Machine Learning in Genomics

Bhattacharjee, Sudipto; Ghosh, Abhirupa; Saha, Banani; Saha, Sudipto

doi:10.1007/978-981-16-5993-5_4

Cited by 2 publications

(1 citation statement)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Supervised models rely on labeled training data as input [e.g., random forest (RF), logistic regression (LR)], whereas unsupervised ones use unlabeled raw data (e.g., neural network, hidden Markov model) to enable predictions/classifications given sufficient data. Machine learning models have been successfully used to predict both genomic features ( 31 ) and phenotypic traits ( 32 – 34 ) and can be used with any type of biological, including genomic, data ( 35 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Riedling,

Walker,

Rokas

2024

Microbiol Spectr

View full text Add to dashboard Cite

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed. IMPORTANCE Fungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Riedling,

Walker,

Rokas

2024

Microbiol Spectr

View full text Add to dashboard Cite

show abstract

The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network

Wang

Wang²,

Cui

et al. 2023

Electronics

View full text Add to dashboard Cite

How to use multi-dimensional time series data is a huge challenge for big data analysis. Multiple trajectories of medical use in electronic medical data are typical time series data. Although many artificial-intelligence techniques have been proposed to use the multiple trajectories of medical use in predicting the risk of concurrent medical use, most existing methods pay less attention to the temporal property of medical-use trajectory and the potential correlation between the different trajectories of medical use, resulting in limited concurrent multi-trajectory applications. To address the problem, we proposed a multi-stage neural network-based application mode of multi-dimensional time series data for feature learning of high-dimensional electronic medical data in adverse event prediction. We designed a synthetic factor for the multiple -trajectories of medical use with the combination of a Long Short Term Memory–Deep Auto Encoder neural network and bisecting k-means clustering method. Then, we used a deep neural network to produce two kinds of feature vectors for risk prediction and risk-related factor analysis, respectively. We conducted extensive experiments on a real-world dataset. The results showed that our proposed method increased the accuracy by 5%~10%, and reduced the false rate by 3%~5% in the risk prediction of concurrent medical use. Our proposed method contributes not only to clinical research, where it helps clinicians make effective decisions and establish appropriate therapy programs, but also to the application optimization of multi-dimensional time series data for big data analysis.

show abstract

Machine Learning in Genomics

Cited by 2 publications

References 97 publications

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

The Application Mode of Multi-Dimensional Time Series Data Based on a Multi-Stage Neural Network

Contact Info

Product

Resources

About