Evidence map›Paper›PMID 37461685›Full record

ArticlebioRxiv : the preprint server for biology2023

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis.

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, Ali Hashemi Gheinani

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Akshay AkshayFunctional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland.
Mitali KatochInstitute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Navid ShekarchizadehDepartment of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany.
Masoud AbediDepartment of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany.
Ankush SharmaKG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway.
Fiona C BurkhardFunctional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland.
Rosalyn M AdamUrological Diseases Research Center, Boston Children's Hospital, MA, USA.
Katia MonastyrskayaFunctional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland.
Ali Hashemi GheinaniFunctional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland.

Funding

NIMBLE: Non-Invasive Markers of Bladder DeteriorationR01DK127673 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI ADAM, ROSALYN M, LEE, RICHARD SANG-YONG · 2021 to 2025
$2.8M
NIDDK NIH HHS R01 DK127673
6 · The paper itself

Abstract

Background: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. Results: To address these challenges, we have developed a novel tool called Conclusion: MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Indexed as

AutoMLClassification problemsData analysisMachine learningVisualization

Identifiers

PMID37461685
PMCPMC10349995

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.