104,812 research outputs found
DNA methylation analysis to differentiate reference, breed, and parent-of-origin effects in the bovine pangenome era
No full textBackground: Most DNA methylation studies have used a single reference genome with little attention paid to the bias introduced due to the reference chosen. Reference genome artifacts and genetic variation, including single nucleotide polymorphisms (SNPs) and structural variants (SVs), can lead to differences in methylation sites (CpGs) between individuals of the same species. We analyzed whole-genome bisulfite sequencing data from the fetal liver of Angus (Bos taurus taurus), Brahman (Bos taurus indicus), and reciprocally crossed samples. Using reference genomes for each breed from the Bovine Pangenome Consortium, we investigated the influence of reference genome choice on the breed and parent-of-origin effects in methylome analyses. Results: Our findings revealed that ∼75% of CpG sites were shared between Angus and Brahman, ∼5% were breed specific, and ∼20% were unresolved. We demonstrated up to ∼2% quantification bias in global methylation when an incorrect reference genome was used. Furthermore, we found that SNPs impacted CpGs 13 times more than other autosomal sites (P < 5 × 10−324) and SVs contained 1.18 times (P < 5 × 10−324) more CpGs than non-SVs. We found a poor overlap between differentially methylated regions (DMRs) and differentially expressed genes (DEGs) and suggest that DMRs may be impacting enhancers that target these DEGs. DMRs overlapped with imprinted genes, of which 1, DGAT1, which is important for fat metabolism and weight gain, was found in the breed-specific and sire-of-origin comparisons. Conclusions: This work demonstrates the need to consider reference genome effects to explore genetic and epigenetic differences accurately and identify DMRs involved in controlling certain genes.Callum MacPhillamy, Tong Chen, Stefan Hiendleder, John L. Williams, Hamid Alinejad-Rokny, and Wai Yee Lo
Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers
No full textThe use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://github.com/MLBC-lab/iACP-RF
Information Sciences Letters An International Journal Performance Assessment of Feasible Scheduling Multiprocessor Tasks Solutions by using DEA FDH method
No full textAbstract: In this paper, an attempt has been made to investigate how DEA FDH method based on linear programming can select one or more efficient scheduling solutions on multiprocessor tasks obtained by any heuristic algorithms through some feasible solutions for NP-complete problems. This article will consider the problem of scheduling multiprocessor tasks with multi–criteria, namely, minimizing total completion time (makespan) and minimizing the number of tardy tasks and shows that most efficient schedule(s) will be determined
Deep learning in spatially resolved transcriptfomics: A comprehensive technical view
No full textSpatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inf lation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.Roxana Zahedi, Reza Ghamsari, Ahmadreza Argha, Callum Macphillamy, Amin Beheshti, Roohallah Alizadehsani, Nigel H. Lovell, Mohammad Lotfollahi and Hamid Alinejad-Rokn
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A method to avoid errors associated with the analysis of hypermutated viral sequences by alignment-based methods
No full textThe human genome encodes for a family of editing enzymes known as APOBEC3 (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like3). They induce context dependent G-to-A changes, referred to as "hypermutation", in the genome of viruses such as HIV, SIV, HBV and endogenous retroviruses. Hypermutation is characterized by aligning affected sequences to a reference sequence. We show that indels (insertions/deletions) in the sequences lead to an incorrect assignment of APOBEC3 targeted and non-target sites. This can result in an incorrect identification of hypermutated sequences and erroneous biological inferences made based on hypermutation analysis
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
The construction of Karen Karnak: The multi-author-function
No full textThis thesis is situated within the comparatively recent developments of Web 2.0 and the emergence of interactive WikiMedia, and explores the mode of authorship within a Read/Write culture compared to that of a Read/Only tradition. The hypothesis of this study is that the role of the audience has become merged with the author, and as such, represents new functions and attributes, distinct from a more conventional concept of authorship, in which the roles of audience and author are more separate. Read/Write and participatory culture, as defined by this study, is focused on collaboration, and includes the influences of D.I.Y. culture, Open-Source practices and the production of text by multiple authors. Multi-authorship presents a re-thinking of several concepts which support the notion of the individual author, since the focus of multi-authorship is not on attribution and ownership of a finished text, but on the continued malleability of a text. Modes of multi-authorship, demonstrated in the use of the pseudonyms Alan Smithee and Karen Eliot, represent declarative authors whose names signify multiple origins, whilst concurrently indicating a distinct body of work. The function of these names form an important context to this study, since primary research involves the construction of an experimental mode of multi-authorship utilising WikiMedia technology and the interaction of thirty nine participants, who are invited to create a body of work under the collective pseudonym Karen Karnak. The data generated by this experiment is analysed using aspects of Michel Foucault's author-function to identify and determine power structures inherent in the WikiMedia context. The interplay of power structures, including concepts such as identity, ownership and the body of work, affect the resulting mode of authorship and contribute to the construction of Karen Karnak, suggesting further areas of research into the emerging multi-author
Contribution of Information and Communication Technology (ICT) in Country’S H-Index
Get PDFThe aim of this study is to examine the effect of Information and Communication Technology (ICT) development on country’s scientific ranking as measured by H-index. Moreover, this study applies ICT development sub-indices including ICT Use, ICT Access and ICT skill to find the distinct effect of these sub-indices on country’s H-index. To this purpose, required data for the panel of 14 Middle East countries over the period 1995 to 2009 is collected. Findings of the current study show that ICT development increases the H-index of the sample countries. The results also indicate that ICT Use and ICT Skill sub-indices positively contribute to higher H-index but the effect of ICT access on country’s H-index is not clear
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