ArticleMethods in molecular biology (Clifton, N.J.)2024
Design of Bioengineered Peptides/Proteases as Anti-cancer Reagents with Integrated Omics and Machine Learning Approaches.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 2 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a heterogeneous disorder of uncontrolled growth of cells, which has proven to be a major burden worldwide. Many treatment options are available for cancer therapy, yet side effects and drug resistance remain major hurdles. Therefore, it is necessary to develop novel drugs for cancer therapy. Anti-cancer peptides (ACPs) are attractive candidates with remarkable potency, low toxicity, and high specificity advantages. However, traditional experimental identification of ACPs is time-consuming and expensive. Integrated omics combined with machine learning (ML) is considered a new powerful and cost-effective strategy to discover ACPs from natural products. In this chapter, we describe in detail experimental procedures for collecting both transcriptomic and proteomic data from venoms, followed by descriptive approaches to ML prediction.
Indexed as
Identifiers
What OpenQuestion holds
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.