World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
7655
World Ranking
12906
National Ranking
116

Overview

Juho Rousu is affiliated with Aalto University in Finland. Their research integrates multiple disciplines, focusing largely on biochemistry, genetics, molecular biology, and computer science. They have published extensively on topics related to computational drug discovery, machine learning in materials science, and metabolomics studies.

Their main fields of study include:

  • Biochemistry, Genetics and Molecular Biology
  • Computer Science

Rousu's subfields of study encompass:

  • Molecular Biology
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Materials Chemistry
  • Spectroscopy

Their research topics cover:

  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Metabolomics and Mass Spectrometry Studies
  • Bioinformatics and Genomic Networks
  • Analytical Chemistry and Chromatography
  • Mass Spectrometry Techniques and Applications
  • Microbial Natural Products and Biosynthesis

Frequent coauthors include:

  • Sándor Szedmák (18 publications)
  • Tero Aittokallio (14 publications)
  • Tianduanyi Wang (11 publications)
  • Anna Cichońska (8 publications)
  • Tapio Pahikkala (8 publications)

Rousu has published papers in several prominent venues. The most frequent publication venues are:

  • bioRxiv (Cold Spring Harbor Laboratory) - 11 publications
  • arXiv (Cornell University) - 9 publications
  • Bioinformatics - 6 publications
  • Nature Communications - 2 publications
  • PLoS Computational Biology - 2 publications

Recent publications include the following:

  • Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra, 2020, Nature Biotechnology
  • Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects, 2020, Nature Communications
  • Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions, 2021, PLoS Computational Biology
  • Systematic review of computational methods for drug combination prediction, 2022, Computational and Structural Biotechnology Journal
  • Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods, 2020, Applied Microbiology and Biotechnology

Best Publications

  • SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information.

    Kai Dührkop;Markus Fleischauer;Marcus Ludwig;Alexander A. Aksenov

  • Searching molecular structure databases with tandem mass spectra using CSI:FingerID.

    Kai Dührkop;Huibin Shen;Marvin Meusel;Juho Rousu

  • Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

    Kai Dührkop;Louis Felix Nothias;Markus Fleischauer;Raphael Reher

  • Kernel-Based Learning of Hierarchical Multilabel Classification Models

    Juho Rousu;Craig Saunders;Sandor Szedmak;John Shawe-Taylor

  • General and Efficient Multisplitting of Numerical Attributes

    Tapio Elomaa;Juho Rousu

  • Metabolite identification and molecular fingerprint prediction through machine learning

    Markus Heinonen;Huibin Shen;Nicola Zamboni;Juho Rousu

  • Critical Assessment of Small Molecule Identification 2016: automated methods

    Emma L. Schymanski;Christoph Ruttkies;Martin Krauss;Céline Brouard;Céline Brouard

  • FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data.

    Markus Heinonen;Ari Rantanen;Ari Rantanen;Taneli Mielikäinen;Juha Kokkonen

  • metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

    Anna Cichonska;Juho Rousu;Pekka Marttinen;Antti J. Kangas

  • Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.

    Heli Julkunen;Anna Cichonska;Anna Cichonska;Anna Cichonska;Prson Gautam;Sandor Szedmak

  • Metabolite identification through multiple kernel learning on fragmentation trees.

    Huibin Shen;Kai Dührkop;Sebastian Böcker;Juho Rousu

  • Learning hierarchical multi-category text classification models

    Juho Rousu;Craig Saunders;Sandor Szedmak;John Shawe-Taylor

  • Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

    Anna Cichonska;Balaguru Ravikumar;Elina Parri;Sanna Timonen

  • Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

    Esa Pitkänen;Paula Jouhten;Jian Hou;Muhammad Fahad Syed

  • Fast metabolite identification with Input Output Kernel Regression

    Céline Brouard;Huibin Shen;Kai Dührkop;Florence d'Alché-Buc

  • Learning with multiple pairwise kernels for drug bioactivity prediction

    Anna Cichonska;Anna Cichonska;Tapio Pahikkala;Sandor Szedmak;Heli Julkunen

  • Inferring branching pathways in genome-scale metabolic networks

    Esa Pitkänen;Paula Jouhten;Juho Rousu

  • A Tutorial on Canonical Correlation Methods

    Viivi Uurtio;João M. Monteiro;Jaz Kandola;John Shawe-Taylor

  • Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates

    Tapio Elomaa;Juho Rousu

  • Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.

    Grímur Hjörleifsson Eldjárn;Andrew Ramsay;Justin J J van der Hooft;Katherine R Duncan

  • Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

    Markus Heinonen;Henrik Mannerström;Juho Rousu;Samuel Kaski

  • Liquid-chromatography retention order prediction for metabolite identification.

    Eric Bach;Sandor Szedmak;Céline Brouard;Sebastian Böcker

Frequent Co-Authors

Esko Ukkonen
Esko Ukkonen University of Helsinki
Samuel Kaski
Samuel Kaski Aalto University
John Shawe-Taylor
John Shawe-Taylor University College London
Sebastian Böcker
Sebastian Böcker Friedrich Schiller University Jena
Tero Aittokallio
Tero Aittokallio University of Helsinki
Harri Lähdesmäki
Harri Lähdesmäki Aalto University
Tapio Pahikkala
Tapio Pahikkala University of Turku
Liisa Holm
Liisa Holm University of Helsinki
Samuli Ripatti
Samuli Ripatti University of Helsinki
Nicola Zamboni
Nicola Zamboni ETH Zurich

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