activity
20152025
most citedA Semi-Supervised Method for Predicting Cancer Survival Using Incomplete Clinical Data

3 citations · 7 across the 10 of their papers we have counts for

collaborators

12 papers

eess.IV2025

A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis

Reihaneh Hassanzadeh, Anees Abrol, Hamid Reza Hassanzadeh +1

Multimodal data analysis can lead to more accurate diagnoses of brain disorders due to the complementary information that each modality adds. However, a major challenge of using mu…

cs.LG2025

Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records

Ekaterina Redekop, Zichen Wang, Rushikesh Kulkarni +7

Longitudinal data in electronic health records (EHRs) represent an individual`s clinical history through a sequence of codified concepts, including diagnoses, procedures, medicatio…

q-bio.NC2024★ 1 cited

Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers

Reihaneh Hassanzadeh, Anees Abrol, Hamid Reza Hassanzadeh +1

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the a…

cs.CL2021★ 1 cited

Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations

Longxiang Zhang, Renato Negrinho, Arindam Ghosh +4

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long…

cs.CL2017

DeepDeath: Learning to Predict the Underlying Cause of Death with Big Data

Hamid Reza Hassanzadeh, Ying Sha, May D. Wang

Multiple cause-of-death data provides a valuable source of information that can be used to enhance health standards by predicting health related trajectories in societies with larg…

q-bio.QM2017

MotifMark: Finding Regulatory Motifs in DNA Sequences

Hamid Reza Hassanzadeh, Pushkar Kolhe, Charles L. Isbell +1

The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing,…

cs.AI2016

Fuzzy Constraints Linear Discriminant Analysis

Hamid Reza Hassanzadeh, Hadi Sadoghi Yazdi, Abedin Vahedian

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion tha…

eess.SY2016★ 1 cited

A New Type-II Fuzzy Logic Based Controller for Non-linear Dynamical Systems with Application to a 3-PSP Parallel Robot

Hamid Reza Hassanzadeh

The concept of uncertainty is posed in almost any complex system including parallel robots as an outstanding instance of dynamical robotics systems. As suggested by the name, uncer…

cs.CV2016★ 1 cited

DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins

Hamid Reza Hassanzadeh, May D. Wang

Transcription factors (TFs) are macromolecules that bind to \textit{cis}-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of…

cs.LG2016

A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival

Hamid Reza Hassanzadeh, John H. Phan, May D. Wang

Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widel…

cs.GT2016

A Multi-Dimensional Fairness Combinatorial Double-Sided Auction Model in Cloud Environment

Reihaneh Hassanzadeh, Ali Movaghar, Hamid Reza Hassanzadeh

In cloud investment markets, consumers are looking for the lowest cost and a desirable fairness while providers are looking for strategies to achieve the highest possible profit an…

cs.LG2015★ 3 cited

A Semi-Supervised Method for Predicting Cancer Survival Using Incomplete Clinical Data

Hamid Reza Hassanzadeh, John H. Phan, May D. Wang

Prediction of survival for cancer patients is an open area of research. However, many of these studies focus on datasets with a large number of patients. We present a novel method…