Evidence map›Paper›PMID 41998014›Full record

ArticleScientific reports2026

A multi-task learning approach combining regression and classification tasks for joint feature selection.

Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Manuel Brenner, Florian Hess, Roman Schefzik, Daniel Durstewitz, Georgia Koppe, Emanuel Schwarz and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Han CaoDepartment of Theoretical Neuroscience, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Sivanesan RajanDepartment of Psychiatry and Psychotherapy, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Bianka HahnDepartment of Anesthesiology, Surgical Intensive Care Medicine and Pain Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Ersoy KocakDepartment of Psychiatry and Psychotherapy, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Manuel BrennerInterdisciplinary Center for Scientific Computing, Heidelberg University, Mathematikon Im Neuenheimer Feld 205, 69120, Heidelberg, Germany.
Florian HessDepartment of Theoretical Neuroscience, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Roman SchefzikDepartment of Psychiatry and Psychotherapy, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Daniel DurstewitzDepartment of Theoretical Neuroscience, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Georgia KoppeDepartment of Theoretical Neuroscience, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.
Emanuel Schwarz *Department of Psychiatry and Psychotherapy, Medical Faculty, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany. emanuel.schwarz@zi-mannheim.de.
Verena Schneider-Lindner *Department of Anesthesiology, Surgical Intensive Care Medicine and Pain Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. verena.schneider-lindner@medma.uni-heidelberg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms, and has been widely applied in the biomedical analysis for shared biomarker identification. Although MTL has successfully supported either regression or classification tasks, incorporating mixed types of tasks into a unified MTL framework remains challenging, especially in biomedicine, where it can lead to biased biomarker identification. To address this issue, we propose an improved method of multi-task learning, MTLComb, which balances the weights of regression and classification tasks to promote unbiased biomarker identification. We demonstrate the algorithmic efficiency and clinical utility of MTLComb through analyses on both simulated data and actual biomedical studies pertaining to sepsis and schizophrenia. The code is available at https://github.com/transbioZI/MTLComb .

Indexed as

Machine LearningAlgorithmsBiomarkersClassification AlgorithmsHumansRegression AnalysisSchizophreniaSepsisBiomarkersBiomarker identificationMachine learningMolecular psychiatrySchizophreniaSepsis

Identifiers

PMID41998014
PMCPMC13090349

What OpenQuestion holds

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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.