Anubha Dey | Computational Biology | Best Researcher Award

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Ms. Anubha Dey | Computational Biology | Best Researcher Award

 PHD, University of Hyderabad India

Anubha Dey is a dedicated Ph.D. researcher at the University of Hyderabad with a strong background in computational biology and machine learning. She has contributed significantly to understanding genetic interactions and cancer research through advanced modeling techniques. With a passion for teaching and mentoring, Anubha aspires to inspire future leaders in scientific innovation.

Professional Profile 

Scopus

Scholar

πŸŽ“Education

Ph.D. (Pursuing), University of Hyderabad, 2019-present. Master of Technology, Anna University, 2017-2019. Bachelor of Technology, Maulana Abul Kalam Azad University of Technology, West Bengal

πŸ’ΌExperience

Researcher: Developing machine learning models for cancer biology and genetic interactions. Assistant System Engineer-Trainee, Tata Consultancy Services, 2016-2017. Presented findings at renowned conferences like ICGA and CSHL

πŸ†Award and Honor

Best Oral Presentation, BioAnveshna 2024. Best Poster Presentation, International Cancer Symposium 2022. Selected for HySci 2024 Oral Talk, IIT Hyderabad. Qualified JEST 2019, a prestigious entrance test

πŸ”¬ Research Focus

Machine learning in genetic interaction prediction. Synthetic lethal interactions in cancer. Transcript complexity and RNA splicing. Chromatin loop dynamics and epigenetics

Publications Top Noted

  • Title: Chromatin loop dynamics during cellular differentiation are associated with changes to both anchor and internal regulatory features
    Year: 2023
    Authors: ML Bond, ES Davis, IY Quiroga, A Dey, M Kiran, MI Love, H Won
    Citation: Genome Research 33 (8), 1258-1268

 

  • Title: Inefficient splicing of long non-coding RNAs is associated with higher transcript complexity in human and mouse
    Year: 2023
    Authors: K Basu, A Dey, M Kiran
    Citation: RNA Biology 20 (1), 563-572

 

  • Title: MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network
    Year: 2024
    Authors: A Dey, S Mudunuri, M Kiran
    Citation: PLOS Computational Biology 20 (8), e1012336

 

  • Title: CaTCH: Calculating transcript complexity of human genes
    Year: 2024
    Authors: K Koushiki Basu, Anubha Dey
    Citation: Methodx

 

 

Mohammadsadeq Mottaqi | Computational biology | Young Scientist Award

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Mr. Mohammadsadeq Mottaqi | Computational biology | Young Scientist Award

MohammadSadeq Mottaqi is a dedicated Ph.D. candidate in Biochemistry at the City University of New York, with a strong foundation in Biotechnology from the University of Tehran. He possesses a robust skill set in Python, R, and various bioinformatics tools. His research focuses on machine learning-based drug action prediction, molecular dynamics simulations, and protein-ligand interactions. He has contributed significantly to research projects at Hunter College and Weill Cornell Medicine, and has industry experience as an R&D intern. MohammadSadeq is a published author, with works in journals like the Journal of Environmental Management and Informatics in Medicine Unlocked.

Profile

Orcid

Scholar

EducationπŸŽ“

City University of New York, the Graduate Center, New York, NY PhD in Biochemistry, Computer Science Department
Aug 2022 – Present. University of Tehran, Tehran, Iran B.Sc. in Biotechnology, GPA: 3.8 (top 15%) Sep 2017 – March 2022

 Technical SkillsπŸ’»

Programming Languages: Python, R, Bioinformatics Tools and Libraries: PyMol, MOE, Gromacs, Spss, Unity, Scikit-learn, Data Analysis: Transcriptomics data analysis, Statistical analysis, Data Visualization, Operating Systems: Linux, Unix, Others: High-performance computing

 Relevant Experience 🌟

Machine learning-based prediction methods for drug actions
Prof. Lei Xie lab, Hunter College and Weill Cornell Medicine
June 2023 – Present
Thesis: Deep learning models for chemical-induced differential gene expression and cell viability predictions for de novo drugs and cell lines

  • Resolve challenges in predictive modeling using algorithmic approaches
  • Design pre-processing pipelines for Transcriptomics datasets
  • Integrate disparate Transcriptomics datasets (80 cell-lines) using Python
  • Incorporate multiple diverse labeled and unlabeled OMICS data
  • Contribute to writing manuscript

Molecular dynamics simulations of mutated HER2 protein
Research assistant – Dr. Mateusz Marianski lab, Hunter College
April 2023 – June 2023

  • Investigated coupling reactions and residue interactions in several mutated HER2 proteins
  • Performed molecular dynamics (MD) simulations employing Gromacs
  • Analyzed the data of the MD simulations
  • Presented the interpreted results

Protein-Ligand interactions in docking
Research assistant – Prof. Tom Kurtzman lab, Lehman College
Nov 2022 – Jan 2023

  • Identified the potential molecular interactions between protein pharmacophores and ligands
  • Wrote Python scripts in PyMOL for detection of potential H-bonds and Van der Waals forces

Aral Teb CO. Ltd., Tehran, Iran
R&D Intern
May 2020 – Oct 2020

  • Designed and executed experiments for a bacterial diagnostic kit
  • Converted 3D MRI data into a format compatible with 3D bioprinter
  • Served as a key researcher in the clean room

Virtual reality (VR) project, Iran’s National Elites Foundation
Nov 2020 – Aug 2021

  • Devised 7 biotechnological experiments in VR environments employing Unity software
  • Collaborated with 9 computer scientists and 4 biologists

Publications Top Notes πŸ“

  • Iman Kianian, Mohammad Sadeq Mottaqi, et al. Automated identification of toxigenic cyanobacterial genera for water quality control purposes, Journal of Environmental Management

 

  • Mohammad Sadeq Mottaqi, et al. Contribution of machine learning approaches in response to SARS-CoV-2 infection, Informatics in Medicine Unlocked, 2021