bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using discriminative vector machine model with 2DPCA to predict interactions among proteins
Authors: Li Zhengwei , Nie Ru , You Zhuhong , Cao Chen , Li Jiashu 
Published: December 2019

Abstract
Background
The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades.

Results
Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets.

Conclusions
All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.
Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion
Authors: Wang Shunfang , Wang Xiaoheng 
Published: December 2019

Abstract
Background
Protein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes.

Results
We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master.

Conclusions
Experimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.
Classification of adaptor proteins using recurrent neural networks and PSSM profiles
Authors: Khanh Le Nguyen Quoc , Nguyen Quang H. , Chen Xuan , Rahardja Susanto , Nguyen Binh P. 
Published: December 2019

Abstract
Background
Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem.

Results
Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively.

Conclusions
This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.
iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks
Authors: Nguyen Quang H. , Nguyen-Vo Thanh-Hoang , Le Nguyen Quoc Khanh , Do Trang T.T. , Rahardja Susanto , Nguyen Binh P. 
Published: December 2019

Abstract
Background
Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and k-mers for data transformation and ensembles of convolutional neural networks for model construction, to identify enhancers and classify their strength. The benchmark dataset from Liu et al.’s study was used to develop and evaluate the ensemble models. A comparative analysis between iEnhancer-ECNN and existing state-of-the-art methods was done to fairly assess the model performance.

Results
Our experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews’s correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively.

Conclusions
iEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.
JCDB: a comprehensive knowledge base for Jatropha curcas, an emerging model for woody energy plants
Authors: Zhang Xuan , Pan Bang-Zhen , Chen Maosheng , Chen Wen , Li Jing , Xu Zeng-Fu , Liu Changning 
Published: December 2019

Abstract
Background
Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energy plants. With the development of high-throughput sequencing, the genome of Jatropha curcas has been sequenced by different groups and a mass of transcriptome data was released. How to integrate and analyze these omics data is crucial for functional genomics research on J. curcas.

Results
By establishing pipelines for processing novel gene identification, gene function annotation, and gene network construction, we systematically integrated and analyzed a series of J. curcas transcriptome data. Based on these data, we constructed a J. curcas database (JCDB), which not only includes general gene information, gene functional annotation, gene interaction networks, and gene expression matrices but also provides tools for browsing, searching, and downloading data, as well as online BLAST, the JBrowse genome browser, ID conversion, heatmaps, and gene network analysis tools.

Conclusions
JCDB is the most comprehensive and well annotated knowledge base for J. curcas. We believe it will make a valuable contribution to the functional genomics study of J. curcas. The database is accessible at http://jcdb.liu-lab.com/.
Commensal microbiota modulates larval foraging behaviour, development rate and pupal production in Bactrocera tryoni
Authors: Morimoto Juliano , Nguyen Binh , Tabrizi Shabnam T. , Lundbäck Ida , Taylor Phillip W. , Ponton Fleur , Chapman Toni A. 
Published: December 2019

Abstract
Backround
Commensal microbes can promote survival and growth of developing insects, and have important fitness implications in adulthood. Insect larvae can acquire commensal microbes through two main routes: by vertical acquisition from maternal deposition of microbes on the eggshells and by horizontal acquisition from the environment where the larvae develop. To date, however, little is known about how microbes acquired through these different routes interact to shape insect development. In the present study, we investigated how vertically and horizontally acquired microbiota influence larval foraging behaviour, development time to pupation and pupal production in the Queensland fruit fly (‘Qfly’), Bactrocera tryoni.

Results
Both vertically and horizontally acquired microbiota were required to maximise pupal production in Qfly. Moreover, larvae exposed to both vertically and horizontally acquired microbiota pupated sooner than those exposed to no microbiota, or only to horizontally acquired microbiota. Larval foraging behaviour was also influenced by both vertically and horizontally acquired microbiota. Larvae from treatments exposed to neither vertically nor horizontally acquired microbiota spent more time overall on foraging patches than did larvae of other treatments, and most notably had greater preference for diets with extreme protein or sugar compositions.

