scholarly journals Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

2016 ◽  
Author(s):  
Michael P. Pound ◽  
Alexandra J. Burgess ◽  
Michael H. Wilson ◽  
Jonathan A. Atkinson ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analysis ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Plant Phenotyping ◽  
Learning Approaches ◽  
Challenging Problem ◽  
Feature Identification ◽  
Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

scholarly journals Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Primož Godec ◽  
Matjaž Pančur ◽  
Nejc Ilenič ◽  
Andrej Čopar ◽  
Martin Stražar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Data Analysis ◽  
Protein Localization ◽  
Visual Programming ◽  
Developmental Competence ◽  
Image Clustering ◽  
Small Scale ◽  
Learning Approaches

Abstract Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

scholarly journals Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2075
Author(s):  
Óscar Apolinario-Arzube ◽  
José Antonio García-Díaz ◽  
José Medina-Moreira ◽  
Harry Luna-Aveiga ◽  
Rafael Valencia-García
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Interfaces ◽  
State Of The Art ◽  
Learning Approaches ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Intended Meaning ◽  
Language User ◽  
Learning Architectures

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

scholarly journals Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

GigaScience ◽  
2017 ◽  
Vol 6 (10) ◽  
Author(s):  
Michael P. Pound ◽  
Jonathan A. Atkinson ◽  
Alexandra J. Townsend ◽  
Michael H. Wilson ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Plant Phenotyping ◽  
Art Performance

A Review of Deep Machine Learning

2016 ◽  
Vol 24 ◽  
pp. 124-136 ◽  
Author(s):  
Ben Bright Benuwa ◽  
Yong Zhao Zhan ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Frank Banaseka Kataka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Algorithm Design ◽  
Prior State ◽  
Art Techniques

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.

scholarly journals Attentive deep learning-based tumor-only somatic mutation classifier achieves high accuracy agnostic of tissue type and capture kit.

2021 ◽  
Author(s):  
R. Tyler McLaughlin ◽  
Maansi Asthana ◽  
Marc Di Meo ◽  
Michele Ceccarelli ◽  
Howard J. Jacob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Variant Calling ◽  
Learning Model ◽  
Tissue Type ◽  
Germline Variants ◽  
Art Performance ◽  
Deep Learning Model ◽  
Tumor Dna

In precision oncology, reliable identification of tumor-specific DNA mutations requires sequencing tumor DNA and non-tumor DNA (so-called "matched normal") from the same patient. The normal sample allows researchers to distinguish acquired (somatic) and hereditary (germline) variants. The ability to distinguish somatic and germline variants facilitates estimation of tumor mutation burden (TMB), which is a recently FDA-approved pan-cancer marker for highly successful cancer immunotherapies; in tumor-only variant calling (i.e., without a matched normal), the difficulty in discriminating germline and somatic variants results in inflated and unreliable TMB estimates. We apply machine learning to the task of somatic vs germline classification in tumor-only samples using TabNet, a recently developed attentive deep learning model for tabular data that has achieved state of the art performance in multiple classification tasks (Arik and Pfister 2019). We constructed a training set for supervised classification using features derived from tumor-only variant calling and drawing somatic and germline truth-labels from an independent pipeline incorporating the patient-matched normal samples. Our trained model achieved state-of-the-art performance on two hold-out test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (F1-score: 88.3), and a metastatic melanoma dataset, (F1-score 79.8). Concordance between matched-normal and tumor-only TMB improves from R2 = 0.006 to 0.705 with the addition of our classifier. And importantly, this approach generalizes across tumor tissue types and capture kits and has a call rate of 100%. The interpretable feature masks of the attentive deep learning model explain the reasons for misclassified variants. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of White patients due to the racial biases of germline databases. We show that our machine learning approach appreciably reduces this racial bias in tumor-only variant-calling.

scholarly journals OnionMHC: A deep learning model for peptide — HLA-A*02:01 binding predictions using both structure and sequence feature sets

2021 ◽  
pp. 2050009
Author(s):  
Shikhar Saxena ◽  
Sambhavi Animesh ◽  
Melissa J. Fullwood ◽  
Yuguang Mu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Binding Affinity ◽  
State Of The Art ◽  
Learning Model ◽  
Peptide Sequence ◽  
Sequencing Analysis ◽  
Art Performance ◽  
Deep Learning Model ◽  
Human Receptor

Abstract The peptide binding to Major Histocompatibility Complex (MHC) proteins is an important step in the antigen-presentation pathway. Thus, predicting the binding potential of peptides with MHC is essential for the design of peptide-based therapeutics. Most of the available machine learning-based models predict the peptide-MHC binding based on the sequence of amino acids alone. Given the importance of structural information in determining the stability of the complex, here we have utilized both the complex structure and the peptide sequence features to predict the binding affinity of peptides to human receptor HLA-A*02:01. To our knowledge, no such model has been developed for the human HLA receptor before that incorporates both structure and sequence-based features. Results: We have applied machine learning techniques through the natural language processing (NLP) and convolutional neural network to design a model that performs comparably with the existing state-of-the-art models. Our model shows that the information from both sequence and structure domains results in enhanced performance in the binding prediction compared to the information from one domain alone. The testing results in 18 weekly benchmark datasets provided by the Immune Epitope Database (IEDB) as well as experimentally validated peptides from the whole-exome sequencing analysis of the breast cancer patients indicate that our model has achieved state-of-the-art performance. Conclusion: We have developed a deep-learning model (OnionMHC) that incorporates both structure as well as sequence-based features to predict the binding affinity of peptides with human receptor HLA-A*02:01. The model demonstrates state-of-the-art performance on the IEDB benchmark dataset as well as the experimentally validated peptides. The model can be used in the screening of potential neo-epitopes for the development of cancer vaccines or designing peptides for peptide-based therapeutics. OnionMHC is freely available at https://github.com/shikhar249/OnionMHC .

scholarly journals A COMPREHENSIVE STUDY ON APPLICATION OF DEEP LEARNING IN BRAIN TUMOR DETECTION

2021 ◽  
Vol 23 (07) ◽  
pp. 977-994
Author(s):  
Josmy Mathew ◽  
◽  
Dr. N. Srinivasan ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumours ◽  
Data Augmentation ◽  
State Of The Art ◽  
Network Models ◽  
Data Sets ◽  
Learning Approaches ◽  
Neural Network Models ◽  
Comprehensive Study

Deep Learning is an area of machine learning which, because of its capability to handle a large quantity of data, has demonstrated amazing achievements in each field, notably in biomedicine. Its potential and abilities were evaluated and utilised with an effective prognosis in the identification of brain tumours with MRI pictures. The diagnosis of MRI images by computer-aided brain tumours includes tumour identification, segmentation and classification. Many types of research have concentrated in recent years on conventional or basic machine learning approaches in the detection of brain tumours. Throughout this overview, we offer a comprehensive assessment of the surveys that have been reported so far and the current approaches for detecting tumours. Our review examines the major processes in deep learning approaches for detecting brain tumours including preprocessing, extraction of features and classification and their performance and limitations. We also explore state-of-the-art neural network models to identify brain tumours through extensive trials with and without data augmentation. This review also discusses existing data sets for brain tumour detection assessments.

scholarly journals OnionMHC: A Deep Learning Model for Peptide - HLA-A*02:01 Binding Predictions using both Structure and Sequence Feature Sets

2020 ◽  
Author(s):  
Shikhar Saxena ◽  
Sambhavi Animesh ◽  
Melissa Fullwood ◽  
Yuguang Mu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Binding Affinity ◽  
State Of The Art ◽  
Learning Model ◽  
Peptide Sequence ◽  
Sequencing Analysis ◽  
Art Performance ◽  
Deep Learning Model ◽  
Human Receptor

Abstract Background:The peptide binding to Major Histocompatibility Complex (MHC) proteins is an important step in the antigen-presentation pathway. Thus, predicting the binding potential of peptides with MHC is essential for the design of peptide-based therapeutics. Most of the available machine learning-based models predict the peptide-MHC binding based on the sequence of amino acids alone. Given the importance of structural information in determining the stability of the complex, here we have utilized both the complex structure and the peptide sequence features to predict the binding affinity of peptides to human receptor HLA-A*02:01. To our knowledge, no such model has been developed for the human HLA receptor before that incorporates both structure and sequence-based features.Results:We have applied machine learning techniques through the natural language processing (NLP) and convolutional neural network to design a model that performs comparably with the existing state-of-the-art models. Our model shows that the information from both sequence and structure domains results in enhanced performance in the binding prediction compared to the information from one domain alone. The testing results in 18 weekly benchmark datasets provided by the Immune Epitope Database (IEDB) as well as experimentally validated peptides from the whole-exome sequencing analysis of the breast cancer patients indicate that our model has achieved state-of-the-art performance.Conclusion: We have developed a deep-learning model (OnionMHC) that incorporates both structure as well as sequence-based features to predict the binding affinity of peptides with human receptor HLA-A*02:01. The model demonstrates state-of-the-art performance on the IEDB benchmark dataset as well as the experimentally validated peptides. The model can be used in the screening of potential neo-epitopes for the development of cancer vaccines or designing peptides for peptide-based therapeutics. OnionMHC is freely available at https://github.com/shikhar249/OnionMHC

scholarly journals A State-of-the-Art Survey on Deep Learning Theory and Architectures

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 292 ◽  
Author(s):  
Md Zahangir Alom ◽  
Tarek M. Taha ◽  
Chris Yakopcic ◽  
Stefan Westberg ◽  
Paheding Sidike ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Language Processing ◽  
Large Scale ◽  
Medical Information ◽  
State Of The Art ◽  
Generative Models ◽  
Learning Approaches

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

scholarly journals Deep understanding of shopper behaviours and interactions using RGB-D vision

2020 ◽  
Vol 31 (7-8) ◽  
Author(s):  
Marina Paolanti ◽  
Rocco Pietrini ◽  
Adriano Mancini ◽  
Emanuele Frontoni ◽  
Primo Zingaretti
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Deep Understanding ◽  
Learning Approaches ◽  
Multimedia Big Data ◽  
People Counting ◽  
The Past ◽  
Feature Based ◽  
Top View

Abstract In retail environments, understanding how shoppers move about in a store’s spaces and interact with products is very valuable. While the retail environment has several favourable characteristics that support computer vision, such as reasonable lighting, the large number and diversity of products sold, as well as the potential ambiguity of shoppers’ movements, mean that accurately measuring shopper behaviour is still challenging. Over the past years, machine-learning and feature-based tools for people counting as well as interactions analytic and re-identification were developed with the aim of learning shopper skills based on occlusion-free RGB-D cameras in a top-view configuration. However, after moving into the era of multimedia big data, machine-learning approaches evolved into deep learning approaches, which are a more powerful and efficient way of dealing with the complexities of human behaviour. In this paper, a novel VRAI deep learning application that uses three convolutional neural networks to count the number of people passing or stopping in the camera area, perform top-view re-identification and measure shopper–shelf interactions from a single RGB-D video flow with near real-time performances has been introduced. The framework is evaluated on the following three new datasets that are publicly available: TVHeads for people counting, HaDa for shopper–shelf interactions and TVPR2 for people re-identification. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods (accuracy of 99.5% on people counting, 92.6% on interaction classification and 74.5% on re-id), bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications.

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