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Tapio Pahikkala

Tapio Pahikkala

D-Index & Metrics

Computer Science

D-Index
35
Citations
6052
World Ranking
11598
National Ranking
98

Overview

Tapio Pahikkala is affiliated with the University of Turku in Finland. Their research spans primarily the domain of computer science, with a notable focus on artificial intelligence. The work also integrates various interdisciplinary subfields including molecular biology, pulmonary and respiratory medicine, computational theory and mathematics, and information systems.

The scientist's publication record includes key research papers addressing different aspects of machine learning, healthcare applications, and computational drug discovery. Selected recent papers include:

  • Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects (2020, Nature Communications)
  • GeFeS: A generalized wrapper feature selection approach for optimizing classification performance (2020, Computers in Biology and Medicine)
  • Synthetic minority oversampling of vital statistics data with generative adversarial networks (2020, Journal of the American Medical Informatics Association)
  • Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology (2020, Forestry An International Journal of Forest Research)
  • Budget-Based Classification of Parkinson's Disease From Resting State EEG (2023, IEEE Journal of Biomedical and Health Informatics)

Frequent venues for publication include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • SSRN Electronic Journal
  • Zenodo (CERN European Organization for Nuclear Research)
  • Computers in Biology and Medicine

The research network comprises frequent collaborators such as Antti Airola, Ileana Montoya Perez, Anna Cichońska, Sándor Szedmák, and Parisa Movahedi.

Main topics addressed by Tapio Pahikkala cover multiple areas within computational science and biomedical research including:

  • Privacy-Preserving Technologies in Data
  • Machine Learning in Healthcare
  • Computational Drug Discovery Methods
  • Tensor decomposition and applications
  • Protein Structure and Dynamics
  • Prostate Cancer Diagnosis and Treatment
  • Prostate Cancer Treatment and Research

The broad interdisciplinary range coupled with frequent contributions to computer science journals and conferences indicates sustained activity in advancing AI methods for healthcare and biosciences applications, leveraging statistical, computational, and data-driven approaches.

Best Publications

  • Toward more realistic drug–target interaction predictions

    Tapio Pahikkala;Antti Airola;Sami Pietilä;Sushil Shakyawar

  • Using Ant Colony System to Consolidate VMs for Green Cloud Computing

    Fahimeh Farahnakian;Adnan Ashraf;Tapio Pahikkala;Pasi Liljeberg

  • All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

    Antti Airola;Sampo Pyysalo;Jari Björne;Tapio Pahikkala

  • Extracting Complex Biological Events with Rich Graph-Based Feature Sets

    Jari Björne;Juho Heimonen;Filip Ginter;Antti Airola

  • An experimental comparison of cross-validation techniques for estimating the area under the ROC curve

    Antti Airola;Tapio Pahikkala;Willem Waegeman;Bernard De Baets

  • Energy-Aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Model

    Fahimeh Farahnakian;Tapio Pahikkala;Pasi Liljeberg;Juha Plosila

  • Regularized Machine Learning in the Genetic Prediction of Complex Traits

    Sebastian Okser;Tapio Pahikkala;Antti Airola;Tapio Salakoski

  • HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

    Iman Azimi;Arman Anzanpour;Amir M. Rahmani;Tapio Pahikkala

  • Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

    Justin Guinney;Tao Wang;Teemu D Laajala;Teemu D Laajala;Kimberly Kanigel Winner

  • Estimating the prediction performance of spatial models via spatial k-fold cross validation

    Jonne Pohjankukka;Tapio Pahikkala;Paavo Nevalainen;Jukka Heikkonen

  • 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

  • Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing

    Fahimeh Farahnakian;Tapio Pahikkala;Pasi Liljeberg;Juha Plosila

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

    Anna Cichonska;Balaguru Ravikumar;Elina Parri;Sanna Timonen

  • A Graph Kernel for Protein-Protein Interaction Extraction

    Antti Airola;Sampo Pyysalo;Jari Björne;Tapio Pahikkala

  • Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers

    Fahimeh Farahnakian;Tapio Pahikkala;Pasi Liljeberg;Juha Plosila

  • Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health

    Iman Azimi;Tapio Pahikkala;Amir M. Rahmani;Amir M. Rahmani;Hannakaisa Niela-Vilén

  • Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

    Jussi Toivonen;Ileana Montoya Perez;Parisa Movahedi;Harri Merisaari

  • Learning with multiple pairwise kernels for drug bioactivity prediction

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

  • An efficient algorithm for learning to rank from preference graphs

    Tapio Pahikkala;Evgeni Tsivtsivadze;Antti Airola;Jouni Järvinen

  • A comparison of AUC estimators in small-sample studies

    Antti Airola;Tapio Pahikkala;Willem Waegeman;Bernard De Baets

  • Fitting methods for intravoxel incoherent motion imaging of prostate cancer on region of interest level: Repeatability and gleason score prediction

    Harri Merisaari;Harri Merisaari;Parisa Movahedi;Parisa Movahedi;Parisa Movahedi;Ileana M. Perez;Ileana M. Perez;Ileana M. Perez;Jussi Toivonen;Jussi Toivonen;Jussi Toivonen

  • Comparison of automatic summarisation methods for clinical free text notes.

    Hans Moen;Hans Moen;Hans Moen;Laura-Maria Peltonen;Laura-Maria Peltonen;Juho Heimonen;Juho Heimonen;Antti Airola

Frequent Co-Authors

Tapio Salakoski
Tapio Salakoski University of Turku
Tero Aittokallio
Tero Aittokallio University of Helsinki
Bernard De Baets
Bernard De Baets Ghent University
Pasi Liljeberg
Pasi Liljeberg University of Turku
Juha Plosila
Juha Plosila University of Turku
Sampo Pyysalo
Sampo Pyysalo University of Turku
Hannu Tenhunen
Hannu Tenhunen Royal Institute of Technology
Filip Ginter
Filip Ginter University of Turku
Juho Rousu
Juho Rousu Aalto University
Esa Tyystjärvi
Esa Tyystjärvi University of Turku

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