Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques.

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

4 citing papers in PubMed, 7 citations in OpenAlex.

1. When global and local molecular descriptors are more than the sum of its parts: Simple, But Not Simpler?Molecular diversity · 2020
   Article
2. Development of classification models for identification of important structural features of isoform-selective histone deacetylase inhibitors (class I).Molecular diversity · 2020
   Article
3. Artificial Intelligence in Hematology: Current Challenges and Opportunities.Current hematologic malignancy reports · 2020
   Review
4. Application of machine learning in the management of lymphoma: Current practice and future prospects.Digital health
   Review

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 at 2 institutions in 1 country.

Funding

No grant is acknowledged in the PubMed record.

Abstract

Indexed as