Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction.

A S Jereesh, V K Govindan

Open access · hybridAbstract read

In one paragraph

Article in 3 Biotech, 2016. 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, top 92% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 2 institutions in 1 country.

A S JereeshDepartment of Computer Science, Cochin University of Science and Technology, Cochin, Kerala, India. jereesh@cusat.ac.in.

V K GovindanDepartment of Computer Science and Engineering, Indian Institute of Information Technology Pala, Kottayam, Kerala, India.

Cochin University of Science and Technology · INIndian Institute of Information Technology and Management, Kerala · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bio-inspired algorithms are widely used to optimize the model parameters of GRN. In this paper, focus is given to develop improvised versions of bio-inspired algorithm for the specific problem of reconstruction of gene regulatory network. The approach is applied to the data set that was developed by the DNA microarray technology through biological experiments in the lab. This paper introduced a novel hybrid method, which combines the clonal selection algorithm and BFGS Quasi-Newton algorithm. The proposed approach implemented for real world E. coli data set and identified most of the relations. The results are also compared with the existing methods and proven to be efficient.

Indexed as

BFGS Quasi-NewtonClonal selection algorithmDNA microarrayGene regulatory networkImmuno-hybrid algorithmOptimization algorithm

Identifiers

PMID28330294

DOI10.1007/s13205-016-0536-1

PMCPMC5065543

OpenAlexW2531664838

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.