Moses Owoicho Abah | Bioinformatics in Pharmaceuticals | Best Researcher Award

Published on by Top Pharmaceutical

Moses Owoicho Abah | Bioinformatics in Pharmaceuticals | Best Researcher Award

Dr. Moses Owoicho Abah at World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia, Russia.

Dr. Moses Owoicho Abah is a passionate cancer researcher and molecular oncologist 🧪, currently pursuing a Ph.D. in Oncology/Radiation Therapy 🎓 at I.M. Sechenov First Moscow Medical University. With academic roots in Biochemistry, Bioinformatics, and Medical Biochemistry 🧠, he specializes in RNA sequencing, immunoassays, and biomarker discovery. Dr. Abah has worked across leading institutions in Nigeria and Russia 🇳🇬🇷🇺, contributing to drug discovery, genomics, and personalized oncology. He brings deep expertise in data analysis using Python and R 📊, and his impactful research is published in Scopus-indexed journals 📚. His goal is to revolutionize cancer treatment globally.

Publication Profile 

Google Scholar

Education

Dr. Moses Owoicho Abah’s educational path reflects his unwavering dedication to biomedical sciences and cancer research 🧬. He is currently pursuing a Ph.D. in Oncology/Radiation Therapy (2022–2025) at the Institute for Personalized Oncology, I.M. Sechenov First Moscow Medical University 🇷🇺. He earned his M.Sc in Medical Biochemistry (2020–2022) from the University of Abuja 🇳🇬 and completed a Professional Program in Bioinformatics (2019–2020) at MIPT, Russia. His foundation lies in a B.Sc (Hons) in Biochemistry (2010–2015) from FUAM and a Senior School Certificate from GSS Otukpo (2002–2007) 📚. His journey blends science, research, and innovation.

Experience

Since February 2025, Dr. Moses Owoicho Abah has served as a Cancer Research Coordinator at the Genetics, Genomics, and Bioinformatics Department of the National Institute for Cancer Research and Treatment, Nigeria 🇳🇬. He collaborates closely with the Principal Investigator to uphold global research standards 🌍, prepares proposals, budgets, protocols 📄, and handles informed consent and participant screening ✅. Skilled in RStudio and Python 📊🐍, he generates complex data visuals, manages study supplies, and maintains communication with stakeholders. He also revises and submits manuscripts to top journals 📚, contributing meaningfully to global cancer research.

Awards

Dr. Moses Owoicho Abah has received multiple prestigious awards for his excellence in research and academics 🎓. In July 2021, he was honored with the Associate Fellow Award (CMRf-Bioinformatics) by the Royal Society of Clinical and Academic Researchers of Nigeria (ROSCARON) 🧬. In February 2020, he emerged Overall Best Scholar in Clinical Medicine, Chemistry & Biology during the Russian Open Door Scholarship competition 🇷🇺. Earlier, in August 2018, he was conferred an Honorary Fellow Award by the Association of Economists and Statisticians of Nigeria (AESN) 📈—a testament to his cross-disciplinary brilliance.

Research Focus 

Dr. Moses Owoicho Abah’s research lies at the intersection of oncology, molecular medicine, and bioinformatics 🧪. His work focuses on cancer biology, particularly angiogenesis, cytokine signaling, and drug resistance mechanisms in lung and renal cancers 💉🩸. He explores innovative therapies targeting biomarkers using comparative transcriptomics and RNA sequencing for precision medicine. Additionally, he investigates the toxicological effects of everyday compounds, such as triclosan 🧼, and their impact on hormonal balance. His studies also include natural product pharmacology and the anti-diabetic potential of medicinal plants 🌿, bridging traditional medicine with modern therapeutic strategies.

