Diyuan Lu
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View article: Digital Response Test in Epilepsy assesses interictal epileptiform discharge effects in real time
Digital Response Test in Epilepsy assesses interictal epileptiform discharge effects in real time Open
Objective Interictal epileptiform discharges (IEDs) in people with epilepsy (PWE) can impair cognitive functions and increase reaction time (RT) and the likelihood of missed reactions. These effects are not routinely assessed, because reli…
View article: Decoding <i>Helicobacter pylori</i> Resistance: Machine Learning–Enhanced Prediction of Antibiotic Susceptibility using Whole-Genome Sequencing
Decoding <i>Helicobacter pylori</i> Resistance: Machine Learning–Enhanced Prediction of Antibiotic Susceptibility using Whole-Genome Sequencing Open
Background Helicobacter pylori is a significant risk factor for gastric cancer, peptic ulcers, and MALT lymphoma. Rising antibiotic resistance rates complicate treatment strategies. While nucleotide sequence based assays are reliable in pr…
View article: Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens
Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens Open
Precision oncology relies on molecular biomarkers to stratify patients into responders and non-responders to a given treatment. Although gene expression profiles have historically been explored for biomarker discovery, fewer studies invest…
View article: Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy
Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy Open
Background Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactor…
View article: Enhancing Gene Expression Representation and Drug Response Prediction with Data Augmentation and Gene Emphasis
Enhancing Gene Expression Representation and Drug Response Prediction with Data Augmentation and Gene Emphasis Open
Representation learning for tumor gene expression (GEx) data with deep neural networks is limited by the large gene feature space and the scarcity of available clinical and preclinical data. The translation of the learned representation be…
View article: Machine learning for healthcare with a focus on the early diagnosis of epilepsy and brain tumor detection
Machine learning for healthcare with a focus on the early diagnosis of epilepsy and brain tumor detection Open
Machine learning (ML) techniques have evolved rapidly in recent years and have shown impressive capabilities in feature extraction, pattern recognition, and causal inference. There has been an increasing attention to applying ML to medical…
View article: Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data Open
We apply deep learning (DL) on Magnetic resonance spectroscopy (MRS) data for the task of brain tumor detection. Medical applications often suffer from data scarcity and corruption by noise. Both of these problems are prominent in our data…
View article: Staging Epileptogenesis with Deep Neural Networks
Staging Epileptogenesis with Deep Neural Networks Open
Epilepsy is a common neurological disorder characterized by recurrent seizures accompanied by excessive synchronous brain activity. The process of structural and functional brain alterations leading to increased seizure susceptibility and …
View article: Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation Open
The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems. First, the amount of training data may be scarce, as it is limited by the number of patients who have acquired the condition to be diagnosed. Se…
View article: Staging Epileptogenesis with Deep Neural Networks
Staging Epileptogenesis with Deep Neural Networks Open
Epilepsy is a common neurological disorder characterized by recurrent seizures accompanied by excessive synchronous brain activity. The process of structural and functional brain alterations leading to increased seizure susceptibility and …
View article: Towards Early Diagnosis of Epilepsy from EEG Data
Towards Early Diagnosis of Epilepsy from EEG Data Open
Epilepsy is one of the most common neurological disorders, affecting about 1% of the population at all ages. Detecting the development of epilepsy, i.e., epileptogenesis (EPG), before any seizures occur could allow for early interventions …
View article: Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy Open
Epilepsy is the fourth most common neurological disorder, affecting about 1% of the population at all ages. As many as 60% of people with epilepsy experience focal seizures which originate in a certain brain area and are limited to part of…