Phylogenetic convolutional neural networks Phylogenetic convolutional neural network (Ph-CNN) is a novel deep neural network architecture proposed by Fioranti et al. for clasification of Metagenomics databased on the convolutional neural networks. Their approachtries to endow Phylogenetic data with a vicinity notion using patristic distance to select k-neighborhoods for each OTU . Each OTU and his neighborhood is processed with a convolutional filter to mimic the CNN workflow . According to their published results, Ph-CNN reaches promising results compared to fully connected neural networks, random forest and support vector machines. Random forest thumbnail|Some bioinformatic applications of Random Forest.  Random forests (RF) classify by constructing an ensemble of decision trees, and outputting the average prediction of the individual trees. This is a modification of bootstrap aggregating (which aggregates a large collection of decision trees) and can be used for classification or regression. As RF gives an internal estimate of generalization error, cross-validation is unnecessary. In addition, it produces proximities, which can be used to impute missing values, and which also enable novel visualizations of the data. Random forests have been successfully used for a wide variety of applications and enjoy considerable popularity in several disciplines. Protein secondary structure prediction is a main focus of this subfield as the further protein foldings ( tertiary and quartenary structures ) are determined based on the secondary structure. Solving the true structure of a protein is an incredibly expensive and time-intensive process, furthering the need for systems that can accurately predict the structure of a protein by analyzing the amino acid sequence directly. Prior to machine learning, researchers needed to conduct this prediction manually. This trend began in 1951 when Pauling and Corey released their work on predicting the hydrogen bond configurations of a protein from a polypeptide chain. Today, through the use of automatic feature learning, the best machine learning techniques are able to achieve an accuracy of 82-84\%. The current state-of-the-art in secondary structure prediction uses a system called DeepCNF (deep convolutional neural fields) which relies on the machine learning model of artificial neural networks to achieve an accuracy of approximately 84\% when tasked to classify the amino acids of a protein sequence into one of three structural classes (helix, sheet, or coil). The theoretical limit for three-state protein secondary structure is 88–90\%. Metagenomics Metagenomics is the study of microbial communities from environmental DNA samples. Currently, there are many limitations and challenges in the implementation of machine learning tools due to the high amount of data proceeded from environmental samples. According to Lin and colleagues, machine learning requires a great computing power, but the development of fast supercomputers and web servers has made access to these tools easier nowadays. A major challenge in the characterization of differences in the microbiome composition across groups of samples is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries. Despite their importance for processing the great amount of information from environmental samples, the development of machine learning tools related to metagenomics has been focused on the study of gut microbiota and the relationship with digestive diseases, such as ; Inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and Diabetes , with the aim for obtaining better approaches of diagnosis and treatments for those pathologies. There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data  e.g. 16S rRNA or whole-genome sequencing (WGS)  by the use of methods such as ; least absolute shrinkage and selection operator classifier, Random forest, supervised classification model  and gradient boosted tree model. Recently, more advanced models have been developed by the use of neural networks . For example, recurrent neural network (RNN), convolutional neural network (CNN), and Hopfield neural network . For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks . As a result, they can establish the classification between healthy and sick patients .
Phylogenetic convolutional neural networks Phylogenetic convolutional neural network (Ph-CNN) is a novel convolutional neural network architecture proposed by Fioranti et al. to classify metagenomics data. In this approach, phylogenetic data is endowed with patristic distance (the sum of the lengths of all branches connecting two operational taxonomic units) to select k-neighborhoods for each OTU , and each OTU and its neighbors are processed with convolutional filters . According to their published results, Ph-CNN reaches promising results compared to fully connected neural networks, random forest and support vector machines. Random forest thumbnail|Some bioinformatic applications of Random Forest. |220x220px Random forests (RF) classify by constructing an ensemble of decision trees, and outputting the average prediction of the individual trees. This is a modification of bootstrap aggregating (which aggregates a large collection of decision trees) and can be used for classification or regression. As random forests give an internal estimate of generalization error, cross-validation is unnecessary. In addition, they produces proximities, which can be used to impute missing values, and which also enable novel visualizations of the data. Random forests have been successfully used for a wide variety of applications and enjoy considerable popularity in several disciplines. Protein secondary structure prediction is a main focus of this subfield as tertiary and quartenary structures are determined based on the secondary structure. Solving the true structure of a protein is an incredibly expensive and time-intensive process, furthering the need for systems that can accurately predict the structure of a protein by analyzing the amino acid sequence directly. Prior to machine learning, researchers needed to conduct this prediction manually. This trend began in 1951 when Pauling and Corey released their work on predicting the hydrogen bond configurations of a protein from a polypeptide chain. Today, through the use of automatic feature learning, the best machine learning techniques are able to achieve an accuracy of 82-84\%. The current state-of-the-art in secondary structure prediction uses a system called DeepCNF (deep convolutional neural fields) which relies on the machine learning model of artificial neural networks to achieve an accuracy of approximately 84\% when tasked to classify the amino acids of a protein sequence into one of three structural classes (helix, sheet, or coil). The theoretical limit for three-state protein secondary structure is 88–90\%. Metagenomics Metagenomics is the study of microbial communities from environmental DNA samples. Currently, there are many limitations and challenges in the implementation of machine learning tools due to the high amount of data proceeded from environmental samples. Although machine learning requires great amounts of computing power, the development of fast supercomputers and web servers has made access to these tools easier . The high dimensionality of microbiome datasets is a major challenge in characterizing differences in the microbiome composition across groups of samples ; this significantly limits the power of current approaches for identifying true differences and increases the chance of false discoveries. Despite their importance for processing the great amount of information from environmental samples, the development of machine learning tools related to metagenomics has been focused on the study of gut microbiota and the relationship with digestive diseases, such as inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and diabetes , with the aim of obtaining better approaches of diagnosis and treatments for those pathologies. There are many examples of algorithms developed to classify microbial communities according to the health condition of the host, regardless of the type of sequence data , e.g. 16S rRNA or whole-genome sequencing (WGS) , by the use of methods such as least absolute shrinkage and selection operator classifier, random forest, supervised classification model , and gradient boosted tree model. Recently, more advanced models have been developed by the use of neural networks , such as recurrent neural networks (RNN), convolutional neural networks (CNN), and Hopfield neural networks . For example, in 2018 , Fioravanti et al developed an algorithm called Ph-CNN to classify data samples from healthy patients and patients with IBD symptoms (to distinguish healthy and sick patients) by using phylogenetic trees and convolutional neural networks .
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