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Frank Emmert-Streib

Frank Emmert-Streib

D-Index & Metrics

Computer Science

D-Index
47
Citations
8784
World Ranking
6502
National Ranking
45

Overview

Frank Emmert-Streib is affiliated with Tampere University in Finland. Their research primarily spans the field of computer science, with a focus on several subfields including artificial intelligence, molecular biology, computational theory and mathematics, geometry and topology, and statistical and nonlinear physics.

Their work addresses a diverse range of topics, including:

  • Graph theory and applications
  • Topic modeling
  • Complex network analysis techniques
  • Bioinformatics and genomic networks
  • Computational drug discovery methods
  • Explainable artificial intelligence (XAI)
  • Machine learning in healthcare

Frank Emmert-Streib has contributed multiple papers to peer-reviewed journals. Some of the recent notable publications include:

  • "An Introductory Review of Deep Learning for Prediction Models With Big Data" (2020), published in Frontiers in Artificial Intelligence
  • "Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence" (2021), published in Information Fusion
  • "Named Entity Recognition and Relation Detection for Biomedical Information Extraction" (2020), published in Frontiers in Cell and Developmental Biology
  • "Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges" (2024), published in AI
  • "Robustness of differential gene expression analysis of RNA-seq" (2021), published in Computational and Structural Biotechnology Journal

Their frequent coauthors, some with significant collaborative counts, include:

  • Matthias Dehmer
  • Olli Yli-Harja
  • Shailesh Tripathi
  • Modjtaba Ghorbani
  • Zhen Yang

Frank Emmert-Streib has published extensively in several scientific journals, demonstrating a recurring presence in these venues:

  • Frontiers in Artificial Intelligence
  • Information Sciences
  • Applied Mathematics and Computation
  • IEEE Access
  • Symmetry

Beyond journal articles, their contributions include book publications. Notably, they have authored a book titled Cancer systems biology published by Frontiers Media in 2022.

Best Publications

  • An Introductory Review of Deep Learning for Prediction Models With Big Data

    Frank Emmert-Streib;Zhen Yang;Han Feng;Shailesh Tripathi

  • Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    Frank Emmert-Streib;Matthias Dehmer;Benjamin Haibe-Kains

  • A review of connectivity map and computational approaches in pharmacogenomics.

    Aliyu Musa;Laleh Soltan Ghoraie;Shu-Dong Zhang;Galina V. Glazko

  • Fifty years of graph matching, network alignment and network comparison

    Frank Emmert-Streib;Matthias Dehmer;Yongtang Shi

  • Inferring the conservative causal core of gene regulatory networks

    Gökmen Altay;Frank Emmert-Streib

  • Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

    Andreas Holzinger;Andreas Holzinger;Matthias Dehmer;Frank Emmert-Streib;Rita Cucchiara

  • High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

    Frank Emmert-Streib;Matthias Dehmer

  • Harnessing naturally randomized transcription to infer regulatory relationships among genes.

    Lin S Chen;Frank Emmert-Streib;John D Storey

  • Named Entity Recognition and Relation Detection for Biomedical Information Extraction

    Nadeesha Perera;Matthias Dehmer;Frank Emmert-Streib

  • Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data

    Frank Emmert-Streib;Galina V. Glazko;Gökmen Altay;Gökmen Altay;Ricardo de Matos Simoes

  • Networks for systems biology: conceptual connection of data and function

    Frank Emmert-Streib;M. Dehmer

  • Bagging Statistical Network Inference from Large-Scale Gene Expression Data

    Ricardo de Matos Simoes;Frank Emmert-Streib

  • Unite and conquer

    Galina V. Glazko;Frank Emmert-Streib

  • Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases

    Frank Emmert-Streib;Galina V. Glazko

  • Revealing differences in gene network inference algorithms on the network level by ensemble methods

    Gökmen Altay;Frank Emmert-Streib

  • On Entropy-Based Molecular Descriptors: Statistical Analysis of Real and Synthetic Chemical Structures

    Matthias Dehmer;Kurt Varmuza;Stephan Borgert;Frank Emmert-Streib

  • Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

    Yasir Rahmatallah;Frank Emmert-Streib;Galina V. Glazko

  • Analysis of Microarray Data: A Network-Based Approach

    Frank Emmert-Streib;Matthias Dehmer

  • Analysis of Complex Networks: From Biology to Linguistics

    Matthias Dehmer;Frank Emmert-Streib

  • A review of connectivity map and computational approaches in pharmacogenomics

    Unknown

  • The chronic fatigue syndrome: a comparative pathway analysis.

    Frank Emmert-Streib

  • Information Theory and Statistical Learning

    Frank Emmert-Streib;Matthias Dehmer

Frequent Co-Authors

Matthias Dehmer
Matthias Dehmer University of Miami
Zengqiang Chen
Zengqiang Chen Nankai University
Benjamin Haibe-Kains
Benjamin Haibe-Kains Princess Margaret Cancer Centre
Andreas Holzinger
Andreas Holzinger BOKU University
Kevin M. Prise
Kevin M. Prise Queen's University Belfast
Max Mühlhäuser
Max Mühlhäuser Technical University of Darmstadt
John-Dylan Haynes
John-Dylan Haynes Charité - University Medicine Berlin
Gianluca Bontempi
Gianluca Bontempi Université Libre de Bruxelles
John Quackenbush
John Quackenbush Harvard University
Arcady Mushegian
Arcady Mushegian National Science Foundation

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