Machine learning and complex biological data
By Chunming Xu and Scott A. Jackson
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics
By Chunming Xu and Scott A. Jackson
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics
BioMed Central
Machine learning and complex biological data - Genome Biology
Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
Using Deep Learning to Annotate the Protein Universe
Bileschi et al.: http://biorxiv.org/cgi/content/short/626507v1
#ArtificialIntelligence #Biology #DeepLearning #Technology
Bileschi et al.: http://biorxiv.org/cgi/content/short/626507v1
#ArtificialIntelligence #Biology #DeepLearning #Technology
bioRxiv
Using Deep Learning to Annotate the Protein Universe
Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of…
Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models
Rikhye et al.: https://www.biorxiv.org/content/10.1101/764456v1
#Biology #Neuroscience #HiddenMarkovModels
Rikhye et al.: https://www.biorxiv.org/content/10.1101/764456v1
#Biology #Neuroscience #HiddenMarkovModels
bioRxiv
Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models
Sequence learning is a vital cognitive function and has been observed in numerous brain areas. Discovering the algorithms underlying sequence learning has been a major endeavour in both neuroscience and machine learning. In earlier work we showed that by…
Deep Learning in Life Sciences
by Massachusetts Institute of Technology (MIT)
Course Site: https://mit6874.github.io/
Lecture Videos: https://youtube.com/playlist?list=PLypiXJdtIca5ElZMWHl4HMeyle2AzUgVB
We will explore both conventional and deep learning approaches to key problems in the life sciences, comparing and contrasting their power and limits. Our aim is to enable you to evaluate a wide variety of solutions to key problems you will face in this rapidly developing field, and enable you to execute on new enabling solutions that can have large impact.
As part of the subject you will become an expert in using modern cloud resources to implement your solutions to challenging problems, first in problem sets that span a carefully chosen set of tasks, and then in an independent project.
You will be programming using Python 3 and TensorFlow 2 in Jupyter Notebooks on the Google Cloud, a nod to the importance of carefully documenting your work so it can be precisely reproduced by others.
#artificialintelligence #deeplearning #tensorflow #python #biology #lifescience
by Massachusetts Institute of Technology (MIT)
Course Site: https://mit6874.github.io/
Lecture Videos: https://youtube.com/playlist?list=PLypiXJdtIca5ElZMWHl4HMeyle2AzUgVB
We will explore both conventional and deep learning approaches to key problems in the life sciences, comparing and contrasting their power and limits. Our aim is to enable you to evaluate a wide variety of solutions to key problems you will face in this rapidly developing field, and enable you to execute on new enabling solutions that can have large impact.
As part of the subject you will become an expert in using modern cloud resources to implement your solutions to challenging problems, first in problem sets that span a carefully chosen set of tasks, and then in an independent project.
You will be programming using Python 3 and TensorFlow 2 in Jupyter Notebooks on the Google Cloud, a nod to the importance of carefully documenting your work so it can be precisely reproduced by others.
#artificialintelligence #deeplearning #tensorflow #python #biology #lifescience
mit6874.github.io
Spring 2021 6.874 Computational Systems Biology: Deep Learning in the Life Sciences
Course materials and notes for MIT class 6.802 / 6.874 / 20.390 / 20.490 / HST.506 Computational Systems Biology: Deep Learning in the Life Sciences