H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Biology and Biochemistry H-index 65 Citations 17,080 199 World Ranking 3947 National Ranking 297

Overview

What is he best known for?

The fields of study he is best known for:

  • Gene
  • Artificial intelligence
  • Statistics

Fabian J. Theis mostly deals with Genetics, Computational biology, Artificial intelligence, Bioinformatics and Cell biology. His Genetics research is multidisciplinary, relying on both Metabolome, Metabolomics and Gout. His Computational biology study combines topics from a wide range of disciplines, such as Human cell, Gene expression profiling, RNA, Transcriptome and Regulation of gene expression.

The study incorporates disciplines such as DNA microarray, Gene expression, microRNA and Single-cell analysis in addition to Regulation of gene expression. His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence. His biological study spans a wide range of topics, including Molecular biology and Cell cycle.

His most cited work include:

  • SCANPY : large-scale single-cell gene expression data analysis (1041 citations)
  • The Human Cell Atlas (853 citations)
  • Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (812 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Independent component analysis, Algorithm, Computational biology and Blind signal separation are his primary areas of study. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Computer vision and Pattern recognition. His Independent component analysis research includes themes of Speech recognition, Source separation, Matrix decomposition, Signal processing and Principal component analysis.

As a part of the same scientific study, he usually deals with the Computational biology, concentrating on Cell and frequently concerns with Cell biology. His study in Blind signal separation is interdisciplinary in nature, drawing from both Matrix and Non-negative matrix factorization. Regulation of gene expression and Gene regulatory network are the subjects of his Genetics studies.

He most often published in these fields:

  • Artificial intelligence (16.89%)
  • Independent component analysis (15.23%)
  • Algorithm (14.57%)

What were the highlights of his more recent work (between 2017-2021)?

  • Computational biology (14.07%)
  • Cell (6.62%)
  • Transcriptome (5.30%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Computational biology, Cell, Transcriptome, Cell biology and Artificial intelligence. Fabian J. Theis has included themes like RNA-Seq, Gene expression, RNA, Proteomics and Genomics in his Computational biology study. His research in Cell is mostly focused on Cell type.

The concepts of his Transcriptome study are interwoven with issues in Epigenetics and DNA methylation. His work carried out in the field of Cell biology brings together such families of science as Lineage, Enteroendocrine cell and Programmed cell death. His Artificial intelligence research includes elements of Machine learning and Pattern recognition.

Between 2017 and 2021, his most popular works were:

  • SCANPY : large-scale single-cell gene expression data analysis (1041 citations)
  • Current best practices in single-cell RNA-seq analysis: a tutorial. (374 citations)
  • Single-cell RNA-seq denoising using a deep count autoencoder (272 citations)

In his most recent research, the most cited papers focused on:

  • Gene
  • Artificial intelligence
  • Statistics

Fabian J. Theis mainly focuses on Computational biology, Cell, Genomics, Transcriptome and Artificial intelligence. Fabian J. Theis has researched Computational biology in several fields, including Snapshot, RNA, Proteomics, Gene and Computational model. His studies deal with areas such as Lung and Cell biology as well as Cell.

The various areas that Fabian J. Theis examines in his Transcriptome study include Cancer research and Immune system. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition. Fabian J. Theis focuses mostly in the field of Cell type, narrowing it down to matters related to Gene expression profiling and, in some cases, Preprocessor and Epigenetics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

SCANPY : large-scale single-cell gene expression data analysis

F. Alexander Wolf;Philipp Angerer;Fabian J. Theis.
Genome Biology (2018)

1041 Citations

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Florian Buettner;Kedar N Natarajan;Kedar N Natarajan;F Paolo Casale;Valentina Proserpio;Valentina Proserpio.
Nature Biotechnology (2015)

874 Citations

An atlas of genetic influences on human blood metabolites

So-Youn Shin;Eric B Fauman;Ann-Kristin Petersen;Jan Krumsiek.
Nature Genetics (2014)

740 Citations

Diffusion pseudotime robustly reconstructs lineage branching

Laleh Haghverdi;Maren Büttner;F Alexander Wolf;Florian Buettner.
Nature Methods (2016)

597 Citations

The Human Cell Atlas

Aviv Regev;Aviv Regev;Aviv Regev;Sarah A Teichmann;Sarah A Teichmann;Sarah A Teichmann;Eric S Lander;Eric S Lander;Eric S Lander;Ido Amit.
eLife (2017)

595 Citations

Genome-wide association analyses identify 18 new loci associated with serum urate concentrations

Anna Köttgen;Anna Köttgen;Eva Albrecht;Alexander Teumer;Veronique Vitart.
Nature Genetics (2013)

568 Citations

Diffusion maps for high-dimensional single-cell analysis of differentiation data.

Laleh Haghverdi;Florian Buettner;Fabian J. Theis.
Bioinformatics (2015)

410 Citations

Sparse component analysis and blind source separation of underdetermined mixtures

P. Georgiev;F. Theis;A. Cichocki.
IEEE Transactions on Neural Networks (2005)

403 Citations

Current best practices in single-cell RNA-seq analysis: a tutorial.

Malte D Luecken;Fabian J Theis.
Molecular Systems Biology (2019)

390 Citations

Hypergraphs and cellular networks.

Steffen Klamt;Utz-Uwe Haus;Fabian J. Theis.
PLOS Computational Biology (2009)

385 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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