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Article ; Online: Urachal Anomalies During Infancy: A Case Series.

Rustogi, Deepika / Panwar, Jai Bharat / Motla, Mamta / Kumar, Karunesh

2023 Volume 60, Issue 10, Page(s) 863–865

MeSH term(s)	Infant ; Humans ; Urologic Diseases
Language	English
Publishing date	2023-09-25
Publishing country	India
Document type	Letter
ZDB-ID	402594-5
ISSN	0974-7559 ; 0019-6061
ISSN (online)	0974-7559
ISSN	0019-6061
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Article: Bioinformatics drives discovery in Biomedicine.

Gujar, Ravindra / Panwar, Bharat / Dhanda, Sandeep Kumar

Bioinformation

2020 Volume 16, Issue 1, Page(s) 13–16

Abstract: Bioinformatics has evolved from providing basic solutions, such as sequence alignment, structure predictions, and phylogenetic analysis to an independent data-driven field. The unprecedented growth of genomic technologies and the enormous data have ... ...

Abstract	Bioinformatics has evolved from providing basic solutions, such as sequence alignment, structure predictions, and phylogenetic analysis to an independent data-driven field. The unprecedented growth of genomic technologies and the enormous data have opened an avenue for bioinformaticians (Bioinformatics professionals) never been seen before in the history of mankind. The novel opportunity also requires creative solutions that need skills to deal with noisy, unstructured information to offer valuable biological insights. Currently, we are seeing only the tip of an iceberg and the future will revolve around big data sets in all forms of biological research. The emerging challenge is to unfold the hidden iceberg of data.
Language	English
Publishing date	2020-01-01
Publishing country	Singapore
Document type	Editorial
ZDB-ID	2203786-X
ISSN	0973-2063
ISSN	0973-2063
DOI	10.6026/97320630016013
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides.

Panwar, Bharat / Raghava, Gajendra P S

Genomics

2015 Volume 105, Issue 4, Page(s) 197–203

Abstract: The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting ... ...

Abstract	The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/).
MeSH term(s)	Base Sequence ; Binding Sites ; Models, Molecular ; RNA/chemistry ; RNA/metabolism ; RNA-Binding Proteins/chemistry ; RNA-Binding Proteins/metabolism ; Sequence Analysis, RNA/methods ; Software ; Support Vector Machine
Chemical Substances	RNA-Binding Proteins ; RNA (63231-63-0)
Language	English
Publishing date	2015-04
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	356334-0
ISSN	1089-8646 ; 0888-7543
ISSN (online)	1089-8646
ISSN	0888-7543
DOI	10.1016/j.ygeno.2015.01.005
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Article ; Online: miRmine: a database of human miRNA expression profiles.

Panwar, Bharat / Omenn, Gilbert S / Guan, Yuanfang

Bioinformatics (Oxford, England)

2017 Volume 33, Issue 10, Page(s) 1554–1560

Abstract: Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of ... ...

Abstract	Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly. Results: We used 304 high-quality microRNA sequencing (miRNA-seq) datasets from NCBI-SRA and calculated expression profiles for different tissues and cell-lines. In each miRNA-seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell-line. This user-friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell-line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats. Availability and implementation: http://guanlab.ccmb.med.umich.edu/mirmine. Contact: bharatpa@umich.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Databases, Genetic ; Female ; Gene Expression Regulation ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Male ; MicroRNAs/genetics ; Sequence Analysis, RNA/methods ; Transcriptome
Chemical Substances	MicroRNAs
Language	English
Publishing date	2017-01-17
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btx019
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Article ; Online: Prediction of uridine modifications in tRNA sequences.

Panwar, Bharat / Raghava, Gajendra P S

BMC bioinformatics

2014 Volume 15, Page(s) 326

Abstract: Background: In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA ... ...

Abstract	Background: In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived. Results: A three-steps strategy has been applied to develop an efficient method for the prediction of UMs. In the first step, we developed a common prediction model for all the kingdoms using a dataset from MODOMICS-2008. Support Vector Machine (SVM) based prediction models were developed and evaluated by five-fold cross-validation technique. Different approaches were applied and found that a hybrid approach of binary and structural information achieved highest Area under the curve (AUC) of 0.936. In the second step, we used newly added tRNA sequences (as independent dataset) of MODOMICS-2012 for the kingdom-wise prediction performance evaluation of previously developed (in the first step) common model and achieved performances between the AUC of 0.910 to 0.949. In the third and last step, we used different datasets from MODOMICS-2012 for the kingdom-wise individual prediction models development and achieved performances between the AUC of 0.915 to 0.987. Conclusions: The hybrid approach is efficient not only to predict kingdom-wise modifications but also to classify them into two most prominent UMs: Pseudouridine (Y) and Dihydrouridine (D). A webserver called tRNAmod (http://crdd.osdd.net/raghava/trnamod/) has been developed, which predicts UMs from both tRNA sequences and whole genome.
MeSH term(s)	Base Sequence ; Models, Genetic ; RNA Processing, Post-Transcriptional ; RNA, Transfer/chemistry ; RNA, Transfer/genetics ; RNA, Transfer/metabolism ; Support Vector Machine ; Uridine/analogs & derivatives ; Uridine/genetics ; Uridine/metabolism
Chemical Substances	RNA, Transfer (9014-25-9) ; Uridine (WHI7HQ7H85)
Language	English
Publishing date	2014-10-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/1471-2105-15-326
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: miRmine: a database of human miRNA expression profiles

Panwar, Bharat / Omenn, Gilbert S / Guan, Yuanfang

Bioinformatics. 2017 May 15, v. 33, no. 10

2017

Abstract	Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly. Results: We used 304 high-quality microRNA sequencing (miRNA-seq) datasets from NCBI-SRA and calculated expression profiles for different tissues and cell-lines. In each miRNA-seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell-line. This user-friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell-line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats. Availability and Implementation: http://guanlab.ccmb.med.umich.edu/mirmine Contact: bharatpa@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Keywords	bioinformatics ; cell lines ; data collection ; databases ; gene expression regulation ; high-throughput nucleotide sequencing ; humans ; microRNA ; non-coding RNA ; tissues
Language	English
Dates of publication	2017-0515
Size	p. 1554-1560.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btx019
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Article ; Online: Single agent subcutaneous blinatumomab for advanced acute lymphoblastic leukemia.

