Lanjing Zhang, MD
Department of Chemical Biology
Ernest
Mario School of Pharmacy
Rutgers University
Office Room #: 107, 164 Frelinghuysen Rd.
Piscataway, NJ 08854
E-mail: Lanjing.Zhang #at# rutgers.edu,
URL: https://thezhanglab.github.io/
List of Contributors
·
Yongfeng Zhang, PhD,
co-Investigator, School of Arts and Sciences, Rutgers University
·
Yong Lin, PhD,
co-Investigator, School of Public Health, Rutgers University
·
Fei Deng, Postdoc
research associate, School of Pharmacy, Rutgers University
·
Yunqi Li, PhD student, School of Arts and Sciences, Rutgers University
·
Mary (Nora) Disis, MD, co-Investigator, School of Medicine, Cancer
Vaccine Institute, UW Medicine
·
Chao Cheng, PhD,
co-Investigator, Baylor College of Medicine
·
Victoria
VanUitert, PhD, Junior faculty mentee, Bowling
Green State University, OH
·
Iris Shen, summer high
school student, Winsor School, Boston, MA.
· Award Number: R37CA277812
· Duration: September 1, 2022 - August 31, 2026
· Title: Screening and confirmatory machine learning for explainable
modeling of non-cancer deaths in cancer patients
· Keywords: Machine learning,
omics, cancer, survival
Project Summary
Due to the high stakes of
healthcare, the primary barrier is the extremely low tolerance of errors in
healthcare practice, which requires extremely high sensitivity and specificity
of any modelling. However, nearly all Machine learning (ML) models focus on
improving the accuracy. It cannot yet reach both extremely high sensitivity and
specificity using healthcare data. Separate screening and confirmatory ML tools
are proposed to achieve very high sensitivity and specificity. Moreover, many
ML algorithms suffer from the lack of clear explanations, such as deep learning
and neural networks, and would unlikely meet the FAIR criteria. Cancer is the
second leading cause of death in the U.S. The number of cancer survivors
continues to grow; unfortunately, so does the number of non-cancer deaths in
cancer patients. However, nearly all `omic and large
population studies focused on binary outcomes (cancer death or recurrence).
Therefore, there is an urgent need to better understand and reduce non-cancer
deaths in cancer patients, using `omic and population
data. To address these problems, the project will develop screening and
confirmatory ML to model cancer and noncancer deaths in breast, colorectal,
prostate and lung cancer patients using `omic data
and electronic health records (EHR). The proposed research will result in
fundamental contribution to ML tools, workflows and methods to make novel use
of `omic and EHR data for cancer care. It timely
meets the urgent needs in precise reduction of non-cancer deaths. This project
also uniquely addresses the Transformative Data Science research theme. The
interdisciplinary collaboration in this project as outlined in the
Collaboration Plan will offer a diverse basis for creative problem solving and
validation. The proposal has 3 broader impacts: 1) The developed novel ML
algorithms and technology will enable physicians to more precisely
prognosticate and treat cancer patients based on their risk of multicategory
deaths. 2) The research program will support and nurture undergraduate and
graduate researchers. 3) The proposed research program will support high school
and undergraduate students both in the conduct of research and in awareness of
ML usefulness. RELEVANCE (See instructions): The proposed research is relevant
to public health because the development and better utilization novel machine
learning for classifying non-cancer deaths in cancer patients is expected to
reduce the morbidity and mortality in these patients. Thus, the proposed
research is relevant to the part of the NIH's mission that pertains to
developing fundamental knowledge that will help to lengthen human lives and
reduce the burdens of human illness.
Publications
and Products:
Note: All full-text papers can be searched and
downloaded in PDF, if legally available, at the PI's ResearchGate page.
Journal articles
·
Balasubramanian
I, Bandyopadhyay S, Flores J, Smak JB, Lin X, Liu H, Sun S, Golovchenko
NB, Liu Y, Wang D, Patel R, Joseph II, Suntornsaratoon
P, Vargas J, Green PHR, Bhagat Govind, Lagana SM,
Ying W, Zhang Y, Wang Z, Li WV, Singh S, Zhou Z, Kollias
G, Farr LA, Moonah SN, Yu S, Wei Z, Ferraris R,
Bonder EM, Zhang L, Kiela PR, Edelblum KL, Liu TL, Gao N. Infection and inflammation
stimulate expansion of a CD74+ Paneth cell subset to regulate disease
progression. EMBO J. 2023 Nov 2;42(21):e113975 DOI: 10.15252/embj.2023113975
PMID: 37718683
·
Hu K, Zhang
L. Challenges and Opportunities Associated with Lifting the Zero COVID-19
Policy in China. Explor Res Hypothesis Med.
2024 Jan-Mar;9(1):71-75. doi: 10.14218/erhm.2023.00002. Epub 2023 Mar 8. PMID: 38572142; PMCID:PMC10989839.
·
Liang Y, Guo
GL, Zhang L. Current and Emerging Molecular Markers of Liver Diseases: A
Pathogenic Perspective. Gene Expression 2022; 21(1), 919. doi:
10.14218/GEJLR.2022.00010 PMCID: PMC11192043
· Cui M, Deng
F, Disis ML, Cheng C, Zhang L. Advances in
the Clinical Application of High-throughput Proteomics. Explor
Res Hypothesis Med (in press).
Project Impact
§ Education: We are training junior faculty,
postdoc associate, and undergraduate, graduate and high school students. Each of them will be taught based
on their levels of research experiences, background and interest. For junior faculty and postdoc
research associate, we will help them successfully obtain extramural grants
and eventually become an independent scientist. For the high school, undergraduate and graduate students, we aim to
help them obtain experiences and basic knowledge in machine learning and propel their career in science, engineering and/or medicine. Most of the software and coding developed in this project will made publicly available (see below). All new progress will be added into the other research collections upon completion.
§ Collaborations: In this project
we have established collaborations with several schools of Rutgers University,
UW Medicine and Baylor College of Medicine. Through such collaborations we
expect to explore many real applications and produce bigger Research Impacts.
Current and
Future Activities
The following are some of the highlights of our
ongoing work.
1. Develop highly
sensitive and specific machine learning algorithms to classify non-cancer
causes in cancer patients.
2. Study
effective and scalable methods for improving machine learning fairness.
Potential
Related Project(s)
Project Web
site URL: https://thezhanglab.github.io/R37.html
Online
software: Online software will be downloaded at https://github.com/FeiDeng-RUTGERS/.