Ali Sahimi

ORCID: 0000-0001-8958-349X
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About
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Research Areas
  • Amyotrophic Lateral Sclerosis Research
  • Genetic Associations and Epidemiology
  • Prostate Cancer Treatment and Research
  • Prostate Cancer Diagnosis and Treatment
  • Neurogenetic and Muscular Disorders Research
  • Prion Diseases and Protein Misfolding
  • biodegradable polymer synthesis and properties
  • Molecular Biology Techniques and Applications
  • Cholinesterase and Neurodegenerative Diseases
  • Race, Genetics, and Society
  • Cancer-related molecular mechanisms research
  • Parkinson's Disease Mechanisms and Treatments
  • Neurological diseases and metabolism
  • Protein Structure and Dynamics
  • Inflammatory Biomarkers in Disease Prognosis
  • Extracellular vesicles in disease

New York University
2025

University of Southern California
2019-2023

Broad Center
2019

Anqi Wang Jiayi Shen Alex A Rodriguez Edward J. Saunders Fei Chen and 95 more Rohini Janivara Burcu F. Darst Xin Sheng Yili Xu Alisha Chou Sara Benlloch Tokhir Dadaev Mark N. Brook Anna Plym Ali Sahimi Thomas J Hoffman Atushi Takahashi Koichi Matsuda Yukihide Momozawa Masashi Fujita Triin Laisk Jéssica Figuerêdo Kenneth Muir Shuji Ito Xiaoxi Liu Yuji Uchio Michiaki Kubo Yoichiro Kamatani Artitaya Lophatananon Peggy Wan Caroline Andrews Adriana Lori Parichoy Pal Choudhury Johanna Schleutker Teuvo L.J. Tammela Csilla Sipeky Anssi Auvinen Graham G. Giles Melissa C. Southey Robert J. MacInnis Cezary Cybulski Dominika Wokołorczyk Jan Lubiński Christopher T. Rentsch Kelly Cho Benjamin H. McMahon David E. Neal Jenny L. Donovan Freddie C. Hamdy Richard M. Martin Børge G. Nordestgaard Sune F. Nielsen Maren Weischer Stig E. Bojesen Martin Andreas Røder Hein Vincent Stroomberg Jyotsna Batra Suzanne K. Chambers Lisa G. Horvath Judith A. Clements Wayne Tilly Gail P. Risbridger Henrik Grönberg Markus Aly Robert Szulkin Martin Eklund Tobias Nordström Nora Pashayan Alison M. Dunning Maya Ghoussaini Ruth C. Travis Timothy J. Key Elio Ríboli Jong Y. Park Thomas A. Sellers Hui-Yi Lin Demetrius Albanes Stephanie J. Weinstein Michael B. Cook Lorelei A. Mucci Edward Giovannucci Sara Lindström Peter Kraft David J. Hunter Kathryn L. Penney Constance Turman Catherine M. Tangen Phyllis J. Goodman Ian M. Thompson Robert J. Hamilton Neil E. Fleshner Antonio Finelli Marie‐Élise Parent Janet L. Stanford Elaine A. Ostrander Stella Koutros Laura E. Beane Freeman Meir Stampfer Alicja Wolk Niclas Håkansson

10.1038/s41588-023-01534-4 article EN Nature Genetics 2023-11-09

Abstract Background: Polygenic risk scores (PRS) could effectively identify individuals at higher of prostate cancer (PCa) and may have notable implications for screening. However, PRS classification depends on the comparison population. Objective: We investigated how different reference populations methods defining categories affect interpretation. Methods: evaluated a multi-ancestry PCa in 6, 194 cases 24, 693 controls from self-identified non-Hispanic White (WH), African American (AA),...

10.1158/1538-7445.am2025-7398 article EN Cancer Research 2025-04-21

Abstract The most common known cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is a hexanucleotide repeat expansion (HRE) in C9ORF72 that contributes to neurodegeneration by both loss-of-function (decreased protein levels) gain-of-function (e.g. dipeptide production) mechanisms. Although therapeutics targeting the mechanisms are clinical development, it unclear if these will be efficacious given contribution processes neurodegeneration. Moreover, there lack...

