Archive Layout with Content

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five
Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

EntryItem 
John Doe2016Description of the item in the list
Jane Doe2019Description of the item in the list
Doe Doe2022Description of the item in the list
Header1Header2Header3
cell1cell2cell3
cell4cell5cell6
cell1cell2cell3
cell4cell5cell6
Foot1Foot2Foot3

Definition Lists

Definition List Title
Definition list division.
Startup
A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork
Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live
I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

Ordered List (Nested)

  1. List item one
    1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
    2. List item two
    3. List item three
    4. List item four
  2. List item two
  3. List item three
  4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you strikeout text.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H2O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC2, which should lift the 2 up.

Variable Tag

This allows you to denote variables.

Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models

Published in Journal of Pharmaceutical Analysis, 2025

This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles, knowledge graph embeddings, and large language model representations to enhance drug repurposing predictions.

Recommended citation: Yan, Y., Yang, Y., Tong, Z., Wang, Y., Yang, F., Pan, Z., Liu, C., Bai, M., Xie, Y., Li, Y., Shu, K., & Li, Y. (2025). "Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models." Journal of Pharmaceutical Analysis, 15(6), 101275.
Download Paper

Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information

Published in Nucleic Acids Research, 2016

This paper details a major update to the Therapeutic Target Database (TTD), significantly increasing its coverage of clinical trial drugs and targets, and cross-linking them to major pathway databases.

Recommended citation: Yang, H., Qin, C., Li, Y. H., Tao, L., Zhou, J., Yu, C. Y., Xu, F., Chen, Z., Zhu, F., & Chen, Y. Z. (2016). "Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information." Nucleic Acids Research. 44(D1):D1069-D1074.
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SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity

Published in PLoS ONE, 2016

This paper details major improvements to the SVM-Prot web-server, a machine learning tool for predicting protein functional families from sequences, complementing similarity-based methods.

Recommended citation: Li, Y. H., Xu, J. Y., Tao, L., Li, X. F., Li, S., Zeng, X., Chen, S. Y., Zhang, P., Qin, C., Zhang, C., Chen, Z., Zhu, F., & Chen, Y. Z. (2016). "SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity." PLoS ONE. 11(8):e0155290.
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The human kinome targeted by FDA approved multi-target drugs and combination products: a comparative study from the drug-target interaction network perspective

Published in PLoS ONE, 2016

This paper provides a comparative network analysis of FDA-approved multi-target drugs and combination products that target the human kinome, offering insights for next-generation polypharmacology.

Recommended citation: Li, Y. H., Wang, P. P., Li, X. X., Yu, C. Y., Yang, H., Zhou, J., Xue, W. W., Tan, J., & Zhu, F. (2016). "The human kinome targeted by FDA approved multi-target drugs and combination products: a comparative study from the drug-target interaction network perspective." PLoS ONE. 11(11):e0165737.
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Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

Published in Nucleic Acids Research, 2018

This paper describes a major update to the Therapeutic Target Database (TTD), enhancing its utility for patient-focused research and clinical investigation of targeted therapeutics.

Recommended citation: Li, Y. H., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., Zhang, Y., Li, S., Yang, F., Sun, Q., Qin, C., Zeng, X., Chen, Z., Chen, Y. Z., & Zhu, F. (2018). "Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics." Nucleic Acids Research. 46(D1):D1121-D1127.
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Clinical trials, progression-speed differentiating features, and swiftness rule of the innovative targets of first-in-class drugs

Published in Briefings in Bioinformatics, 2020

This paper analyzes the clinical trial timelines of 89 innovative targets of first-in-class drugs to identify features that differentiate the speed of clinical progression.

Recommended citation: Li, Y. H., Li, X. X., Hong, J. J., Wang, Y. X., Fu, J. B., Yang, H., Yu, C. Y., Li, F. C., Hu, J., Xue, W. W., Jiang, Y. Y., Chen, Y. Z., & Zhu, F. (2020). "Clinical trials, progression-speed differentiating features, and swiftness rule of the innovative targets of first-in-class drugs." Briefings in Bioinformatics. 21(2):649-662.
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Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network

Published in Food Research International, 2022

This work develops and compares three structure-taste relationship models based on artificial neural networks to predict whether a molecule is a bitterant, sweetener, or neither.

Recommended citation: Bo, W., Qin, D., Zheng, X., Wang, Y., Ding, B., Li, Y., & Liang, G. (2022). "Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network." Food Research International. 153:110974.
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A Comparative Benchmarking and Evaluation Framework for Heterogeneous Network-Based Drug Repositioning Methods

Published in Briefings in Bioinformatics, 2024

This paper presents a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods, providing a comprehensive framework to evaluate their performance, scalability, and usability.

Recommended citation: Li, Y., Yang, Y., Tong, Z. et al. (2024). "A Comparative Benchmarking and Evaluation Framework for Heterogeneous Network-Based Drug Repositioning Methods." Briefings in Bioinformatics. 25(3):bbae172.
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DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

Published in Computers in Biology and Medicine, 2024

This paper introduces DTNPD, a specialized, comprehensive database of drugs and targets for neurological and psychiatric disorders (NPDs) to address data quality and coverage gaps in existing resources.

Recommended citation: Luo, D., Tong, Z., Wen, L., Bai, M., Jin, X., Liu, Z., Li, Y., & Xue, W. (2024). "DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders." Computers in Biology and Medicine. 175:108536.
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Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

Published in Genome Biology, 2024

This paper presents a systematic evaluation of 49 simulation methods for single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, providing practical guidelines for selecting appropriate simulators.

Recommended citation: Duo, H., Li, Y., Lan, Y. et al. (2024). "Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios." Genome Biology. 25:145.
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Lung Cancer Biomarker Database (LCBD): A Comprehensive and Curated Repository of Lung Cancer Biomarkers

Published in BMC Cancer, 2025

This paper introduces the Lung Cancer Biomarker Database (LCBD), a centralized, curated platform designed to consolidate fragmented biomarker data to aid in early screening and personalized treatment of lung cancer.

Recommended citation: Li, Y., Tong, Z., Yang, Y. et al. (2025). "Lung Cancer Biomarker Database (LCBD): a comprehensive and curated repository of lung cancer biomarkers." BMC Cancer. 25:478.
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VARIDT 4.0: distribution variability of drug transporters

Published in Nucleic Acids Research (IF=13.1, CAS Ranking: Q2 Top), 2025

This paper introduces VARIDT 4.0, a database focused on the distribution variability of drug transporters, published in Nucleic Acids Research.

Recommended citation: Li, Y., Yang, F., Pan, Z., Yan, Y., Jiang, B., Huang, X., Wang, H., Qin, X., Zeng, S., Fu, T., & Zhu, F. (2025). "VARIDT 4.0: distribution variability of drug transporters." Nucleic Acids Research. Accepted.
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