA of different etiologies and a matched group of 38 healthy controls were recruited and evaluated through a comprehensive battery of neuropsychological tests. The findings show a worse performance in ataxic patients on planning, visuospatial skills, naming, and Theory of Mind tasks, regardless their physical and psychological symptomatology. The influence of clinical status as well as functional ability-related variables on their performance were analyzed, showing that the level of disability and motor disturbances have a significant effect on verbal memory, verbal fluency, and working memory. These findings suggest that patients with HA can manifest cognitive and neuropsychiatric symptoms as part of their clinical features, which demands its inclusion for the diagnosis and management of the disease.
A quality control portal for sequencing data deposited at the European genome–phenome archive
Authors: Fernández-Orth Dietmar , Rueda Manuel , Singh Babita , Moldes Mauricio , Jene Aina , Ferri Marta , Vasallo Claudia , Fromont Lauren A , Navarro Arcadi , Rambla Jordi 
Published: April 2022

Abstract
Since its launch in 2008, the European Genome–Phenome Archive (EGA) has been leading the archiving and distribution of human identifiable genomic data. In this regard, one of the community concerns is the potential usability of the stored data, as of now, data submitters are not mandated to perform any quality control (QC) before uploading their data and associated metadata information. Here, we present a new File QC Portal developed at EGA, along with QC reports performed and created for 1 694 442 files [Fastq, sequence alignment map (SAM)/binary alignment map (BAM)/CRAM and variant call format (VCF)] submitted at EGA. QC reports allow anonymous EGA users to view summary-level information regarding the files within a specific dataset, such as quality of reads, alignment quality, number and type of variants and other features. Researchers benefit from being able to assess the quality of data prior to the data access decision and thereby, increasing the reusability of data (https://ega-archive.org/blog/data-upcycling-powered-by-ega/).
Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer’s disease
Authors: Bi Xia-an , Zhou Wenyan , Luo Sheng , Mao Yuhua , Hu Xi , Zeng Bin , Xu Luyun 
Published: April 2022

AbstractThe roles of brain regions activities and gene expressions in the development of Alzheimer’s disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region–gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.
On the influence of several factors on pathway enrichment analysis
Authors: Mubeen Sarah , Tom Kodamullil Alpha , Hofmann-Apitius Martin , Domingo-Fernández Daniel 
Published: April 2022

AbstractPathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.
D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19
Authors: Yang Yanqing , Zhou Deshan , Zhang Xinben , Shi Yulong , Han Jiaxin , Zhou Liping , Wu Leyun , Ma Minfei , Li Jintian , Peng Shaoliang , Xu Zhijian , Zhu Weiliang 
Published: April 2022

Abstract
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
Directed graph attention networks for predicting asymmetric drug–drug interactions
Authors: Feng Yi-Yang , Yu Hui , Feng Yue-Hua , Shi Jian-Yu 
Published: April 2022

Abstract
It is tough to detect unexpected drug–drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based approaches, are promising to screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles of two drugs in interaction. Such an asymmetry is crucial to poly-drug treatments since it determines drug priority in co-prescription. This paper designs a directed graph attention network (DGAT-DDI) to predict asymmetric DDIs. First, its encoder learns the embeddings of the source role, the target role and the self-roles of a drug. The source role embedding represents how a drug influences other drugs in DDIs. In contrast, the target role embedding represents how it is influenced by others. The self-role embedding encodes its chemical structure in a role-specific manner. Besides, two role-specific items, aggressiveness and impressionability, capture how the number of interaction partners of a drug affects its interaction tendency. Furthermore, the predictor of DGAT-DDI discriminates direction-specific interactions by the combination between two proximities and the above two role-specific items. The proximities measure the similarity between source/target embeddings and self-role embeddings. In the designated experiments, the comparison with state-of-the-art deep learning models demonstrates the superiority of DGAT-DDI across a direction-specific predicting task and a direction-blinded predicting task. An ablation study reveals how well each component of DGAT-DDI contributes to its ability. Moreover, a case study of finding novel DDIs confirms its practical ability, where 7 out of the top 10 candidates are validated in DrugBank.
NSCGRN: a network structure control method for gene regulatory network inference
Authors: Liu Wei , Sun Xingen , Yang Li , Li Kaiwen , Yang Yu , Fu Xiangzheng 
Published: April 2022

