Feixiong Cheng

Molecular Biology · 2010–2022

A researcher in the Science corpus: 16 papers; trusts 12 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 286 citations into it; #3,053 of 17,773 by trust here, #4,339 by citations
• Computational Theory and Mathematics — 90 citations into it; #2,932 of 12,182 by trust here, #845 by citations
• Genetics — 82 citations into it
• Artificial Intelligence — 50 citations into it
• Pharmacology — 34 citations into it; #74 of 1,112 by trust here, #175 by citations
• Medical & Health AI — 11 citations into it
• Radiology, Nuclear Medicine and Imaging — 10 citations into it
• Toxicology — 10 citations into it

Whom Feixiong Cheng trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Nicholas P. Tatonetti
2. Corinna Cortes
3. Albert-László Barabási
4. Craig Knox
5. Assaf Gottlieb
6. A. Hopkins
7. Yoshihiro Yamanishi
8. Patrick Ye
9. Minoru Kanehisa
10. Vladimir Naumovich Vapnik

Who trusts Feixiong Cheng overall

1. Marinka Zitnik
2. Karen A. Ryall
3. Jae Yong Ryu
4. Michelle M. Li
5. Tengfei Ma
6. Yadi Zhou
7. Wen Zhang
8. Mengying Sun
9. Aik Choon Tan
10. Priyanka Banerjee

Papers from Feixiong Cheng's perspective

Ordered by how strongly the researchers Feixiong Cheng trusts endorse each paper. Same corpus, a different order for every researcher.

1. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs
2. Drug Target Identification Using Side-Effect Similarity
3. From genomics to chemical genomics: new developments in KEGG (Nucleic Acids Res.)
4. SuperTarget and Matador: resources for exploring drug-target relationships (Nucleic Acids Res.)
5. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders
6. Data-Driven Prediction of Drug Effects and Interactions
7. DrugBank: a knowledgebase for drugs, drug actions and drug targets
8. Predicting new molecular targets for known drugs
9. Visualizing Data using t-SNE
10. Network Medicine: A Network-based Approach to Human Disease

Their papers

• Network-based approach to prediction and population-based validation of in silico drug repurposing (2018) — 487 citations
• Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (2012) — 759 citations
• Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. (2014) — 333 citations
• Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients (2018) — 792 citations
• admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties (2012) — 1,969 citations
• Comprehensive Characterization of Cancer Driver Genes and Mutations (2018) — 2,007 citations
• deepDR: a network-based deep learning approach to in silico drug repositioning (2019) — 460 citations
• Network-based prediction of drug combinations (2019) — 699 citations
• Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2 (2020) — 1,405 citations
• Estimation of ADME Properties with Substructure Pattern Recognition (2010) — 312 citations
• Artificial intelligence in COVID-19 drug repurposing (2020) — 413 citations
• Shape shifting: The multiple conformational substates of the PTEN N‐terminal PIP2 ‐binding domain (2022) — 11 citations
• Multimodal machine learning in precision health: A scoping review (2022) — 480 citations
• Pathogenic germline variants in 10,389 adult cancers (2018) — 734 citations
• The Immune Landscape of Cancer (2018) — 4,752 citations
• Oncogenic Signaling Pathways in The Cancer Genome Atlas (2018) — 2,663 citations