Conclusion
The integrity of the microbiota early in life is important for larval foraging behaviour, development time to pupation, and pupal production in Qflies. These findings highlight the complexity of microbial relations in this species, and provide insights to the importance of exposure to microbial communities during laboratory- or mass-rearing of tephritid fruit flies.
Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data
Authors: Wang Qing , Kotoula Vassiliki , Hsu Pei-Chen , Papadopoulou Kyriaki , Ho Joshua W. K. , Fountzilas George , Giannoulatou Eleni 
Published: December 2019

Abstract
Background
The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed.

Methods
We have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed.

Results
We have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error.

Conclusions
Our results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.
wtest: an integrated R package for genetic epistasis testing
Authors: Sun Rui , Xia Xiaoxuan , Chong Ka Chun , Zee Benny Chung-Ying , Wu William Ka Kei , Wang Maggie Haitian 
Published: December 2019

AbstractBackgroundWith the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets.ResultsThe wtest R package performs association testing for main effects, pairwise and high order interactions in genome-wide association study data, and cis-regulation of SNP and CpG sites in genome-wide and epigenome-wide data. The software includes a number of post-test diagnostic and analysis functions and offers an integrated toolset for genetic epistasis testing.ConclusionsThe wtest is an efficient and powerful statistical tool for integrated genetic epistasis testing. The package is available in CRAN:https://CRAN.R-project.org/package=wtest.
Microbiome changes: an indicator of Parkinson’s disease?
Authors: Haikal Caroline , Chen Qian-Qian , Li Jia-Yi 
Published: December 2019

AbstractParkinson’s disease is characterized by dopaminergic neuron loss and intracellular inclusions composed mainly of alpha synuclein (α-syn), but the mechanism of pathogenesis is still obscure. In recent years, more attention has been given to the gut as a key player in the initiation and progression of PD pathology. Several studies characterizing changes in the microbiome, particularly the gut microbiome, have been conducted. Although many studies found a decrease in the bacterial family Prevotellaceae and in butyrate-producing bacterial genera such as Roseburia and Faecalibacteria, and an increase in the genera Akkermansia many of the studies reported contradictory findings. In this review, we highlight the findings from the different studies and reflect on the future of microbiome studies in PD research.
Power quality disturbance classification based on time-frequency domain multi-feature and decision tree
Authors: Zhao Wenjing , Shang Liqun , Sun Jinfan 
Published: December 2019

AbstractAccurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.
Comparative effects of crop residue incorporation and inorganic potassium fertilization on soil C and N characteristics and microbial activities in cotton field
Authors: HU Wei , SUI Ning , YU Chaoran , YANG Changqin , LIU Ruixian , ZHOU Zhiguo 
Published: December 2019

Abstract
Background
Crop residue incorporation into the soil is an effective method to augment soil potassium (K) content, and effects of crop residue and K fertilizer on soil K balance have been compared. However, their influences on other soil characteristics such as carbon (C) and nitrogen (N) characteristics and microbial activities have not been quantified. To address this, field experiments were conducted in 2011 at Dafeng (sandy loam) and Nanjing (clay loam) in China with treatments including blank control without crop residue incorporation and K fertilizer application, 0.9 t·ha− 1 wheat straw incorporation (W1C0), 0.7 t·ha− 1 cotton residue incorporation (W0C1), 0.9 t·ha− 1 wheat straw + 0.7 t·ha− 1 cotton residue incorporation (W1C1) and two K fertilizer rates (150 and 300 kg·ha− 1(K2O)) during the cotton season.

Results
Compared with control, K fertilizer treatments did not alter soil water-soluble organic carbon/soil organic carbon (WSOC/SOC) ratio, microbial biomass carbon (MBC)/SOC ratio, MBC/microbial biomass nitrogen (MBN) ratio, water inorganic nitrogen/total nitrogen ratio (WIN/TN), the number of cellulose-decomposing bacteria, or related enzymes activities, however, W0C1, W1C0 and W1C1 treatments significantly increased WSOC/SOC ratio, MBC/SOC ratio and MBC/ MBN ratio, and decreased WIN/TN ratio at both sites. W0C1, W1C0 and W1C1 treatments also increased the number of soil cellulose-decomposing bacteria and activities of cellulase, β-glucosidase and arylamidase. Regarding different crop residue treatments, W1C0 and W1C1 treatments had more significant influences on above mentioned parameters than W0C1 treatment. Moreover, MBC/MBN ratio was the most important factor to result in the differences in the number of cellulose-decomposing bacteria and soil enzymes activities among different treatments.

