D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Biology and Biochemistry D-index 79 Citations 42,897 200 World Ranking 1838 National Ranking 1041

Overview

What is he best known for?

The fields of study he is best known for:

  • Gene
  • Bacteria
  • DNA

Eric J. Alm spends much of his time researching Genome, Microbiome, Genetics, Ecology and Human microbiome. The study incorporates disciplines such as Archaea, Phylogenetic tree and Metagenomics in addition to Genome. His Human Microbiome Project study in the realm of Microbiome connects with subjects such as Correlation.

His Human Microbiome Project research incorporates themes from Evolutionary biology and Earth Microbiome Project. His research in the fields of Ecology and Species diversity overlaps with other disciplines such as Sampling. The concepts of his Human microbiome study are interwoven with issues in Host, Computational biology, Gastrointestinal Microbiome and Bacteroidetes.

His most cited work include:

  • Structure, function and diversity of the healthy human microbiome (6128 citations)
  • Structure, function and diversity of the healthy human microbiome (6128 citations)
  • Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (2043 citations)

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

The scientist’s investigation covers issues in Microbiome, Genetics, Computational biology, Ecology and Gene. His research in Microbiome is mostly concerned with Human microbiome. His biological study focuses on Genome.

His work investigates the relationship between Computational biology and topics such as Metagenomics that intersect with problems in Mobile genetic elements. His Ecology research is mostly focused on the topic Ecology. His studies deal with areas such as Biological dispersal, Human Microbiome Project and Genomics as well as Evolutionary biology.

He most often published in these fields:

  • Microbiome (39.51%)
  • Genetics (20.68%)
  • Computational biology (18.52%)

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

  • Microbiome (39.51%)
  • Computational biology (18.52%)
  • Metagenomics (16.36%)

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

Eric J. Alm mainly investigates Microbiome, Computational biology, Metagenomics, Fecal bacteriotherapy and Evolutionary biology. His Microbiome research focuses on Human microbiome in particular. His Computational biology research integrates issues from Covariate, Horizontal gene transfer, False discovery rate and Benchmark.

Eric J. Alm combines subjects such as Ecology, Gene expression, Antibiotic resistance and Prophage with his study of Metagenomics. His Fecal bacteriotherapy research incorporates elements of Anaerobic exercise, Clinical trial and Intensive care medicine. His Evolutionary biology study incorporates themes from Adaptation, Host and Phylogenetics.

Between 2017 and 2021, his most popular works were:

  • Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (2043 citations)
  • QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science (385 citations)
  • SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases (194 citations)

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

  • Gene
  • Bacteria
  • DNA

Eric J. Alm mostly deals with Microbiome, Metagenomics, Human microbiome, Immunology and Gut flora. His Microbiome research includes themes of DNA microarray, Pooling data and Physiology. The various areas that he examines in his Metagenomics study include Biodiversity, Gene expression, Regulatory sequence and Synthetic gene.

His studies in Human microbiome integrate themes in fields like Evolutionary biology, Endospore formation and Transmission. His Evolutionary biology research includes elements of Adaptation and Genomics. His biological study spans a wide range of topics, including Case-control study and Inflammatory bowel disease.

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.

Best Publications

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

.
Nature Biotechnology (2019)

6388 Citations

Structure, function and diversity of the healthy human microbiome

Curtis Huttenhower;Curtis Huttenhower;Dirk Gevers;Rob Knight;Rob Knight;Sahar Abubucker.
Nature (2012)

6004 Citations

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

.
Nature Biotechnology (2019)

3419 Citations

A framework for human microbiome research

Barbara A. Methé;Karen E. Nelson;Mihai Pop;Heather H. Creasy.
Nature (2012)

1614 Citations

Inferring Correlation Networks from Genomic Survey Data

Jonathan Friedman;Eric J. Alm;Eric J. Alm.
PLOS Computational Biology (2012)

1091 Citations

Ecology drives a global network of gene exchange connecting the human microbiome

Chris S. Smillie;Mark B. Smith;Jonathan Friedman;Otto X. Cordero.
Nature (2011)

707 Citations

Host lifestyle affects human microbiota on daily timescales.

Lawrence A David;Lawrence A David;Arne C Materna;Jonathan Friedman;Maria I Campos-Baptista.
Genome Biology (2014)

624 Citations

QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Evan Bolyen;Jai Ram Rideout;Matthew R Dillon;Nicholas A Bokulich.
(2018)

586 Citations

MicrobesOnline: an integrated portal for comparative and functional genomics

Paramvir S. Dehal;Marcin P. Joachimiak;Morgan N. Price;John T. Bates.
Nucleic Acids Research (2010)

568 Citations

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

.
The ISME Journal (2016)

520 Citations

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