bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expression based biomarkers and models to classify early and late stage samples of Papillary Thyroid Carcinoma
Authors: Bhalla Sherry , Kaur Harpreet , Kaur Rishemjit , Sharma Suresh , Raghava Gajendra P. S. 
Published: August 2018

AbstractIn this study, we describe the key transcripts and machine learning models developed for classifying the early and late stage samples of Papillary Thyroid Cancer (PTC), using transcripts’ expression data from The Cancer Genome Atlas (TCGA). First, we rank all the transcripts on the basis of area under receiver operating characteristic curve, (AUROC) value to discriminate the early and late stage, based on an expression threshold. With the expression of a single transcript DCN, we can classify the stage samples with a 68.5% accuracy and AUROC of 0.66. Then we implemented various combination of multiple gene panels, selected using various gold standard feature selection techniques. The model based on the expression of 36 multiple transcripts (protein coding and non-coding) selected using SVC-L1 achieves the maximum accuracy of 74.51% with AUROC of 0.75 on independent validation dataset with balanced sensitivity and specificity. Further, these signatures also performed well on external microarray data obtained from GEO, predicting nearly 70% (12 samples out of 17 samples) early stage samples correctly. Further, multiclass model, classifying the normal, early and late stage samples achieves the accuracy of 75.43% with AUROC of 0.80 on independent validation dataset. With correlation analysis, we found that transcripts with maximum change in correlation of their expression in both the stages are significantly enriched in neuroactive ligand receptor interaction pathway. We also propose a panel of five protein coding transcripts, which on the basis of their expression, can segregate cancer and normal samples with 97.32% accuracy and AUROC of 0.99 on independent validation dataset. All the models and dataset used in this study are available from the web server CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).
New Statistical Methods for Constructing Robust Differential Correlation Networks
Authors: Yu Danyang , Zhang Zeyu , Glass Kimberly , Su Jessica , DeMeo Dawn L. , Tantisira Kelan , Weiss Scott T. , Qiu Weiliang 
Published: August 2018

AbstractThe interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fisher’s Z-transformation test is usually used. However, the Fisher’s Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fisher’s Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.
Therapeutic potential of TAF1 bromodomains for cancer treatment
Authors: Garcia-Carpizo Veronica , Ruiz-Llorente Sergio , Sarmentero Jacinto , Barrero Maria J. 
Published: August 2018

ABSTRACTThe discovery of the antiproliferative effects of BRD4 bromodomain inhibitors prompted us to investigate additional bromodomains that might be involved in supporting cellular proliferation. TAF1 is a general transcription factor with two bromodomains that is likely to play important roles in cell viability by supporting transcription. Our work shows that knock down of TAF1 caused antiproliferative effects in several cancer cell lines. Using CRISPR-Cas9 editing techniques we demonstrate that the bromodomains of TAF1 are essential to maintain proliferation of K562 and H322 cells. BAY-299, the best TAF1 bromodomain inhibitor developed so far, also showed antiproliferative effects. BAY-299 caused discrete transcriptional changes that were likely on target but did not correlate with strong effects in cell cycle distribution suggesting that these effects might be, at least in part, mediated by another target. Compared to the TAF1 knock down, BAY-299 specifically downregulated the expression of genes with high levels of TAF1 and histone acetylation at their promoters, suggesting that targeting the bromodomains has distinct transcriptional effects than targeting the whole protein. This might help to provide a therapeutic window to target specifically cancer cells with high levels of histone acetylation at proliferation-related genes.
Low-cost and clinically applicable copy number profiling using repeat DNA
Authors: Abujudeh S , Zeki SS , van Lanschot MCV , Pusung M , Weaver JMJ , Li X , Noorani A , Metz AJ , Bornschein J , Bower L , Miremadi A , Fitzgerald RC , Morrissey ER , Lynch AG ,   Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)Consortium, Fitzgerald Rebecca C. , Noorani Ayesha , Edwards Paul A.W. , Grehan Nicola , Nutzinger Barbara , Hughes Caitriona , Fidziukiewicz Elwira , Bornschein Jan , MacRae Shona , Crawte Jason , Northrop Alex , Contino Gianmarco , Li Xiaodun , la Rue Rachel de , O’Donovan Maria , Miremadi Ahmad , Malhotra Shalini , Tripathi Monika , Tavaré Simon , Lynch Andy G. , Eldridge Matthew , Secrier Maria , Bower Lawrence , Devonshire Ginny , Perner Juliane , Jammula Sriganesh , Davies Jim , Crichton Charles , Carroll Nick , Safranek Peter , Hindmarsh Andrew , Sujendran Vijayendran , Hayes Stephen J. , Ang Yeng , Preston Shaun R. , Oakes Sarah , Bagwan Izhar , Save Vicki , Skipworth Richard J.E. , Hupp Ted R. , O’Neill J. Robert , Tucker Olga , Beggs Andrew , Taniere Philippe , Puig Sonia , Underwood Timothy J. , Noble Fergus , Owsley Jack , Barr Hugh , Shepherd Neil , Old Oliver , Lagergren Jesper , Gossage James , Davies Andrew , Chang Fuju , Zylstra Janine , Mahadeva Ula , Goh Vicky , Ciccarelli Francesca D. , Sanders Grant , Berrisford Richard , Harden Catherine , Lewis Mike , Cheong Ed , Kumar Bhaskar , Parsons Simon L , Soomro Irshad , Kaye Philip , Saunders John , Lovat Laurence , Haidry Rehan , Igali Laszlo , Scott Michael , Sothi Sharmila , Suortamo Sari , Lishman Suzy , Hanna George B. , Peters Christopher J. , Grabowska Anna , Turkington Richard 
Published: August 2018

