Sensitivity analysis of gene ranking methods in phenotype prediction

deAndrés-Galiana, Enrique J.; Fernández-Martínez, Juan Luis; Sonis, Stephen T.

doi:10.1016/j.jbi.2016.10.012

Cited by 22 publications

(23 citation statements)

References 31 publications

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“…These gene-ranking methods and particularly Fisher's ratio turned to be very robust against different kind of noise [24]. Genes with the highest discriminatory power as described by these methods are expected to be involved in the genesis of the CLL.…”

Section: Methodsmentioning

confidence: 99%

“…The methodology tries to determine the shortest lists of most discriminatory genes that predict the NOP16 mutation and is described by Fernández-Martínez et al [20] and De Andrés-Galiana et al [22][23][24]. This classification problem is naturally unbalanced due to the low number of patients that show the NOP16 mutation, and the classifier has to take this feature into account.…”

Section: Methodsmentioning

confidence: 99%

“…We have used the Fisher's Ratio and Fold Change to rank the genes according to their discriminatory power [20,[22][23][24]. These gene-ranking methods and particularly Fisher's ratio turned to be very robust against different kind of noise [24].…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia

Jl¹,

Ej²,

Cernea³

2017

J Mol Genet Med

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NOP16 was the third most important mutation (6.84%) in a reduced-size cohort of 117 patients with Chronic Lymphocytic Leukemia (CLL) whose most recurrent mutations were NOTCH1 (9.4%) and SF3B1 (8.55%). In this paper we analyzed the effect of the NOP16 mutation in gene expression. The NOP16 mutation was predicted with 100% accuracy using a small-scale signature formed by the 26 genes with the highest Fisher's Ratio. SLC39A4 (ZIP4) and WARS are the most discriminatory genes of this mutation providing a predictive accuracy of 97.4%. The Fold Change analysis also confirmed a very important role of the light (IGKV3D-11, IGKC and IGLJ3) and heavy (IGHG1) chain immunoglobulins, SOX11, CCND1 and CHL1. This analysis also highlights the importance of several mechanisms such as the ZIP4-related apoptosis, the SOX11-CCND1 over expression relationship observed in mantle cell-lymphoma, and CHL1 down regulation and over expression of the midkine-neurite growth-promoting factor that enhances the angiogenic and proliferative activities of cancer cells in different types of solid cancers. Besides, the holdout stability analysis has shown the importance of Signaling Events of B Cell Receptor (BCR), P53 signaling, Infectious disease, and TGF-beta Receptor Signaling. The integration of the NOP16 mutation with the IgHV, NOTCH1 and SF3B1 mutations, that were previously analyzed, confirmed that these mutations only share two high discriminatory genes: IGHG1 and RGS13. These genes are involved in different mechanisms concerning signaling and the immunological system. This analysis opens novel working hypothesis for CLL treatment and prognosis.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia

Jl¹,

Ej²,

Cernea³

2017

J Mol Genet Med

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“…In another attempt to tackle noise in microarray reading, authors used microarray synthetic modeling to compare different gene ranking methods (fold change, Fisher's ratio, percentile distance, and entropy) and significance analysis of microarrays. They used the aforementioned ALS and IBM datasets and concluded that the Fisher ratio was the most precise method with the highest discriminatory power [59].…”

Section: Disease Mechanismsmentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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“…Besides, due to the high underdetermined character of the phenotype prediction problems, their associated uncertainty space has a very high dimension, and the characterization of the involved biological pathways is very ambiguous because there exist many equivalent genetic networks that predict the phenotype with similar accuracies. [75][76][77] The important working hypothesis is that by sampling the uncertainty space of the phenotype prediction problems, we are able to understand the altered genetic pathways of the disease in order to use this knowledge in precision medicine for diagnosis, prognosis, and treatment optimization. Different interesting methods were proposed by Cernea et al 8 and successfully applied in the analysis of Triple Negative Breast Cancer metastasis, comparing the results obtained with Bayesian networks.…”

Section: Ai Genomics and The Phenotype Prediction Problemmentioning

confidence: 99%

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Álvarez-Machancoses¹,

deAndrés-Galiana²,

Cernea³

et al. 2020

PGPM

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The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

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Sensitivity analysis of gene ranking methods in phenotype prediction

Cited by 22 publications

References 31 publications

The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia

The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

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