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Keras R-CNN: library for cell detection in biological images using deep neural networks
BackgroundA common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number,...
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Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes
In this chapter, we provide the main elements for implementing deep neural networks in Keras for binary, categorical, and mixed outcomes under...
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Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes
This chapter provides elements for implementing deep neural networks (deep learning) for continuous outcomes. We give details of the hyperparameters...
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Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models
As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional...
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Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa
BackgroundAntimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs)....
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Methanol tolerance upgrading of Proteus mirabilis lipase by machine learning-assisted directed evolution
For many crucial industrial applications, enzyme-catalyzed processes take place in harsh organic solvent environments. However, it remains a...
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Convolutional Neural Networks
We provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation...
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The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines
A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to...
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Identification of the cultivars of the wheat crop from their seed images using deep learning: convolutional neural networks
The characteristics and qualities of seeds (kernels) of wheat cultivars vary, in their size, shape and texture, genetic and biochemicals properties....
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Fundamentals of Big Data, Machine Learning, and Computer Vision Workflow
This chapter serves as a foundational guide to the essential principles of big data, machine learning, and computer vision workflows. The exploration...
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LeafNet: Design and Evaluation of a Deep CNN Model for Recognition of Diseases in Plant Leaves
Leaf disease prediction is an important problem in agriculture because it impacts crop yield and quality. It is feasible to reliably predict leaf...
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Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences
In the midst of the global COVID-19 pandemic, a wealth of data has become available to researchers, presenting a unique opportunity to investigate...
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disperseNN2 : a neural network for estimating dispersal distance from georeferenced polymorphism dataSpatial genetic variation is shaped in part by an organism’s dispersal ability. We present a deep learning tool,
disperseNN2 , for estimating the mean...
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Defining cardiac functional recovery in end-stage heart failure at single-cell resolution
Recovery of cardiac function is the holy grail of heart failure therapy yet is infrequently observed and remains poorly understood. In this study, we...
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Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences
With the advent of deep learning techniques for text generation, comes the possibility of generating fully simulated or synthetic genomes. For this...
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MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks
BackgroundMass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological...
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Deep Learning for Diabetic Retinopathy Prediction
Diabetic retinopathy is a complication of diabetes mellitus. Its early diagnosis can prevent its progression and avoid the development of other major...
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Enhancing urad bean (Vigna mungo L.) crop management with machine learning: Predictive analysis of pod rot severity and pod bug incidence patterns
Urad bean ( Vigna mungo L.), commonly known as black gram, is an important pulse crop in Indian agriculture. However, the crop confronts significant...
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SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and...
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The Python Programming Language
The Python-machine learning collaboration has solidified its place in the IT and data science industries. Python is being used by a lot of market...