Topic outline

  • Literature

    Survey/review papers on Machine Learning in different bioinformatics topics

    Below, survey/review papers are listed by bioinformatics application topic. These are meant to use as starting points for literature search. The oral presentation should not be one of these papers.

    You may also choose a topic for your oral presentation that is not listed below.

    Drug response prediction

    Güvenç Paltun, B., Mamitsuka, H. and Kaski, S., 2021. Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Briefings in bioinformatics, 22(1), pp.346-359. https://academic.oup.com/bib/article/22/1/346/5678052

    Drug combination responses and synergy

    Kong, W., Midena, G., Chen, Y., Athanasiadis, P., Wang, T., Rousu, J., He, L. and Aittokallio, T., 2022. Systematic review of computational methods for drug combination prediction. Computational and Structural Biotechnology Journal. https://www.sciencedirect.com/science/article/pii/S2001037022002100

    Protein-ligand interation prediction

    Dhakal, A., McKay, C., Tanner, J.J. and Cheng, J., 2022. Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions. Briefings in Bioinformatics, 23(1), p.bbab476. https://academic.oup.com/bib/article/23/1/bbab476/6444314?login=true

    Machine learning for antibiotic resistance 

    Sakagianni, A., Koufopoulou, C., Feretzakis, G., Kalles, D., Verykios, V.S. and Myrianthefs, P., 2023. Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review. Antibiotics, 12(3), p.452. https://www.mdpi.com/2079-6382/12/3/452

    Small Molecule Identification using Tandem Mass Spectra

    Nguyen, D.H., Nguyen, C.H. and Mamitsuka, H., 2019. Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches. Briefings in bioinformatics, 20(6), pp.2028-2043. https://academic.oup.com/bib/article/20/6/2028/5066172?login=true

    Protein structure prediction

    Wodak, S.J., Vajda, S., Lensink, M.F., Kozakov, D. and Bates, P.A., 2022. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annual Review of Biophysics, 52. https://www.annualreviews.org/doi/abs/10.1146/annurev-biophys-102622-084607

    Protein function prediction

    Zhou, N., Jiang, Y., Bergquist, T.R., Lee, A.J., Kacsoh, B.Z., Crocker, A.W., Lewis, K.A., Georghiou, G., Nguyen, H.N., Hamid, M.N. and Davis, L., 2019. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome biology, 20(1), pp.1-23. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1835-8

    Prediction of reaction pathways (Retrosynthesis)

    Sun, Y. and Sahinidis, N.V., 2022. Computer-aided retrosynthetic design: fundamentals, tools, and outlook. Current Opinion in Chemical Engineering, 35, p.100721. https://www.sciencedirect.com/science/article/pii/S2211339821000538

    Protein-protein interaction prediction

    Hu, L., Wang, X., Huang, Y.A., Hu, P. and You, Z.H., 2021. A survey on computational models for predicting protein–protein interactions. Briefings in bioinformatics, 22(5), p.bbab036. https://academic.oup.com/bib/article/22/5/bbab036/6159365?login=true

    Single-cell genomics

    ZouRaimundo, F., Meng-Papaxanthos, L., Vallot, C. and Vert, J.P., 2021. Machine learning for single-cell genomics data analysis. Current Opinion in Systems Biology, 26, pp.64-71. https://www.sciencedirect.com/science/article/pii/S2452310021000172
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