Conclusions
Short-term K fertilizer application doesn’t affect soil C and N availability and microbial activities. However, crop residue incorporation alters soil C and N characteristics and microbial activities, and the influence of wheat straw is much stronger than that of cotton straw.
Functional MRI in the pre-operative assessment of GI-RADS 3, 4, and 5 ovarian masses
Authors: Ibrahim Rania S. M. , Maher Manar A. E. L. O. , Abdalaziz Solava , Amer Samar , Shafie Doaa , Hamed Soha T. 
Published: December 2019

Abstract
Background
Characterization of an ovarian lesion is a diagnostic challenge. A correct preoperative assessment is of great importance so as to arrange adequate therapeutic procedures. The aim of the current study is to evaluate the diagnostic performance of functional MRI in differentiation between malignant, borderline, and benign ovarian masses.

Results
This study included 56 adnexal lesions. Bilateral synchronous ovarian lesions are detected in 16 cases. Postoperative histologically proved to be benign in 17 (30%), borderline (low potential malignancy) in 12 (22%), and malignant in 27 (48%). The overall diagnostic performance of conventional MRI in the diagnosis of adenexal lesion was a sensitivity of 74%, specificity of 47%, positive predictive value (PPV) of 76%, negative predictive value (NPV) of 44%, and an accuracy of 66%. Functional pelvic MRI examination showed an increase in overall diagnostic performance compared to conventional values with the highest sensitivity of 90% and NPV of 67% using DWI, and the highest specificity of 88%, PPV of 94%, and an accuracy of 82% using DCE MRI.

Conclusion
Functional MRI in conjugation with conventional MRI plays a key role in the ovarian lesion detection, characterization, and staging. Functional MRI is currently being evaluated as possible predictive and prognostic biomarkers in ovarian lesions.
Humanistic burden and economic impact of chronic kidney disease: a systematic literature review
Authors: Freeman Caroline , Giles Lucia , Field Polly , Sörstadius Elisabeth , van Haalen Heleen 
Published: December 2019

Background: Chronic kidney disease (CKD) is increasing in prevalence worldwide. Progression of CKD to end-stage renal disease (ESRD) can result in the requirement for renal replacement therapy, which incurs considerable healthcare costs and imposes restrictions on patients’ daily living. This systematic review was conducted to inform understanding of the humanistic and economic burden of CKD by collecting quality of life (QoL), symptom burden, and cost and resource use data, with a focus on the impact of disease progression. Methods: Embase, MEDLINE, the Cochrane Library, and conference proceedings were searched in May 2017 according to predefined inclusion criteria. Data were extracted for full publications reporting either QoL or symptom burden (published 2007–2017; reporting data from ≥ 100 patients) or costs and resource use (published 2012–2017). Relevant QoL studies were those that used the 6-dimension or 8-, 12-, or 36-item Short-Form Health Surveys, 5-dimension EuroQol questionnaire, Healthy Days/Health-Related Quality of Life questionnaire, or Kidney Disease Quality of Life Questionnaire. Results: Data were extracted from 95 studies reporting QoL data, 47 studies reporting cost and resource use data, and eight studies reporting descriptions of symptoms; 12 studies (seven QoL; five costs/resource use) reported data for patients with and without CKD, and 15 studies (seven QoL; eight costs/resource use) reported data by disease stage. Patients with CKD, including those with ESRD, had worse QoL than those with normal kidney function, and incurred higher healthcare costs. Disease progression was associated with cost increases, particularly for later stages and in patients receiving dialysis. Increasing CKD severity was also associated with reductions in QoL, although not all studies identified showed a consistent decrease with increasing disease stage. Conclusions: The presence of CKD and CKD progression are associated with reductions in patients’ QoL and increased economic impact. This may be mitigated by interventions that slow progression.