AbstractAccurate inference of gene regulatory networks (GRNs) is an essential premise for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but the identification of redundant regulation remains a challenge faced by researchers. Although combining global and local topology can identify and reduce redundant regulations, the topologies’ specific forms and cooperation modes are unclear and real regulations may be sacrificed. Here, we propose a network structure control method [network-structure-controlling-based GRN inference method (NSCGRN)] that stipulates the global and local topology’s specific forms and cooperation mode. The method is carried out in a cooperative mode of ‘global topology dominates and local topology refines’. Global topology requires layering and sparseness of the network, and local topology requires consistency of the subgraph association pattern with the network motifs (fan-in, fan-out, cascade and feedforward loop). Specifically, an ordered gene list is obtained by network topology centrality sorting. A Bernaola–Galvan mutation detection algorithm applied to the list gives the hierarchy of GRNs to control the upstream and downstream regulations within the global scope. Finally, four network motifs are integrated into the hierarchy to optimize local complex regulations and form a cooperative mode where global and local topologies play the dominant and refined roles, respectively. NSCGRN is compared with state-of-the-art methods on three different datasets (six networks in total), and it achieves the highest F1 and Matthews correlation coefficient. Experimental results show its unique advantages in GRN inference.
A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction
Authors: Yaish Ofir , Asif Maor , Orenstein Yaron 
Published: April 2022

Abstract
CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While this editing technique is quite accurate in the target region, there may be many unplanned off-target sites (OTSs). Consequently, a plethora of computational methods have been developed to predict off-target cleavage sites given a guide RNA and a reference genome. However, these methods are based on small-scale datasets (only tens to hundreds of OTSs) produced by experimental techniques to detect OTSs with a low signal-to-noise ratio. Recently, CHANGE-seq, a new in vitro experimental technique to detect OTSs, was used to produce a dataset of unprecedented scale and quality (&gt;200 000 OTS over 110 guide RNAs). In addition, the same study included in cellula GUIDE-seq experiments for 58 of the guide RNAs. Here, we fill the gap in previous computational methods by utilizing these data to systematically evaluate data processing and formulation of the CRISPR OTSs prediction problem. Our evaluations show that data transformation as a pre-processing phase is critical prior to model training. Moreover, we demonstrate the improvement gained by adding potential inactive OTSs to the training datasets. Furthermore, our results point to the importance of adding the number of mismatches between guide RNAs and their OTSs as a feature. Finally, we present predictive off-target in cellula models based on both in vitro and in cellula data and compare them to state-of-the-art methods in predicting true OTSs. Our conclusions will be instrumental in any future development of an off-target predictor based on high-throughput datasets.
Biological activities of drug inactive ingredients
Authors: Zhang Chenyang , Mou Minjie , Zhou Ying , Zhang Wei , Lian Xichen , Shi Shuiyang , Lu Mingkun , Sun Huaicheng , Li Fengcheng , Wang Yunxia , Zeng Zhenyu , Li Zhaorong , Zhang Bing , Qiu Yunqing , Zhu Feng , Gao Jianqing 
Published: April 2022

AbstractIn a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs’ activity data, which was the first evaluation on the possibility to predict DIG’s activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.
Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary
Authors: Blecua Pedro , Davalos Veronica , de Villasante Izar , Merkel Angelika , Musulen Eva , Coll-SanMartin Laia , Esteller Manel 
Published: April 2022

Abstract
High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package ‘conumee’, to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as ‘conumee-KCN’, improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.
Improved drug–target interaction prediction with intermolecular graph transformer
Authors: Liu Siyuan , Wang Yusong , Deng Yifan , He Liang , Shao Bin , Yin Jian , Zheng Nanning , Liu Tie-Yan , Wang Tong 
Published: April 2022

AbstractThe identification of active binding drugs for target proteins (referred to as drug–target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.
ReadBouncer: precise and scalable adaptive sampling for nanopore sequencing
Authors: Ulrich Jens-Uwe , Lutfi Ahmad , Rutzen Kilian , Renard Bernhard Y 
Published: April 2022

AbstractMotivationNanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications.ResultsHere, we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters. ReadBouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without GPUs, making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background.Availability and implementationThe C++ source code is available at https://gitlab.com/dacs-hpi/readbouncer.Supplementary informationSupplementary data are available at Bioinformatics online.
MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
Authors: Zhang Xianglin , Wang Xiaowo 
Published: April 2022

AbstractMotivationIntermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.ResultsWe proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.Availability and implementationMeConcord is available at https://github.com/WangLabTHU/MeConcord.Supplementary informationSupplementary data are available at Bioinformatics online.