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Computer Science

D-Index
52
Citations
17093
World Ranking
4964
National Ranking
2310

Overview

Marinka Zitnik is a researcher affiliated with Harvard University in the United States. Their work spans major areas within computer science and biochemistry, genetics, and molecular biology, with notable contributions to interdisciplinary fields.

The primary fields of study include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Zitnik's subfields of focus include:

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

Their research covers a variety of main topics such as:

  • Computational Drug Discovery Methods
  • Bioinformatics and Genomic Networks
  • Cell Image Analysis Techniques
  • Advanced Graph Neural Networks
  • Machine Learning in Healthcare
  • Biomedical Text Mining and Ontologies
  • Machine Learning in Materials Science

Frequent publication venues where Zitnik has contributed multiple works include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Machine Intelligence
  • Nature Communications
  • Bioinformatics

Zitnik has collaborated frequently with several researchers throughout their career. Frequent coauthors include:

  • Kexin Huang
  • Michelle M. Li
  • Tianfan Fu
  • Shanghua Gao
  • Ayush Noori

Recent papers published by Zitnik and collaborators demonstrate engagement in topics at the frontier of artificial intelligence and biomedical research:

  • Scientific discovery in the age of artificial intelligence, 2023, Nature
  • Open Graph Benchmark: Datasets for Machine Learning on Graphs, 2020, arXiv (Cornell University)
  • Network medicine framework for identifying drug-repurposing opportunities for COVID-19, 2021, PubMed Central
  • DeepPurpose: a deep learning library for drug-target interaction prediction, 2020, Bioinformatics
  • Building a knowledge graph to enable precision medicine, 2023, Scientific Data

Best Publications

  • Orange: data mining toolbox in python

    Janez Demšar;Tomaž Curk;Aleš Erjavec;Črt Gorup

  • Modeling polypharmacy side effects with graph convolutional networks.

    Marinka Zitnik;Monica Agrawal;Jure Leskovec

  • Open Graph Benchmark: Datasets for Machine Learning on Graphs

    Weihua Hu;Matthias Fey;Marinka Zitnik;Yuxiao Dong

  • Interpretability of machine learning‐based prediction models in healthcare

    Gregor Stiglic;Primoz Kocbek;Nino Fijacko;Marinka Zitnik

  • Strategies for Pre-training Graph Neural Networks

    Weihua Hu;Bowen Liu;Joseph Gomes;Marinka Zitnik

  • Open Graph Benchmark: Datasets for Machine Learning on Graphs

    Weihua Hu;Matthias Fey;Marinka Zitnik;Yuxiao Dong

  • GNNExplainer: Generating Explanations for Graph Neural Networks

    Rex Ying;Dylan Bourgeois;Jiaxuan You;Marinka Zitnik

  • Predicting multicellular function through multi-layer tissue networks

    Marinka Zitnik;Jure Leskovec

  • Predicting multicellular function through multi-layer tissue networks.

    Marinka Zitnik;Jure Leskovec

  • GNNExplainer: Generating Explanations for Graph Neural Networks

    Zhitao Ying;Dylan Bourgeois;Jiaxuan You;Marinka Zitnik

  • Network medicine framework for identifying drug-repurposing opportunities for COVID-19.

    Deisy Morselli Gysi;Deisy Morselli Gysi;Ítalo Do Valle;Marinka Zitnik;Asher Ameli

  • Building a knowledge graph to enable precision medicine

    Unknown

  • DeepPurpose: a deep learning library for drug-target interaction prediction.

    Kexin Huang;Tianfan Fu;Lucas M Glass;Marinka Zitnik

  • Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

    Marinka Zitnik;Francis Nguyen;Francis Nguyen;Bo Wang;Jure Leskovec

  • Graph representation learning in biomedicine and healthcare

    Unknown

  • Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.

    Marinka Zitnik;Francis Nguyen;Francis Nguyen;Bo Wang;Jure Leskovec

  • Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

    Xuan Wang;Yu Zhang;Xiang Ren;Yuhao Zhang

  • Learning Structural Node Embeddings via Diffusion Wavelets

    Claire Donnat;Marinka Zitnik;David Hallac;Jure Leskovec

  • Data Fusion by Matrix Factorization

    Marinka Zitnik;Blaz Zupan

  • Empowering biomedical discovery with AI agents

    Unknown

  • TrustLLM: Trustworthiness in Large Language Models

    Unknown

  • To Embed or Not: Network Embedding as a Paradigm in Computational Biology.

    Walter Nelson;Marinka Zitnik;Bo Wang;Bo Wang;Jure Leskovec

  • Learning Structural Node Embeddings Via Diffusion Wavelets.

    Claire Donnat;Marinka Zitnik;David Hallac;Jure Leskovec

  • Embedding Logical Queries on Knowledge Graphs

    William L. Hamilton;Payal Bajaj;Marinka Zitnik;Dan Jurafsky

  • Network enhancement as a general method to denoise weighted biological networks.

    Bo Wang;Armin Pourshafeie;Marinka Zitnik;Junjie Zhu

  • A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family

    Jordi Carreras-Puigvert;Marinka Zitnik;Marinka Zitnik;Ann-Sofie Jemth;Megan Carter

  • GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

    Xiang Zhang;Marinka Zitnik

  • Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

    Deisy Morselli Gysi;Ítalo Do Valle;Marinka Zitnik;Asher Ameli

  • Graph Meta Learning via Local Subgraphs

    Kexin Huang;Marinka Zitnik

Frequent Co-Authors

Jure Leskovec
Jure Leskovec Stanford University
Jimeng Sun
Jimeng Sun University of Illinois at Urbana-Champaign
Cao Xiao
Cao Xiao General Electric (United Kingdom)
Xiang Zhang
Xiang Zhang University of Hong Kong
Serafim Batzoglou
Serafim Batzoglou Stanford University
Marcus W. Feldman
Marcus W. Feldman Stanford University
Carlos Bustamante
Carlos Bustamante Stanford University
Dan Jurafsky
Dan Jurafsky Stanford University
Russ B. Altman
Russ B. Altman Stanford University

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