Chemoinfomatics Laboratory

A research group dedicated to artificial intelligence/machine leanring algorithms, and the applciations in chemistry, biology and medicine areas.


Recent Works


Transfer Learning

  1. Zhang J., Li B. Y.*, Hu Y., Zhou L. X., Wang G. Z., Guo G., Zhang Q. H., Lei S. C.*, Zhang A. H., A Parameter-Free Framework for Calibration Enhancement of Near-Infrared Spectroscopy Based on Correlation Constraint, Anal. Chim. Acta, 2021, 1142: 169-178.
  2. Zhang J., Hu Y., Zhou L. X., Li B. Y.* Progress of Chemometric Algorithms in Near infrared Spectroscopic Analysis [J]. Journal of instrumental analysis, 2020, 39(10): 1196-1203.
  3. Zhang J., Guo C., Cui X. Y., Cai W. S., Shao X. G.* A two-level strategy for standardization of near infrared spectra by multi-level simultaneous component analysis [J]. Analytica Chimica Acta, 2019, 1050: 25-31.
  4. Zhang J., Cui X. Y., Cai W. S., Shao X. G.* Modified linear model correction: A calibration transfer method without standard samples [J]. NIR news, 2018, 29(8): 24-27.
  5. Zhang J., Cai W. S., Shao X. G.*. New Algorithms for Calibration Transfer in Near Infrared Spectroscopy [J]. Progress in Chemistry, 2017, 29(8): 902-910.

Factor Analysis

  1. Zhang J., Guo C.,Cai W. S., Shao X. G.* Direct non-trilinear decomposition for analyzing high-dimensional data with imperfect trilinearity [J]. Chemometrics and Intelligent Laboratory Systems, 2021, 210, 104244.
  2. Zhang J., Peng Q. R., Xu L. Q., Yang M.*, Wu A. J., Ye S. Z. Application of three-way data analysis(second-order tensor decomposition)algorithms in analysis of liquid chromatography [J]. Chinese Journal of Chromatography, 2014, 32(11): 1165-1171.

Feature Selection

  1. Zhang J., Cui X. Y., Cai W. S., Shao X. G.*. A variable importance criterion for variable selection in near-infrared spectral analysis [J]. Science China Chemistry, 2019, 62(2): 271-279.
  2. Zhang J., Cui X. Y., Cai W. S., Shao X. G.* Combination of heuristic optimal partner bands for variable selection in near-infrared spectral analysis [J]. Journal of Chemometrics, 2018, 32(11): e2971.

Chemo-/bio-informatics Applications

  1. Zhang J.†, Ma L.†, Li B. Y., et al. Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms [J]. Science of the Total Environment, 2023, 870: 161861.
  2. Zhang J.†, Yuan T.†, Wei S. X.†, Feng Z. H.†, Yang L. B.*, Huang H.*. New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning[J]. RSC Adv., 2022, 12: 14716-14723.
  3. Zhang J., Yang L. B., Tian Z. Q., Zhao W. J., Sun C. Q., Zhu L. J., Huang M. J., Guo G.*, Liang G. Y.* Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure–Activity Relationship [J]. ACS Med. Chem. Lett., 2022, 13(1): 99-104.
  4. Yan X. L., Zhang J., Russo D. P., Zhu H.*, Yan B.* Prediction of Nano–Bio Interactions through Convolutional Neural Network Analysis of Nanostructure Images [J]. ACS Sustainable Chemistry & Engineering, 2021, 8, 51, 19096–19104.
  5. Wang S. Y., Zhang J., Cai W. S., Shao X. G.* Titanium dioxide as an adsorbent to enhance the detection ability of near-infrared diffuse reflectance spectroscopy [J]. Chinese Chemical Letters, 2019, 30(5): 1024-1026.
  6. Wang S. Y., Zhang J., Wang C. C., Yu X. M., Cai W. S., Shao X. G*. Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate [J]. Chinese Chemical Letters, 2018, 30(1): 111-114.
  7. Cui X. Y., Zhang J., Cai W. S., Shao X. G*. Selecting temperature-dependent variables in near-infrared spectra for aquaphotomics [J]. Chemometrics and Intelligent Laboratory Systems, 2018, 183: 23-28.
  8. Cui X. Y., Zhang J., Cai W. S., Shao X. G*. Chemometric algorithms for analyzing high dimensional temperature dependent near infrared spectra [J]. Chemometrics and Intelligent Laboratory Systems, 2017, 170: 109-117.
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