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Computer Science

D-Index
40
Citations
8573
World Ranking
9171
National Ranking
3902

Overview

Lin Tan is affiliated with Purdue University West Lafayette in the United States and has contributed extensively to research in the fields of Medicine and Biochemistry, Genetics and Molecular Biology. Their work spans multiple subfields including Molecular Biology, Epidemiology, Cancer Research, Hematology, and Infectious Diseases.

Their recent published papers cover a range of topics, highlighting research on COVID-19, leukemia, lipid metabolism, cardiac protection mechanisms, and graft-versus-host disease. Significant publications include:

  • Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score, 2020, Clinical Infectious Diseases
  • Targeting MCL-1 Dysregulates Cell Metabolism and Leukemia-Stroma Interactions and Re-sensitizes Acute Myeloid Leukemia to BCL-2 Inhibition, 2020, Haematologica
  • Choline Kinase Alpha 2 Acts as a Protein Kinase to Promote Lipolysis of Lipid Droplets, 2021, Molecular Cell
  • ATF4 Protects the Heart From Failure by Antagonizing Oxidative Stress, 2022, Circulation Research
  • Mucus-degrading Bacteroides Link Carbapenems to Aggravated Graft-Versus-Host Disease, 2022, Cell

The topics covered in Lin Tan's research predominantly focus on:

  • Liver Disease Diagnosis and Treatment
  • Hepatitis B Virus Studies
  • Hepatitis C Virus Research
  • Mitochondrial Function and Pathology
  • Cancer, Hypoxia, and Metabolism
  • COVID-19 Clinical Research Studies
  • Acute Myeloid Leukemia Research

Lin Tan has collaborated frequently with several researchers including Philip L. Lorenzi, Yongping Yang, Huabao Liu, Qinghua Shang, and Yong-Ping Chen.

Their research has been published repeatedly in notable venues such as:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Blood
  • Nature Communications
  • SSRN Electronic Journal
  • Journal of Hepatology

Best Publications

  • Automatically learning semantic features for defect prediction

    Song Wang;Taiyue Liu;Lin Tan

  • Heterogeneous Defect Prediction

    Jaechang Nam;Wei Fu;Sunghun Kim;Tim Menzies

  • Hibernator: helping disk arrays sleep through the winter

    Qingbo Zhu;Zhifeng Chen;Lin Tan;Yuanyuan Zhou

  • A High Throughput String Matching Architecture for Intrusion Detection and Prevention

    Lin Tan;Timothy Sherwood

  • CURE: Code-Aware Neural Machine Translation for Automatic Program Repair

    Nan Jiang;Thibaud Lutellier;Lin Tan

  • Have things changed now?: an empirical study of bug characteristics in modern open source software

    Zhenmin Li;Lin Tan;Xuanhui Wang;Shan Lu

  • SherLog: error diagnosis by connecting clues from run-time logs

    Ding Yuan;Haohui Mai;Weiwei Xiong;Lin Tan

  • CoCoNuT: combining context-aware neural translation models using ensemble for program repair

    Thibaud Lutellier;Hung Viet Pham;Lawrence Pang;Yitong Li

  • Personalized defect prediction

    Tian Jiang;Lin Tan;Sunghun Kim

  • AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction

    Jianjun Huang;Xiangyu Zhang;Lin Tan;Peng Wang

  • Deep Semantic Feature Learning for Software Defect Prediction

    Song Wang;Taiyue Liu;Jaechang Nam;Lin Tan

  • /*icomment: bugs or bad comments?*/

    Lin Tan;Ding Yuan;Gopal Krishna;Yuanyuan Zhou

  • AutoComment: mining question and answer sites for automatic comment generation

    Edmund Wong;Jinqiu Yang;Lin Tan

  • Bug characteristics in open source software

    Lin Tan;Chen Liu;Zhenmin Li;Xuanhui Wang

  • @tComment: Testing Javadoc Comments to Detect Comment-Code Inconsistencies

    Shin Hwei Tan;Darko Marinov;Lin Tan;Gary T. Leavens

  • Online defect prediction for imbalanced data

    Ming Tan;Lin Tan;Sashank Dara;Caleb Mayeux

  • CRADLE: Cr oss-backend v a lidation to D etect and L ocalize bugs in D e ep learning libraries

    Hung Viet Pham;Thibaud Lutellier;Weizhen Qi;Lin Tan

  • CloCom: Mining existing source code for automatic comment generation

    Edmund Wong;Taiyue Liu;Lin Tan

  • Do time of day and developer experience affect commit bugginess

    Jon Eyolfson;Lin Tan;Patrick Lam

  • Discovering, reporting, and fixing performance bugs

    Adrian Nistor;Tian Jiang;Lin Tan

Frequent Co-Authors

Yuanyuan Zhou
Yuanyuan Zhou University of California, San Diego
Xiangyu Zhang
Xiangyu Zhang Purdue University West Lafayette
Timothy Sherwood
Timothy Sherwood University of California, Santa Barbara
Darko Marinov
Darko Marinov University of Illinois at Urbana-Champaign
Gary T. Leavens
Gary T. Leavens University of Central Florida
ChengXiang Zhai
ChengXiang Zhai University of Illinois at Urbana-Champaign
Abram Hindle
Abram Hindle University of Alberta
Xuanhui Wang
Xuanhui Wang Google (United States)
Nenad Medvidovic
Nenad Medvidovic University of Southern California
Sunghun Kim
Sunghun Kim Hong Kong University of Science and Technology

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