10.1101/685800 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2019-06-28

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring cancer. However, mechanism underlying tumor education is not known, transcripts associated with TEPs often tumor-associated transcripts. We demonstrated that direct transfer to circulating an unlikely source TEP signal. used CDSeq, latent Dirichlet allocation algorithm, deconvolute signal in blood samples from patients glioblastoma. substantial proportion platelet transcriptome derived...

10.1172/jci.insight.178719 article EN cc-by JCI Insight 2024-08-27

Poly-glycine-alanine (poly-GA) proteins are widely believed to be one of the main toxic dipeptide repeat molecules associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia diseases. Using discontinuous molecular dynamics simulation an all-atom model proteins, we study folding, stability, aggregation poly-GA. The results demonstrate that poly-GA is aggregation-prone protein that, after a long enough time, forms β-sheet-rich aggregates match recent experiment data two...

10.1063/1.5081867 article EN The Journal of Chemical Physics 2019-04-09
David V. Conti B.F. Darst Lilit C. Moss Edward J. Saunders X. Sheng and 95 more Alisha Chou Fredrick R. Schumacher Ali Amin Al Olama Sara Benlloch Tokhir Dadaev Mark N. Brook Ali Sahimi Thomas J. Hoffmann Atsushi Takahashi Koichi Matsuda Yukihide Momozawa Masashi Fujita Kenneth Muir Artitaya Lophatananon P. Wan Loı̈c Le Marchand Lynne R. Wilkens Victoria L. Stevens Susan M. Gapstur Brad Carter Johanna Schleutker Teuvo L.J. Tammela Csilla Sipeky Anssi Auvinen Graham G. Giles Melissa C. Southey Robert J. MacInnis Cezary Cybulski Dominika Wokołorczyk Jan Lubiński David E. Neal Jenny Donovan Freddie C. Hamdy Richard M. Martin Børge G. Nordestgaard Sune F. Nielsen Maren Weischer Stig E. Bojesen Martin Andreas Røder Peter Iversen Jyotsna Batra Suzanne K. Chambers Leire Moya Lisa G. Horvath Judith A. Clements Wayne D. Tilley Gail P. Risbridger Henrik Grönberg Markus Aly Robert Szulkin Martin Eklund Tobias Nordström Nora Pashayan Alison M. Dunning Maya Ghoussaini Ruth C. Travis Timothy J. Key Elio Ríboli J.Y. Park Thomas A. Sellers Hui‐Yi Lin Demetrius Albanes Stephanie J. Weinstein Lorelei A. Mucci Edward Giovannucci Sara Lindström Peter Kraft David J. Hunter Kathryn L. Penney C. Turman Catherine M. Tangen Phyllis J. Goodman Ian M. Thompson Robert J. Hamilton Neil E. Fleshner Antonio Finelli Marie‐Élise Parent Janet L. Stanford Elaine A. Ostrander Milan S. Geybels Stella Koutros Laura E. Beane Freeman Meir J. Stampfer Alicja Wolk Niclas Håkansson Gerald L. Andriole Robert N. Hoover Mitchell J. Machiela Karina D. Sørensen Michael Borre William J. Blot Wei Zheng Edward D. Yeboah James E. Mensah Yong‐Jie Lu

10.17615/3g7m-ta66 article EN Carolina Digital Repository (University of North Carolina at Chapel Hill) 2021-01-01

We propose a machine learning (ML) method to classify ALS–causative and non–ALS–causative variants based on 24 variables in five different datasets. The proposed ML classifies the datasets with very high accuracy. In particular, it predicts ALS 100 percent accuracy, while its accuracy for non-ALS is up 99.31 percent. trained classifier also identifies nine most influencial mutation assessors that help distinguishing two classes from each other. They are FATHMM_score, PROVEAN_score,...

10.1101/2022.03.27.485996 preprint EN cc-by-nc-nd bioRxiv (Cold Spring Harbor Laboratory) 2022-03-28
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