Evidence map›Paper›PMID 41162974›Full record

ReviewBMC biology2025

Machine learning in biological research: key algorithms, applications, and future directions.

Md Nafis Ul Alam, Kiran Basava, Ani Chitransh, H M Abdul Fattah, Hector D Garcia-Verdugo, Shih-Hsuan Lo, Tanisha Lohchab, Kristen M Martinet, Cristian Román-Palacios, Jhan Carlos Salazar and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Md Nafis Ul AlamData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Kiran BasavaData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Ani ChitranshData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
H M Abdul FattahData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Hector D Garcia-VerdugoData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Shih-Hsuan LoData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Tanisha LohchabData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Kristen M MartinetData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Cristian Román-PalaciosData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA. cromanpa@arizona.edu.
Jhan Carlos SalazarData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.
Danielle Van BoxelData Diversity Lab, College of Information Science, University of Arizona, Tucson, AZ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning is a robust framework to analyze questions using complex data in a variety of fields. We present definitions and recent applications of four key machine learning methods and discuss their advantages and challenges in biological research. Through a set of systematically selected case studies, we highlight how machine learning models have been used in a range of applications, including phylogenomics, disease prediction, and host taxonomy prediction. We identify additional potential areas of integration of machine learning into questions with biological relevance. This intersection can be further enhanced through collaboration and innovation on parallelization, interpretability, and preprocessing.

Indexed as

AlgorithmsMachine LearningHumansBiologyData scienceLarge datasetsLearning

Identifiers

PMID41162974
PMCPMC12574268

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.