Evidence map›Paper›PMID 38099958›Full record

ArticleInterdisciplinary sciences, computational life sciences2024

Hessian Regularized

Guo-Sheng Han, Qi Gao, Ling-Zhi Peng, Jing Tang

Abstract read
PubMed Publisher
In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2024. 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. 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

4 authors.

Guo-Sheng HanDepartment of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China. hangs@xtu.edu.cn.ORCID 0000-0002-7712-6213
Qi Gao *Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.
Ling-Zhi Peng *Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.
Jing Tang *Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.

Funding

Hunan Provincial Key Research Program 2022WK2009Key Foundation of Hunan Educational Committee 19A497Natural Science Foundation of Hunan Province of China 2021JJ30684
6 · The paper itself

Abstract

Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized

Indexed as

Deep LearningLung NeoplasmsMicroRNAsAlgorithmsComputational BiologyGenetic Predisposition to DiseaseHumansROC CurveMicroRNAsDeep learningMatrix factorizationmiRNA–disease associationsSimilarity calculationSingular value decomposition

Identifiers

PMID38099958

What OpenQuestion holds

Textmetadata
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.