Jabbour, Elias / Zugmaier, Gerhard / Agrawal, Vaibhav / Martínez-Sánchez, Pilar / Rifón Roca, José J / Cassaday, Ryan D / Böll, Boris / Rijneveld, Anita / Abdul-Hay, Maher / Huguet, Françoise / Cluzeau, Thomas / Díaz, Mar Tormo / Vucinic, Vladan / González-Campos, José / Rambaldi, Alessandro / Schwartz, Stefan / Berthon, Céline / Hernández-Rivas, Jesús María / Gordon, Paul R /

Brüggemann, Monika / Hamidi, Ali / Chen, Yuqi / Wong, Hansen L / Panwar, Bharat / Katlinskaya, Yuliya / Markovic, Ana / Kantarjian, Hagop

American journal of hematology

2024 Volume 99, Issue 4, Page(s) 586–595

Abstract: Blinatumomab is a BiTE® (bispecific T-cell engager) molecule that redirects ... ...

Abstract	Blinatumomab is a BiTE® (bispecific T-cell engager) molecule that redirects CD3
MeSH term(s)	Adult ; Humans ; Remission Induction ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/drug therapy ; Antibodies, Bispecific/adverse effects ; Lymphoma, B-Cell/drug therapy ; Pathologic Complete Response ; Acute Disease ; Neoplasm, Residual ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/drug therapy ; Antineoplastic Agents/adverse effects
Chemical Substances	blinatumomab (4FR53SIF3A) ; Antibodies, Bispecific ; Antineoplastic Agents
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	196767-8
ISSN	1096-8652 ; 0361-8609
ISSN (online)	1096-8652
ISSN	0361-8609
DOI	10.1002/ajh.27227
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Article ; Online: Algorithms for modeling global and context-specific functional relationship networks.

Zhu, Fan / Panwar, Bharat / Guan, Yuanfang

Briefings in bioinformatics

2016 Volume 17, Issue 4, Page(s) 686–695

Abstract: Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the ... ...

Abstract	Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.
Language	English
Publishing date	2016-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbv065
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Article ; Online: Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data.

Kumar, Harshit / Panigrahi, Manjit / Panwar, Anuradha / Rajawat, Divya / Nayak, Sonali Sonejita / Saravanan, K A / Kaisa, Kaiho / Parida, Subhashree / Bhushan, Bharat / Dutt, Triveni

Journal of computational biology : a journal of computational molecular cell biology

2022 Volume 29, Issue 9, Page(s) 943–960

Abstract: Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele ... ...

Abstract	Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.
MeSH term(s)	Genetics, Population ; Genomics/methods ; Machine Learning ; Metagenomics ; Selection, Genetic
Language	English
Publishing date	2022-05-30
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2030900-4
ISSN	1557-8666 ; 1066-5277
ISSN (online)	1557-8666
ISSN	1066-5277
DOI	10.1089/cmb.2021.0447
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Article: Selection of breed-specific SNPs in three Indian sheep breeds using ovine 50 K array

Kumar, Harshit / Panigrahi, Manjit / Rajawat, Divya / Panwar, Anuradha / Nayak, Sonali Sonejita / Kaisa, Kaiho / Bhushan, Bharat / Dutt, Triveni

Small ruminant research. 2021 Dec., v. 205

2021

Abstract: Recently DNA-based genetic purity determination tools have gained popularity, especially, in small ruminants like sheep. The objective of the present study was to select breed-specific SNPs for the three commercially important sheep breeds of India, i.e., ...

Abstract	Recently DNA-based genetic purity determination tools have gained popularity, especially, in small ruminants like sheep. The objective of the present study was to select breed-specific SNPs for the three commercially important sheep breeds of India, i.e., Changthangi, Deccani, and Garole. Sheep genotype data (Ovine 50 K SNP array) from 344 individuals of eight sheep breeds (Garole, Deccani, Changthangi, Tibetan, Australian Merino, Dorset Horn, Rambouillet, and Irish Suffolk) were used as a reference population. Here, we used pre-selection statistics and the minor allele frequency–linkage disequilibrium (MAF-LD) method to analyze the reference population dataset. SNPs selection and breed assignment was executed with a panel of 768 markers for eight breeds near intermediate gene frequencies. We identified 95, 89, and 92 breed-specific SNPs for Changthangi, Deccani, and Garole, respectively. These informative SNPs were found to be associated with essential candidate genes associated with production and disease resistance. This methodology may be implemented in practice to identify individuals as being purebred or not.
Keywords	Merino ; Rambouillet ; alleles ; data collection ; disease resistance ; genotype ; purebreds ; research ; sheep ; single nucleotide polymorphism arrays ; statistics ; India
Language	English
Dates of publication	2021-12
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	286928-7
ISSN	0921-4488
ISSN	0921-4488
DOI	10.1016/j.smallrumres.2021.106545
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