Evidence map›Paper›PMID 38352790›Full record

ArticleHeliyon2024

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.

Erum Yousef Abbasi, Zhongliang Deng, Qasim Ali, Adil Khan, Asadullah Shaikh, Mana Saleh Al Reshan, Adel Sulaiman, Hani Alshahrani

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
10.0field-weighted citation impact, top 1% of its field
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

14 citing papers in PubMed, 40 citations in OpenAlex.

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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

8 authors at 3 institutions in 3 countries.

Erum Yousef AbbasiState Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Zhongliang DengState Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Qasim AliDepartment of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.
Adil KhanState Key Laboratory of Wireless Network Positioning and Communication Engineering Integration Research, School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Asadullah ShaikhDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.
Mana Saleh Al ReshanDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.
Adel SulaimanDepartment of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.
Hani AlshahraniDepartment of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.
Najran University · SABeijing University of Posts and Telecommunications · CNMehran University of Engineering and Technology · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.

Indexed as

Deep learningGenomicsLeukemiaMachine learningMulti-omics

Identifiers

PMID38352790
PMCPMC10862685
OpenAlexW4391482192

What OpenQuestion 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 OpenQuestion graph.