<p>Artificial Intelligence driven approaches, especially based on deep learning algorithms, provided an alternative perspective in summarizing the common features in large-scale and complex datasets and aided the human professions in discovering novel features in cross-domain research. In this dissertation, the author proposed his research of developing AI-driven algorithms to reveal the real relation of complex medical data. The author started to identify the abnormal structures from the radiology images. When the abnormal structure was detected, the author built a model to explore the domain layers or cell phenotype of the specific tissues. Finally, the author evaluated cell-cell communication for the downstream tasks.</p>
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<p>In his first research, the author applied IResNet, a two-stage prediction-interpretation Convolution Neural Network, to assist clinicians in the early diagnosis of Autism Spectrum Disorders (ASD). IresNet first predicted the input sMRI scan to one of the two categories: (1) ASD group or (2) Normal Control group, and interpret the prediction using a \textit{post-hoc} approach and visualized the abnormal structures on top of the raw inputs. The proposed method can be applied to other neural diseases such as Alzheimer's Disease. </p>
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<p>When the abnormal structure was detected, the author proposed a method to reveal the latent relation at the tissue level. Thus the author proposed SiGra, an unsupervised learning paradigm to identify the domain layers and cellular phenotype in a particular tissue slide based on the corresponding gene expression matrix and the morphology representations. SiGra outperformed other benchmarking algorithms in three different tissue slides from three commercialized single-cell platforms.</p>
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<p>At last, the author measured the potential interactions between two cells. The proposed spaCI, measured the correlation of a Ligand-Receptor interaction in the high-dimension latent space and predicted the interactive $L-R$ pair for downstream analysis. </p>
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<p>In summary, the author presented three end-to-end AI-driven frameworks to facilitate clinicians and pathologists in better understanding the latent connections of complex diseases and tissues. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22272901 |
| Date | 20 May 2024 |
| Creators | Ziyang Tang (6593525) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/AI_MEET_BIOINFORMATICS_INTERPRETING_BIOMEDICAL_DATA_USING_DEEP_LEARNING/22272901 |
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