AbstractLarge-scale cancer genome studies suggest that tumors are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Due to the low-cost, the clinical use of genomics assays is biased towards targeted gene panels, which identify SNVs. There is a need for a comparably low-cost and simple assay for high-resolution SCNA profiling. Here we present our method, conliga, which infers SCNA profiles from a low-cost and simple assay.
rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete
Authors: Mirabello Claudio , Wallner Björn 
Published: August 2018

AbstractIn the last few decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and about their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed in the hope of taking advantage of these new architectures. On the other hand, most methods are still based on heavy pre-processing of the input data, as well as the extraction and integration of multiple hand-picked, manually designed features. Since Multiple Sequence Alignments (MSA) are almost always the main source of information in de novo prediction methods, it should be possible to develop Deep Networks to automatically refine the data and extract useful features from it. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering sequence profiles and other pre-calculated features obsolete. We developed rawMSA in three different flavors to predict secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on a par with the top ranked CASP12 methods in the inter-residue contact map prediction category. We believe that rawMSA represents a promising, more powerful approach to protein structure prediction that could replace older methods based on protein profiles in the coming years.Availabilitydatasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa
Biomarker-based assessment of the muscle maintenance and energy status of anurans from an extremely seasonal semi-arid environment, the Brazillian Caatinga
Authors: Madelaire Carla Bonetti , Gomes Fernando Ribeiro , Sokolova Inna 
Published: August 2018

AbstractStrongly seasonal environments pose challenges for performance and survival of animals, especially when resource abundance seasonally fluctuates. We investigated the seasonal variation of expression of key metabolic biomarkers in the muscles of three species of anurans from the drastically seasonal Brazilian semi-arid area, Caatinga. The three studied anuran species (Rhinella jimi, R. granulosaandPleurodema diplolister) differ in their seasonal activity patterns. We examined the expression of proteins regulating energy turnover (AMP-activated protein kinase [AMPK] and protein kinase B [AKT]), protein synthesis and homeostasis (total and phosphorylated eukaryotic initiation factor 2α [eIF2α and p-eIF2α] and chaperone proteins [HSP 60, 70, and 90]) in muscles related to reproduction and locomotion. Cytochrome c oxidase (COX) activity was also assessed as an index of the muscle aerobic capacity. Our results point to the importance of metabolic regulators mediating the muscular function during the drastic seasonal variation. The toads that remain active during the drought appear to maintain muscles through more energy extensive pathways including elevated protein synthesis, while the aestivating species employs energy conservation strategy suppressing protein synthesis, decreasing chaperone expression and increasing expression of AMPK. All three studied species activate cell survival pathways during the drought likely to prevent muscle atrophy, and maintain the muscle capacity throughout the year, despite the resource limitation. These strategies are important considering the unpredictability of the reproductive event and high demand on muscular activity during the reproductive season in these amphibians.Summary statementWe studied seasonal variation of key metabolic regulators in the muscles of anurans that experience drastic variation in environmental conditions and differ in the seasonal activity patterns.
Perivascular space fluid contributes to diffusion tensor imaging changes in white matter
Authors: Sepehrband Farshid , Cabeen Ryan P , Choupan Jeiran , Barisano Giuseppe , Law Meng , Toga Arthur W ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: August 2018

AbstractDiffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.HighlightsPerivascular space (PVS) fluid significantly contributes to diffusion tensor imaging metricsIncreased PVS fluid results in increased mean diffusivity and decreased fractional anisotropyPVS contribution to diffusion signal is overlooked and demands further investigation
A 7-member SNP Assay on the iPlex MassARRAY Platform Provides a Rapid and Affordable Alternative to Typing Major AfricanStaphylococcus aureusTypes
Authors: Nyasinga Justin , Kyany’a Cecilia , Okoth Raphael , Oundo Valerie , Matano Daniel , Wacira Simon , Sang Willie , Musembi Susan , Musila Lillian 
Published: August 2018

AbstractBackgroundData on the clonal distribution ofStaphylococcus aureusin Africa is scanty, partly due to high costs and long turnaround times imposed by conventional genotyping methods such asspaand multilocus sequence typing (MLST) warranting the need for alternative typing approaches. This study applied and evaluated the accuracy, cost and time of using iPlex massARRAY genotyping method on Kenyan staphylococcal isolates.MethodsFifty four clinicalS. aureusisolates from three counties were characterized using iPlex massARRAY,spaand MLST typing methods. Ten Single Nucleotide Polymorphisms (SNPs) from theS. aureusMLST database were assessed by iPlex massARRAY.ResultsThe iPlex massARRAY assay grouped the isolates into 14 SNP genotypes with 9/10 SNPs interrogated showing high detection rates (average 89%). spaand MLST typing revealed 22spatypes and 21 STs that displayed unique regional distribution.spatype t355 (ST152) was the dominant type and t2029 and t037 (ST 241) were observed among MRSA strains. MassARRAY showed 83% and 82% accuracy againstspaand MLST typing respectively in isolate classification. Moreover, massARRAY identified all MRSA strains and a novelspatype. MassARRAY had reduced turnaround time (<12 hrs) compared tospa(3 days) and MLST (20 days) typing. The iPlex massARRAY cost approximately 18 USD compared tospa(30 USD) and MLST (126 USD) typing based on consumable costs/isolate.ConclusionUpon validation with a larger collection of isolates, iPlex massARRAY could provide a faster, more affordable and fairly accurate method of resolving AfricanS.aureusisolates especially in large surveillance studies.
The Evolution of Phenotypic Plasticity when Environments Fluctuate in Time and Space
Authors: King Jessica G. , Hadfield Jarrod D. 
Published: August 2018

AbstractMost studies have explored the evolution of plasticity when the environment, and there-fore the optimal trait, varies in time or space. When the environment varies in time and space we show that genetic adaptation to temporal fluctuations depends on the between-generation autocorrelation in the environment in exactly the same way that genetic adaptation to spatial fluctuations depends on the probability of philopatry. This is because both measure the correlation in parent-offspring environments and therefore the effectiveness of a genetic response to selection. If the capacity to genetically respond to selection is stronger in one dimension (e.g. space) then plasticity mainly evolves in response to fluctuations in the other dimension (e.g. time). If the relationship between the environments of development and selection are the same in time and space then the evolved plastic response to temporal fluctuations is useful in a spatial context and genetic differentiation in space is reduced. However, if the relationship between the environments of development and selection are different then the optimal level of plasticity is different in the two dimensions. In this case the plastic response that evolves to cope with temporal fluctuations may actually be maladaptive in space. This can result in the evolution of hyperplasticity or negative plasticity, the effects of which are mitigated by spatial genetic differentiation. However, genetic differentiation acts in opposition to plasticity resulting in counter-gradient variation. These results highlight the difficulty of making space-for-time substitutions in empirical work but identify the key parameters which need to be measured in order to test whether space-for-time substitutions are likely to be valid.
Independent component analysis provides clinically relevant insights into the biology of melanoma patients
Authors: Nazarov Petr V. , Wienecke-Baldacchino Anke K. , Zinovyev Andrei , Czerwińska Urszula , Muller Arnaud , Nashan Dorothée , Dittmar Gunnar , Azuaje Francisco , Kreis Stephanie 
Published: August 2018

