ReviewEXCLI journal2018
Unraveling the bioactivity of anticancer peptides as deduced from machine learning.
Review in EXCLI journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
60 citing papers in PubMed.
- Next-Generation Anticancer Peptides: Engineering, Nanotheranostics and Clinical Translation.Nanotheranostics · 2026Review
- Antiproliferative Effect of Okra Seed Protein Isolate against HepG2 and A549 Cell Lines.Chemistry & biodiversity · 2026Article
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- Assessment of the effect of pomegranate seed extracts on prostate cancer through cellular and animal models.Scientific reports · 2025Article
- Exploring the potential of anticancer peptides as therapeutic agents for cancer treatment.Research in pharmaceutical sciences · 2025Review
- Anticancer effects of pomegranate-derived peptide PG2 on CDK2 and miRNA-339-5p-mediated apoptosis via extracellular vesicles in acute leukemia.Scientific reports · 2024Article
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
- Classification models for predicting the bioactivity of pan-TRK inhibitors and SAR analysis.Molecular diversity · 2024Article
- Article
- Peptidome Profiling ofAntibiotics (Basel, Switzerland) · 2024Article
- Bionanofactory for green synthesis of collagen nanoparticles, characterization, optimization, in-vitro and in-vivo anticancer activities.Scientific reports · 2024Article
- Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides.BioImpacts : BI · 2024Article
- PepT1-targeted nanodrug based on co-assembly of anti-inflammatory peptide and immunosuppressant for combined treatment of acute and chronic DSS-induced ColitiS.Frontiers in pharmacology · 2024Article
- Short Fragmented Peptides fromCurrent drug discovery technologies · 2024Article
- Peptides as Therapeutic Agents: Challenges and Opportunities in the Green Transition Era.Molecules (Basel, Switzerland) · 2023Review
- Proteome of monocled cobra (Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023Article
- Changes in Length and Positive Charge of Palindromic Sequence RWQWRWQWR Enhance Cytotoxic Activity against Breast Cancer Cell Lines.ACS omega · 2023Article
- Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics.International journal of peptide research and therapeutics · 2023Review
- Tuna trimmingsCurrent research in food science · 2023Article
- Computer-Aided Screening for Potential Coronavirus 3-Chymotrypsin-like Protease (3CLpro) Inhibitory Peptides from Putative Hemp Seed Trypsinized Peptidome.Molecules (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.