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Tushar Bhangale

dblp:73/368 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2017
0000-0002-8233-9977ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › post-GWAS analysis
causal gene identification
0.312017
geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation · Bioinform. 2017
Bioinformatics and computational biology › statistical genetics
genetic association study
0.312017
geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation · Bioinform. 2017
Bioinformatics and computational biology › genomics › variant annotation
non-coding variant functional annotation
0.312017
geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation · Bioinform. 2017
Bioinformatics and computational biology › genomics
genome-wide association study
0.112012
GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies · Bioinform. 2012
Bioinformatics and computational biology › statistical genetics
genotype-phenotype association
0.112012
GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies · Bioinform. 2012
Bioinformatics and computational biology › biostatistics › statistical bioinformatics
statistical genomics
0.112012
GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies · Bioinform. 2012

Methods — techniques the papers use, named apart from their topics

hi-c genome conformation data · 0.3eQTL integration · 0.3r/bioconductor · 0.1netCDF · 0.1
YearPublicationVenuePosition
2017 geneAttribution: trait agnostic identification of candidate genes associated with noncoding variation
abstract
Motivation: We have developed geneAttribution, an R package that assigns candidate causal gene(s) to a risk variant identified by a genetic association study such as a GWAS. The method combines user-supplied functional annotation such as expression quantitative trait loci (eQTL) or Hi-C genome conformation data and reports the most likely candidate genes. In the absence of annotation data, geneAttribution relies on the distances between the genes and the input variant. Availability and Implementation: The package is freely available from http://www.bioconductor.org/ . A quick-start vignette is included with the package. Contact: [email protected].
Arthur Wuster, Diana Chang, Timothy W. Behrens, Tushar Bhangale
Bioinform.4
2012 GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies
abstract
Abstract Summary: GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R’s memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation. Availability and implementation: GWASTools is implemented in R and is available from Bioconductor (http://www.bioconductor.org). An extensive vignette detailing a recommended work flow is included. Contact: [email protected]
Stephanie M. Gogarten, Tushar Bhangale, Matthew P. Conomos, Cecelia A. Laurie, Caitlin P. McHugh, Ian S. Painter, Xiuwen Zheng 0001, David R. Crosslin, Thomas Lumley, Sarah C. Nelson, Ken Rice, Jess Shen, Rohit Swarnkar, Bruce S. Weir, Cathy C. Laurie
Bioinform.2
2000 An Unsupervised Scheme for Detection of Microcalcifications on Mammograms
abstract
Clusters of microcalcifications which appear like small white grains of sand on mammograms are the earliest signs of breast cancer. In this work we employ a Gabor filter bank for texture analysis of mammograms to detect microcalcifications. A subset of the Gabor filter bank with a certain central frequency and different orientations is used to obtain the Gabor-filtered images. The filtered images are then subjected to a histogram based threshold to obtain binary images. Feature vectors are computed using the binary images. A k-means clustering algorithm with a variance scaled Euclidean distance is used for segmentation of the image.
Tushar Bhangale, Uday B. Desai, Upendra Sharma
ICIP1