Publication Top Notes

Qingfeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award

Published on by Top Pharmaceutical

Qingfeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award

Prof Qingfeng Chen, Guangxi University, China

Prof. Qingfeng Chen is a distinguished leader in bioinformatics, data mining, and artificial intelligence. With a Ph.D. in Computer Science from the University of Technology Sydney, he has made significant contributions to drug-target interaction prediction, RNA structure identification, and protein kinase regulation. An active academic leader, he serves as the Chairman of the Guangxi Bioinformatics Association and has held editorial roles in top journals. His extensive publication record and leadership in international conferences reflect his influence. Prof. Chen’s mentorship and dedication to advancing interdisciplinary research make him a trailblazer in his field. 📚💻🔬🌍

Publication Profile

scopus

Educational Backgrounds

Professor Qingfeng Chen holds a Doctor of Philosophy in Computer Science and Technology from the University of Technology Sydney (2004) 🎓. He earned a Master of Mathematics from Guangxi Normal University in 1998, following his Bachelor’s degree in Mathematics from the same institution in 1995 📚. His academic journey reflects a strong foundation in both mathematics and computer science, positioning him as an expert in these fields. With a distinguished academic background, Prof. Chen continues to contribute significantly to the advancement of technology and mathematical research 🔍.

Editorial Role

Prof. Qingfeng Chen is a highly regarded academic, serving as an Associated Editor for Complexity & Intelligent Systems and the Journal of Bioinformatics. He has also contributed as a Guest Editor for notable journals such as Current Protein & Peptide Science, Engineering Letters, and IAENG (International Association of Engineers). Additionally, Prof. Chen serves as a Journal Reviewer for esteemed publications, including Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Information Technology in Biomedicine, Briefing in Bioinformatics, Information Science, Knowledge and Information Systems, and IEEE Transactions on Evolutionary Computation 📚💻🔬.

Current Position

Prof. Qingfeng Chen has held several prestigious positions throughout his career. Since July 2009, he has been a Professor of Bioinformatics, Data Mining, and Artificial Intelligence at the Department of Computer and Electronic Information, Guangxi University. He has also served as an Honorary Research Fellow at La Trobe University in Australia since May 2016. In addition, he is the Chairman of the Guangxi Bioinformatics Association (since August 2022) and the Executive Deputy Director of the Biomedical Informatics Committee of the Guangxi Artificial Intelligence Society (since August 2020). Previously, he was an Honorary Visiting Professor at the University of Technology Sydney and a Research Fellow at City University of Hong Kong 🔬🎓🌍.

Conference Leadership

Prof. Qingfeng Chen has made significant contributions to the academic community, not only through his impactful research and publications but also by showcasing his leadership in organizing key conferences. He has chaired prominent events such as the 12th International Conference on Bioinformatics and Biomedical Science (ICBBS 2023), where his expertise guided discussions on bioinformatics and biomedical advancements. Additionally, he has co-chaired several other international conferences, highlighting his dedication to advancing bioinformatics and artificial intelligence. Through these efforts, Prof. Chen continues to shape and influence the future of these rapidly evolving fields 📅💡🌍.

Extensive Research

Prof. Qingfeng Chen has published a vast array of influential papers and monographs, establishing himself as a leader in bioinformatics. His groundbreaking work in drug-target interaction prediction, RNA structure identification, and protein kinase regulation is highly respected. Notable books such as Intelligent Strategies for Pathway Mining and Secure Transaction Protocol Analysis highlight his expertise in computational biology. Prof. Chen’s journal papers, covering topics like circRNA-disease association prediction and the evolution of SARS-CoV-2, are widely cited and published in prestigious journals like IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Computational Biology and Bioinformatics 📖🔬💡.

Research Focus

Professor Qingfeng Chen’s research primarily focuses on advanced computational biology, particularly in the integration and analysis of multi-omics data for precision medicine. His work involves developing machine learning frameworks, such as interpretable multitask learning models and graph convolutional networks, for predicting cancer outcomes, understanding immune responses, and improving drug-target interaction predictions. His research also explores the application of deep learning techniques to predict responses to therapies like immune checkpoint inhibitors. With a keen interest in cancer biology, his studies aim to enhance biomarker discovery and optimize therapeutic strategies. 🔬💡

Publication Top Notes

scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis

Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma

Bi-SGTAR: A simple yet efficient model for circRNA-disease association prediction based on known association pair only

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability

Entity Alignment Based on Dynamic Graph Attention and Label Propagation

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network