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Bishnu Sarker

Title: Assistant Professor

Department: Information Science

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of Lorraine, 2021
    Major: Computer Science
    Dissertation: "On Graph-Based Approaches for Protein Function Annotation and Knowledge Discovery"
  • MS, Paris VI University, 2016
    Major: Computer Science
    Specialization: Data Learning and Knowledge
  • BS, Khulna University of Engineering & Technology, 2011
    Major: Computer Science and Engineering

Current Scheduled Teaching

No current or future courses scheduled.

Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

HINF 5639.020Population Health InformaticsSpring 2026
INFO 4365.401Health Sciences Information ManagementFall 2025 Syllabus SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Book Chapter

  • Shishir, F.S., Sarker, B., Rahman, F., Shomaji, S. (2023). MetaLLM: Residue-Wise Metal Ion Prediction Using Deep Transformer Model. Lecture Notes in Computer Science. 42-55. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34960-7_4

    • Conference Proceeding

  • Sarker, B., Ritchie, D., Aridhi, S. (2019). Functional Annotation of Proteins using Domain Embedding based Sequence Classification. Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. 163-170. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0008353401630170

    • Journal Article

  • Shishir, F.S., Sarker, B., Rahman, F., Shomaji, S. (2025). A Deep Learning Framework for Protein-to-Metal Binding Prediction Using Protein Language Models. Other. 1-12. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tcbbio.2025.3595446
  • Stephens, D.C., Mungai, M., Crabtree, A., Beasley, H.K., Garza-Lopez, E., Vang, L., Neikirk, K., Vue, Z., Vue, N., Marshall, A.G., Turner, K., Shao, J., Sarker, B., Murray, S., Gaddy, J.A., Davis, J., Damo, S.M., Hinton, A.O. (2023). Protocol for isolating mice skeletal muscle myoblasts and myotubes via differential antibody validation. Other. 4 (4) 102591. Elsevier BV. https://doi.org/10.1016/j.xpro.2023.102591
  • Stephens, D.C., Crabtree, A., Beasley, H.K., Garza‐Lopez, E., Mungai, M., Vang, L., Neikirk, K., Vue, Z., Vue, N., Marshall, A.G., Turner, K., Shao, J., Sarker, B., Murray, S., Gaddy, J.A., Hinton, A.O., Damo, S., Davis, J. (2023). In the Age of Machine Learning Cryo‐EM Research is Still Necessary: A Path toward Precision Medicine. Other. 7 (8) Wiley. https://doi.org/10.1002/adbi.202300122
  • Sarker, B., Khare, N., Devignes, M., Aridhi, S. (2022). Improving automatic GO annotation with semantic similarity. BMC Bioinformatics. 23 (S2) Springer Science and Business Media LLC. https://doi.org/10.1186/s12859-022-04958-7
  • Veras, M.B., Sarker, B., Aridhi, S., Gomes, J.P., Macêdo, J.A., Nguifo, E.M., Devignes, M., Smaïl-Tabbone, M. (2022). On the design of a similarity function for sparse binary data with application on protein function annotation. Knowledge-Based Systems. 238 107863. Elsevier BV. https://doi.org/10.1016/j.knosys.2021.107863
  • Sarker, B., Ritchie, D.W., Aridhi, S. (2020). GrAPFI: predicting enzymatic function of proteins from domain similarity graphs. BMC Bioinformatics. 21 (1) Springer Science and Business Media LLC. https://doi.org/10.1186/s12859-020-3460-7
  • Belhouachi, N., Xochelli, A., Boudjoghra, M., Lesty, C., Cassoux, N., Fardeau, C., Tran, T.H., Choquet, S., Sarker, B., Houillier, C., Alentorn, A., LeHoang, P., Soussain, C., Touitou, V., Merle-Beral, H., Hoang-Xuan, K., Bodaghi, B., Stamatopoulos, K., Davi, F. (2020). Primary vitreoretinal lymphomas display a remarkably restricted immunoglobulin gene repertoire. Blood Advances. 4 (7) 1357-1366. American Society of Hematology. https://doi.org/10.1182/bloodadvances.2019000980
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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