Tero Aittokallio
Molecular Biology · 2014–2022
A researcher in the Science corpus: 8 papers; trusts 26 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 105 citations into it; #9,415 of 17,773 by trust here, #9,727 by citations
- Computational Theory and Mathematics — 47 citations into it; #3,913 of 12,182 by trust here, #1,379 by citations
- Cancer Research — 36 citations into it
- Artificial Intelligence — 10 citations into it
- Computer Vision and Pattern Recognition — 10 citations into it
- Genetics — 10 citations into it
- Biophysics — 9 citations into it
- Immunology — 9 citations into it
Whom Tero Aittokallio trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mehmet Gönen
- Minoru Kanehisa
- Chang Wang
- Paul Geeleher
- Vladimir Yu. Kiselev
- Susumu Goto
- Sridhar Mahadevan
- Ethem Alpaydin
- Christian Ritz
Who trusts Tero Aittokallio overall
Papers from Tero Aittokallio's perspective
Ordered by how strongly the researchers Tero Aittokallio trusts endorse each paper. Same corpus, a different order for every researcher.
- The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity (Nature)
- Random Forests
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
- The Genomic Landscapes of Human Breast and Colorectal Cancers (Science)
- Kernel Methods for Pattern Analysis
- The landscape of cancer genes and mutational processes in breast cancer (Nature)
- The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease
- A global geometric framework for nonlinear dimensionality reduction.
- Wisdom of crowds for robust gene network inference (Nature Methods)
- Molecular Profiling of Breast Cancer Cell Lines Defines Relevant Tumor Models and Provides a Resource for Cancer Gene Discovery (PLoS ONE)
Their papers
- Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis (2014) — 601 citations
- Toward more realistic drug–target interaction predictions (2014) — 514 citations
- SynergyFinder: a web application for analyzing drug combination dose–response matrix data (2017) — 507 citations
- Searching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model (2015) — 455 citations
- A community effort to assess and improve drug sensitivity prediction algorithms (2014) — 728 citations
- SynergyFinder 2.0: visual analytics of multi-drug combination synergies (2020) — 728 citations
- Machine learning and feature selection for drug response prediction in precision oncology applications (2018) — 169 citations
- Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022) — 606 citations