EDITORIAL
Published on 03 Dec 2021
Editorial: Machine Learning Methodologies to Study Molecular Interactions
doi 10.3389/fmolb.2021.806474
- 1,959 views
- 1 citation
- Frontiers in Molecular Biosciences
- Biological Modeling and Simulation
- Research Topics
- Machine Learning Methodologies To Study Molecular Interactions
Machine Learning Methodologies To Study Molecular Interactions
16k
Total downloads
68k
Total views and downloads
Articles
ORIGINAL RESEARCH
Published on 05 Oct 2021
Performance Assessment of the Network Reconstruction Approaches on Various Interactomes
doi 10.3389/fmolb.2021.666705
- 2,646 views
- 7 citations
ORIGINAL RESEARCH
Published on 23 Sep 2021
Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data
doi 10.3389/fmolb.2021.637355
- 2,713 views
- 9 citations
ORIGINAL RESEARCH
Published on 14 Jun 2021
Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl
doi 10.3389/fmolb.2021.658906
- 3,147 views
- 7 citations
ORIGINAL RESEARCH
Published on 11 Jun 2021
N6-Methyladenosine RNA Methylation Regulator-Related Alternative Splicing (AS) Gene Signature Predicts Non–Small Cell Lung Cancer Prognosis
doi 10.3389/fmolb.2021.657087
- 6,103 views
- 25 citations
REVIEW
Published on 10 Jun 2021
Learning the Regulatory Code of Gene Expression
doi 10.3389/fmolb.2021.673363
- 16,474 views
- 29 citations
ORIGINAL RESEARCH
Published on 25 May 2021
Protein Docking Model Evaluation by Graph Neural Networks
doi 10.3389/fmolb.2021.647915
- 14,789 views
- 87 citations
ORIGINAL RESEARCH
Published on 07 May 2021
ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor
doi 10.3389/fmolb.2021.647424
- 2,900 views
- 13 citations
ORIGINAL RESEARCH
Published on 29 Apr 2021
A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins
doi 10.3389/fmolb.2021.637396
- 3,353 views
- 12 citations
PERSPECTIVE
Published on 15 Apr 2021
Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist’s Perspective
doi 10.3389/fmolb.2021.660542
- 3,397 views
- 13 citations
