Machine Learning for Bioinformatics

Shastry, K. Aditya; Sanjay, H. A.

doi:10.1007/978-981-15-2445-5_3

Cited by 38 publications

(20 citation statements)

References 47 publications

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“…As there is no literature that exists on the unique opportunities and challenges associated with computational data-driven research, we hope that our study will not only offer a unique insight into computational data-driven research, but also encourage computational scientists to utilize open omics data and novel machine learning approaches to classify, model, and manage biological data in order to enable high impact discoveries in the biomedical field 38 . We have discussed how computational research has increased opportunities in the biomedical research community and how open omics data in conjunction with effective bioinformatics methods can enable novel biological discoveries.…”

Section: Discussionmentioning

confidence: 99%

Systematic overview of challenges and opportunities of computational data-driven research in biology

Chhugani¹,

Jönsson²,

Mangul³

2020

Preprint

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During the past decade, the rapid advancement of high-throughput technologies has reshaped modern biomedical research by vastly extending the diversity, richness, and availability of data and methods across various domains. Currently, computational researchers are empowered with data, methods, and tools that allow for the possibility of making important contributions in biomedicine –– through primary analysis of pre-clinical and clinical datasets, the application and development of novel machine learning algorithms towards task automation and diagnostic or treatment predictions, and secondary analysis of existing public omics data. Here we discuss the challenges and pitfalls researchers from dry labs are facing and how they are gaining independence and leading high impact projects.

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Section: Discussionmentioning

confidence: 99%

Systematic overview of challenges and opportunities of computational data-driven research in biology

Chhugani¹,

Jönsson²,

Mangul³

2020

Preprint

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“…Machine learning (ML) is a fast-growing branch of AI which is making its way into biomedicine [19] , [21] , [22] . This branch of AI attempts to use algorithms to design machines for learning and predicting without explicitly planning.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 diagnosis from routine blood tests using artificial intelligence techniques

Rikan

Azar

Ghafari

et al. 2022

Biomedical Signal Processing and Control

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“…Due to their ability to discern complex patterns among a large number of features in big datasets, machine learning (ML) methods have found favor in various applications of computational biology and bioinformatics ( Shastry and Sanjay, 2020 ) including the prediction of microbe-host molecular interactions. A variety of supervised and unsupervised methods have been used to predict the interactions between microbial and host proteins ( Table 2 ).…”

Section: Classification Of Computational Methods In Microbiome-host Interactionsmentioning

confidence: 99%

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

et al. 2021

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The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

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Machine Learning for Bioinformatics

Cited by 38 publications

References 47 publications

Systematic overview of challenges and opportunities of computational data-driven research in biology

Systematic overview of challenges and opportunities of computational data-driven research in biology

COVID-19 diagnosis from routine blood tests using artificial intelligence techniques

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

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