ABSTRACTThe integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.
Clustering of the structures of protein kinase activation loops: A new nomenclature for active and inactive kinase structures
Authors: Modi Vivek , Dunbrack Roland L. 
Published: August 2018

AbstractTargeting protein kinases is an important strategy for intervention in cancer. Inhibitors are directed at the active conformation or a variety of inactive conformations. While attempts have been made to classify these conformations, a structurally rigorous catalogue of states has not been achieved. The kinase activation loop is crucial for catalysis and begins with the conserved DFGmotif (Asp-Phe-Gly). This motif is observed in two major classes of conformations, DFGin - a set of active and inactive conformations where the Phe residue is in contact with the C-helix of the N-terminal lobe, and DFGout - an inactive form where Phe occupies the ATP site exposing the C-helix pocket. We have developed a clustering of kinase conformations based on the location of the Phe side chain (DFGin, DFGout, and DFGinter or intermediate) and the backbone dihedral angles of the sequence X-D-F, where X is the residue before the DFGmotif, and the DFG-Phe side-chain rotamer, utilizing a density-based clustering algorithm. We have identified 8 distinct conformations and labeled them based on their Ramachandran regions (A=alpha, B=beta, L=left) and the Phe rotamer (minus, plus, trans). Our clustering divides the DFGin group into six clusters including ‘BLAminus,’ which contains active structures, and two common inactive forms, ‘BLBplus’ and ‘ABAminus.’ DFGout structures are predominantly in the ‘BBAminus’ conformation, which is essentially required for binding Type II inhibitors. The inactive conformations have specific features that make them unable to bind ATP, magnesium ion, and/or substrates. Our structurally intuitive nomenclature will aid in understanding the conformational dynamics of these proteins and structure-based development of kinase drugs.Significance statementProtein kinases play important roles in signaling pathways and are widely studied as drug targets. Their active site exhibits remarkable structural variation as observed in the large number of available crystal structures. We have developed a clustering scheme and nomenclature to categorize and label all the observed conformations in human protein kinases. This has enabled us to clearly define the geometry of the active state and to distinguish closely related inactive states which were previously not characterized. Our classification of kinase conformations will help in better understanding the conformational dynamics of these proteins and the development of inhibitors against them.
MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease
Authors: Taroni Jaclyn N. , Grayson Peter C. , Hu Qiwen , Eddy Sean , Kretzler Matthias , Merkel Peter A. , Greene Casey S. 
Published: August 2018

SUMMARYUnsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most gene expression datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We trained a Pathway Level Information ExtractoR (PLIER) model using on a large public data compendium comprised of multiple experiments, tissues, and biological conditions. We then transferred the model to small rare disease datasets in an approach we term MultiPLIER. Models constructed from large, diverse public data i) included features that aligned well to important biological factors; ii) were more comprehensive than those constructed from individual datasets or conditions; iii) transferred to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained on specifically those datasets.
GBAT: a gene-based association method for robust trans-gene regulation detection
Authors: Liu Xuanyao , Mefford Joel A , Dahl Andrew , Subramaniam Meena , Battle Alexis , Price Alkes L , Zaitlen Noah 
Published: August 2018

AbstractIdentification of trans-eQTLs has been limited by a heavy multiple testing burden, read-mapping biases, and hidden confounders. To address these issues, we developed GBAT, a powerful gene-based method that allows robust detection of trans gene regulation. Using simulated and real data, we show that GBAT drastically increases detection of trans-gene regulation over standard trans-eQTL analyses.