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• Journal article
  Vano LJ, McCutcheon RA, Rutigliano G, Kaar SJ, Finelli V, Nordio G, Wellby G, Sedlacik J, Statton B, Rabiner EA, Ye R, Veronese M, Hopkins SC, Koblan KS, Everall IP, Howes ODet al., 2024,

  Mesostriatal Dopaminergic Circuit Dysfunction in Schizophrenia: A Multimodal Neuromelanin Sensitive Magnetic Resonance Imaging and [<SUP>18</SUP>F]-DOPA Positron Emission Tomography Study

  , BIOLOGICAL PSYCHIATRY, Vol: 96, Pages: 674-683, ISSN: 0006-3223
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• Journal article
  Wu Y, Jewell S, Xing X, Nan Y, Strong AJ, Yang G, Boutelle MGet al., 2024,

  Real-time non-invasive imaging and detection of spreading depolarizations through EEG: an ultra-light explainable deep learning approach

  , IEEE Journal of Biomedical and Health Informatics, Vol: 28, Pages: 5780-5791, ISSN: 2168-2208

  A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension – frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile.

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• Journal article
  Zhang S, Fang Y, Nan Y, Wang S, Ding W, Ong Y-S, Frangi AF, Pedrycz W, Walsh S, Yang Get al., 2024,

  Fuzzy Attention-Based Border Rendering Orthogonal Network for Lung Organ Segmentation

  , IEEE TRANSACTIONS ON FUZZY SYSTEMS, Vol: 32, Pages: 5462-5476, ISSN: 1063-6706
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• Journal article
  Li Z, Chang D, Zhang Z, Luo F, Liu Q, Zhang J, Yang G, Wu Wet al., 2024,

  Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction

  , IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 43, Pages: 3398-3411, ISSN: 0278-0062
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• Journal article
  Hu J, Wu P, Liu Y, Li Y, Li Q, Wang S, Qian K, Yang Get al., 2024,

  Discovering photoswitchable molecules for drug delivery with large language models and chemist instruction training

  , Pharmaceuticals, Vol: 17, ISSN: 1424-8247

  Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face’s Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist’s intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance.

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• Journal article
  Li X, Chen J, Zhang H, Cho Y, Hwang SH, Gao Z, Yang Get al., 2024,

  Hierarchical Relational Inference for Few-Shot Learning in 3D Left Atrial Segmentation

  , IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, Vol: 8, Pages: 3352-3367, ISSN: 2471-285X
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• Journal article
  Liu Y, Yu Y, Ouyang J, Jiang B, Ostmeier S, Wang J, Lu-Liang S, Yang Y, Yang G, Michel P, Liebeskind DS, Lansberg M, Moseley ME, Heit JJ, Wintermark M, Albers G, Zaharchuk Get al., 2024,

  Prediction of Ischemic Stroke Functional Outcomes from Acute-Phase Noncontrast CT and Clinical Information

  , RADIOLOGY, Vol: 313, ISSN: 0033-8419
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• Journal article
  Nan Y, Xing X, Wang S, Tang Z, Felder FN, Zhang S, Ledda RE, Ding X, Yu R, Liu W, Shi F, Sun T, Cao Z, Zhang M, Gu Y, Zhang H, Gao J, Wang P, Tang W, Yu P, Kang H, Chen J, Lu X, Zhang B, Mamalakis M, Prinzi F, Carlini G, Cuneo L, Banerjee A, Xing Z, Zhu L, Mesbah Z, Jain D, Mayet T, Yuan H, Lyu Q, Qayyum A, Mazher M, Wells A, Walsh SLF, Yang Get al., 2024,

  Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

  , MEDICAL IMAGE ANALYSIS, Vol: 97, ISSN: 1361-8415
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• Journal article
  Niu Z, Xiao X, Wu W, Cai Q, Jiang Y, Jin W, Wang M, Yang G, Kong L, Jin X, Yang G, Chen Het al., 2024,

  PharmaBench: enhancing ADMET benchmarks with large language models

  , Scientific Data, Vol: 11, ISSN: 2052-4463

  Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

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• Journal article
  Wen S, Liu Y, Yang G, Chen W, Wu H, Zhu X, Wang Yet al., 2024,

  A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations

  , SCIENTIFIC REPORTS, Vol: 14, ISSN: 2045-2322
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