Meta-learning framework applied in bioinformatics inference system design

Arredondo, Tomás; Ormazábal, Wladimir

doi:10.1504/ijdmb.2015.066775

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…In bioinformatics over the last 5 years, there have been studies using meta-learning approaches applied to the design of inferential systems (see for example Arredondo and Ormazábal (2015) , who developed inference systems for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps). However, these have mainly been inference procedures for deducing missing knowledge at the level of the network node rather than at the level of the arc and interaction mechanism, i.e.…”

Section: The Challenges Of the Modern Machine Learningmentioning

confidence: 99%

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Lecca

2021

Front. Bioinform.

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Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.

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Section: The Challenges Of the Modern Machine Learningmentioning

confidence: 99%

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Lecca

2021

Front. Bioinform.

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“…Meta-learning based algorithm selection has been used in a variety of domains, such as automated machine learning , natural language processing Garouani andZaysa (2022), computer vision Mohammadi et al (2019), bioinformatics Arredondo and Ormazábal (2015) and many others. These applications are mainly focused on finding the best performing model for a given data set or task.…”

Section: Related Surveysmentioning

confidence: 99%

An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

Garouani,

Ahmad,

Bouneffa

2024

Preprint

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Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparame-ters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.

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“…It has become increasingly popular in medical research, including the field of computational biology, where it is used to test and make inferences about biological data (Gardner et al, 2016). Meta-analysis also helps in the development of bioinformatic inference systems, providing accurate prediction capabilities for classification tasks (Arredondo & Ormazábal, 2015). Overall, meta-analysis is an important tool in bioinformatics for synthesizing and interpreting data from multiple studies, enabling better decision-making and advancing research in the field.…”

Section: Bioinformatic Studiesmentioning

confidence: 99%

Exploring the Impact of Meta-Analysis in Scientific Research: A Review

Patial,

Sobti

2024

MEDIN

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Meta-analysis is a well-established statistical analysis method that aims to analyze the number of different studies which are addressing the same hypothesis. This statistical analysis increases the efficiency of the output and provides us with consensus and significant results. When addressing several datasets, the relevance of the meta-analysis is revealed when some hidden feature or the results are brought up. The available literature also provides us with references to numerous meta-analysis studies in different scientific areas to provide new insight into available research with discoveries and outcomes. In our comprehensive and extensive review study, we will embark upon a thorough investigation into the pivotal role and profound impact of meta-analysis in various fields, encompassing but not limited to bioinformatics, medicine, criminology, ecology, cancer research, education, psychology, pharmaceuticals, food safety, epidemiology, neurodegenerative diseases, diabetes, and hypertension. By immersing ourselves in the intricacies and complexities of these diverse and multifarious subjects, our all-encompassing and in-depth study aims to delve deeply into the very essence of the challenges and opportunities that pervade a vast array of disciplines. From the critical realm of healthcare to the captivating and enthralling realm of social sciences, we seek to unravel the interconnected and interwoven tapestry of issues and possibilities that span across a wide spectrum of scholarly fields.

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Meta-learning framework applied in bioinformatics inference system design

Cited by 4 publications

References 27 publications

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

Exploring the Impact of Meta-Analysis in Scientific Research: A Review

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