Tags

• ML-MS 2
• asd 1
• fmri 1
• ml-ms 1
• eeg 2
• HPC-MS 1
• software 10
• preprint 1
• ML-seizure 4
• conference 1
• workshop 2
• Grants 2
• Academia 2
• ML 4
• gene 2
• ADRD 3
• journal 3
• mri 1
• EEG 1
• Epilepsy 1
• AE 1
• ComBat 1
• ASD 1
• Multisite 1
• award 1
• funding 1
• undergraduate research 1

ML-MS

• Bilal Shabbir Presents Novel Framework for Peptide Property Prediction at ASMS 2025
• Machine-Learning and the Future of HPC for MS-Based Omics

asd

• Characterization and diagnosis of Autism Spectrum

fmri

• Characterization and diagnosis of Autism Spectrum

ml-ms

• ML Ecosystem for Mass Spectrometry Data

eeg

• MLSPred-Bench: Reference EEG Benchmark for Prediction of Epileptic Seizures
• Predicting Epileptic Seizures

HPC-MS

• HPC Engine for Mass Spectrometry based Omics Data

software

• UtilLLM_EPS
• MAESTRO
• phyNGSC
• HiCOPS & GiCOPS
• Temporal Pattern Mining (TPM) algorithm
• ProteoRift
• Mass-Simulator
• MS-Reduce
• J-EROS
• ASD-DiagNet

preprint

• Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification

ML-seizure

• Paras Parani Advances to University-Wide 3MT Competition
• Dr. Umair Mohammad Selected as an AES Fellow
• 2024 IEEE International Conference on Big Data workshop HPC-BOD Paper Acceptance
• CDMA Paper Acceptance

conference

• Utilizing Pretrained Vision Transfomers and Large Language Models for Epileptic Seizure Prediction

workshop

• Robustness of ML-Based Seizure Prediction Using Noisy EEG Data From Limited Channels
• Lightweight Transformer exhibits comparable performance to LLMs for Seizure Prediction: A case for light-weight models for EEG data

Grants

• NIH Funding Mechanisms (Research and Development) - DP grants - part 2
• NIH Funding Mechanisms (Research and Development) - part 1

Academia

• NIH Funding Mechanisms (Research and Development) - DP grants - part 2
• NIH Funding Mechanisms (Research and Development) - part 1

ML

• Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models
• MLSPred-Bench: Transforming Electroencephalography (EEG) Datasets into Machine Learning-Ready Seizure Prediction Benchmarks
• Alzheimer’s disease-associated gene ranking using PhenoGeneRanker
• Predicting Individual’s Cognitive Performance Through Multi-Omics Blood Data Using Hierarchical Input Neural Network - HINN

gene

• Alzheimer’s disease-associated gene ranking using PhenoGeneRanker
• Predicting Individual’s Cognitive Performance Through Multi-Omics Blood Data Using Hierarchical Input Neural Network - HINN

ADRD

• Predicting and Characterizing Alzheimer's Disease & Related Dementias
• Alzheimer’s disease-associated gene ranking using PhenoGeneRanker
• Predicting Individual’s Cognitive Performance Through Multi-Omics Blood Data Using Hierarchical Input Neural Network - HINN

journal

• Machine-learning models for Alzheimer’s disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation
• TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease
• Alzheimer’s disease diagnosis using gray matter of T1-weighted sMRI data and vision transformer

mri

• Predicting and Characterizing Alzheimer's Disease & Related Dementias

EEG

• MLSPred-Bench: Transforming Electroencephalography (EEG) Datasets into Machine Learning-Ready Seizure Prediction Benchmarks

Epilepsy

• MLSPred-Bench: Transforming Electroencephalography (EEG) Datasets into Machine Learning-Ready Seizure Prediction Benchmarks

AE

• Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

ComBat

• Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

ASD

• Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

Multisite

• Overcoming Site Variability in Multisite fMRI Studies: An Autoencoder Framework for Enhanced Generalizability of Machine Learning Models

award

• Anju Singh selected for OURS FIU and UR2PhD

funding

• Anju Singh selected for OURS FIU and UR2PhD

undergraduate research

• Anju Singh selected for OURS FIU and UR2PhD