activity
20182025
most citedExploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

88 citations · 199 across the 9 of their papers we have counts for

collaborators

9 papers

quant-ph2025

Enhancing Drug Discovery: Quantum Machine Learning for QSAR Prediction with Incomplete Data

Wei-Yin Chiang, Po-Yu Kao, Tzu-Lan Yeh +3

Qualitative structure-activity relationship (QSAR) is important for drug discovery and offers valuable insights into the biological interactions of potential drug candidates. It ha…

quant-ph2022★ 88 cited

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Po-Yu Kao, Ya-Chu Yang, Wei-Yin Chiang +8

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time and resource-consuming, and i…

q-bio.BM2021★ 20 cited

Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning

Po-Yu Kao, Shu-Min Kao, Nan-Lan Huang +1

Drug-target interaction (DTI) prediction plays a crucial role in drug discovery, and deep learning approaches have achieved state-of-the-art performance in this field. We introduce…

eess.IV2019★ 5 cited

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

Po-Yu Kao, Jefferson W. Chen, B. S. Manjunath

The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to othe…

cs.CV2019★ 3 cited

Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior

Po-Yu Kao, Jefferson W. Chen, B. S. Manjunath

We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation. First, we utilize the ground-truth brain tumor lesi…

cs.LG2019★ 8 cited

Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

Po-Yu Kao, Angela Zhang, Michael Goebel +2

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature…

cs.CV2019★ 15 cited

Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement

Jiaxiang Jiang, Po-Yu Kao, Samuel A. Belteton +2

We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged ce…

eess.IV2019★ 1 cited

Automated Segmentation of CT Scans for Normal Pressure Hydrocephalus

Angela Zhang, Po-Yu Kao, Ronald Sahyouni +3

Normal Pressure Hydrocephalus (NPH) is one of the few reversible forms of dementia, Due to their low cost and versatility, Computed Tomography (CT) scans have long been used as an…

cs.CV2018★ 59 cited

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

Po-Yu Kao, Thuyen Ngo, Angela Zhang +2

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize…