Robust Sampling of Defective Pathways in Multiple Myeloma

Fernández-Martínez, Juan Luis; Andrés-Galiana, Enrique J. de; Fernández-Ovies, Francisco Javier; Cernea, Ana; Kloczkowski, Andrzej

doi:10.3390/ijms20194681

Cited by 6 publications

(2 citation statements)

References 22 publications

(28 reference statements)

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“…In this expression,

is the prior distribution for sampling the genetic signatures, and

is the likelihood of the genetic signature

, which depends on its predictive accuracy

. These algorithms have been recently applied to perform the robust sampling of the altered pathways in different diseases: Parkinson’s, Alzheimer’s, multiple sclerosis, multiple myeloma, and triple-negative cancer [ 19 , 20 , 21 , 22 , 23 ].…”

Section: Specific Problemsmentioning

confidence: 99%

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space

deAndrés-Galiana

Martínez

Fernández-Brillet³

et al. 2022

IJMS

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Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equations to have an imperfect solution. In addition, in the case of highly underdetermined parameterization, noise can be absorbed by the model, generating spurious solutions. This is a very undesirable situation that might lead to incorrect conclusions. We presented mathematical formalism based on the inverse problem theory combined with artificial intelligence methodologies to perform an enhanced sampling of noisy biomedical data to improve the finding of meaningful solutions. Random sampling methods fail for high-dimensional biomedical problems. Sampling methods such as smart model parameterizations, forward surrogates, and parallel computing are better suited for such problems. We applied these methods to several important biomedical problems, such as phenotype prediction and a problem related to predicting the effects of protein mutations, i.e., if a given single residue mutation is neutral or deleterious, causing a disease. We also applied these methods to de novo drug discovery and drug repositioning (repurposing) through the enhanced exploration of huge chemical space. The purpose of these novel methods that address the problem of noise and uncertainty in biomedical data is to find new therapeutic solutions, perform drug repurposing, and accelerate and optimize drug discovery, thus reestablishing homeostasis. Finding the right target, the right compound, and the right patient are the three bottlenecks to running successful clinical trials from the correct analysis of preclinical models. Artificial intelligence can provide a solution to these problems, considering that the character of the data restricts the quality of the prediction, as in any modeling procedure in data analysis. The use of simple and plain methodologies is crucial to tackling these important and challenging problems, particularly drug repositioning/repurposing in rare diseases.

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“…In this expression,

is the prior distribution for sampling the genetic signatures, and

is the likelihood of the genetic signature

, which depends on its predictive accuracy

Section: Specific Problemsmentioning

confidence: 99%

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space

deAndrés-Galiana

Martínez

Fernández-Brillet³

et al. 2022

IJMS

Self Cite

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“…We illustrated the importance of genomic robust sampling in precision medicine and uncertainty analysis with the analysis of the Triple Negative Breast Cancers (TNBC) phenotype. The algorithms utilized have been successfully utilized in the analysis of breast cancer and lymph node metastasis, 8,90 in Sarcopenia, 91 Multiple Sclerosis, 92 Multiple Myeloma 93 and Inclusion Body Myositis. 94 More specifically, we performed a robust sampling in order to find out the altered genetic pathways by the metastasis events.…”

Section: Case Study: Analysis Of Metastasis and Survival In Tnbc Intrmentioning

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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