Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically-Based Features

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine-learning classifiers and regressors and to evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks (DNNs) on user-defined physicochemically relevant features developed for this work outperforming graph convolutional, random forest, and large featurizations. The results show that these user-provided structure-, ligand-, and statistically based features and specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), and other metrics and by their interpretability and chemical meaning of the descriptors/features. In addition, the same features in the DNN method performed better than in a multivariate logistic model: validation MCC = 0.468 and training MCC = 0.868 for the present work compared to evaluation set MCC = 0.2036 and training set MCC = 0.5364 for the multivariate logistic regression on the full, unbalanced set. Techniques of this type may improve AR and toxicity description and prediction, improving assessment and design of compounds. Source code and data are available on github.

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Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically-Based Features

10.20944/preprints202102.0318.v3 ◽

2021 ◽

Author(s):

Alfonso T. García-Sosa

Keyword(s):

Neural Networks ◽

Androgen Receptor ◽

Logistic Model ◽

Deep Neural Networks ◽

State Of The Art ◽

Protein Structures ◽

Training Set ◽

Multivariate Logistic Model ◽

And Training ◽

Better Than

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine learning classifiers and regressors and evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to dif- ferent results, with deep neural networks (DNNs) on user-defined physicochemically-relevant features developed for this work outperforming graph convolutional, random forest, and large featurizations. The results show that these user-provided structure-, ligand-, and statistically-based features and specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), and other metrics and by their interpretability and chemical meaning of the descriptors/features. In addition, the same features in the DNN method performed better than in a multivariate logistic model: validation MCC = 0.468 and training MCC = 0.868 for the present work compared to evalu- ation set MCC = 0.2036 and training set MCC = 0.5364 for the multivariate logistic regression on the full, unbalanced set. Techniques of this type may improve AR and toxicity description and predic- tion, improving assessment and design of compounds. Source code and data are available at https://github.com/AlfonsoTGarcia-Sosa/ML

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Androgen Receptor Binding Prediction with Random Forest, Deep Neural Networks, and Graph Convolutional Neural Networks

10.20944/preprints202102.0318.v1 ◽

2021 ◽

Author(s):

Alfonso T. García-Sosa

Keyword(s):

Prostate Cancer ◽

Neural Networks ◽

Androgen Receptor ◽

Random Forest ◽

Deep Neural Networks ◽

State Of The Art ◽

Source Code ◽

Protein Structures ◽

Binding Prediction ◽

Machine Learning Classifiers

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine learning classifiers and regressors and evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks on user-defined physicochemically-relevant features developed for this work outperform graph convolutional, random forest, and large featurizations. The results can help provide clues on risk of substances and better experimental design for toxicity assays. Source code and data are available at https://github.com/AlfonsoTGarcia-Sosa/ML

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Reverse engineering imperceptible backdoor attacks on deep neural networks for detection and training set cleansing

Computers & Security ◽

10.1016/j.cose.2021.102280 ◽

2021 ◽

Vol 106 ◽

pp. 102280

Author(s):

Zhen Xiang ◽

David J. Miller ◽

George Kesidis

Keyword(s):

Neural Networks ◽

Reverse Engineering ◽

Deep Neural Networks ◽

Training Set ◽

And Training

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Self-Supervised Learning for Generalizable Out-of-Distribution Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5966 ◽

2020 ◽

Vol 34 (04) ◽

pp. 5216-5223 ◽

Cited By ~ 1

Author(s):

Sina Mohseni ◽

Mandar Pitale ◽

JBS Yadawa ◽

Zhangyang Wang

Keyword(s):

Neural Networks ◽

Autonomous Vehicles ◽

Deep Neural Networks ◽

State Of The Art ◽

Feature Learning ◽

Detection Methods ◽

Training Set ◽

Safety Critical ◽

Multiple Image ◽

A New Technique

The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as autonomous vehicles needs to address a variety of DNNs' vulnerabilities, one of which being detecting and rejecting out-of-distribution outliers that might result in unpredictable fatal errors. We propose a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time. Our technique does not need to pre-know the distribution of targeted OOD samples and incur no extra overheads compared to other methods. We perform multiple image classification experiments and observe our technique to perform favorably against state-of-the-art OOD detection methods. Interestingly, we witness that our method also reduces in-distribution classification risk via rejecting samples near the boundaries of the training set distribution.

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Deep Neural Networks with Multistate Activation Functions

Computational Intelligence and Neuroscience ◽

10.1155/2015/721367 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Chenghao Cai ◽

Yanyan Xu ◽

Dengfeng Ke ◽

Kaile Su

Keyword(s):

Neural Networks ◽

Gradient Descent ◽

Deep Neural Networks ◽

Error Rates ◽

Stochastic Gradient Descent ◽

Activation Functions ◽

Classification Problems ◽

Training Set ◽

Relative Improvement ◽

Better Than

We propose multistate activation functions (MSAFs) for deep neural networks (DNNs). These MSAFs are new kinds of activation functions which are capable of representing more than two states, including theN-order MSAFs and the symmetrical MSAF. DNNs with these MSAFs can be trained via conventional Stochastic Gradient Descent (SGD) as well as mean-normalised SGD. We also discuss how these MSAFs perform when used to resolve classification problems. Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates. Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets. The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates.

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Innovative Topologies and Algorithms for Neural Networks

Future Internet ◽

10.3390/fi12070117 ◽

2020 ◽

Vol 12 (7) ◽

pp. 117

Author(s):

Salvatore Graziani ◽

Maria Gabriella Xibilia

Keyword(s):

Neural Networks ◽

Network Architecture ◽

Deep Neural Networks ◽

State Of The Art ◽

Text Processing ◽

Successful Outcome ◽

Training Algorithm ◽

Special Issue ◽

And Training ◽

Selection Of

The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved the state of the art in many applications, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks is devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. The papers of this Special Issue make significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. Twelve papers are collected in the issue, addressing many relevant aspects of the topic.

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Convolutional Neural Networks for Classifying Melanoma Images

10.1101/2020.05.22.110973 ◽

2020 ◽

Author(s):

Abhinav Sagar ◽

J Dheeba

Keyword(s):

Neural Networks ◽

Skin Cancer ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Deep Neural Networks ◽

State Of The Art ◽

Cancer Classification ◽

Previous State ◽

Better Than

AbstractIn this work, we address the problem of skin cancer classification using convolutional neural networks. A lot of cancer cases early on are misdiagnosed as something else leading to severe consequences including the death of a patient. Also there are cases in which patients have some other problems and doctors think they might have skin cancer. This leads to unnecessary time and money spent for further diagnosis. In this work, we address both of the above problems using deep neural networks and transfer learning architecture. We have used publicly available ISIC databases for both training and testing our model. Our work achieves an accuracy of 0.935, precision of 0.94, recall of 0.77, F1 score of 0.85 and ROC-AUC of 0.861 which is better than the previous state of the art approaches.

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Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽

10.3390/a14020039 ◽

2021 ◽

Vol 14 (2) ◽

pp. 39

Author(s):

Carlos Lassance ◽

Vincent Gripon ◽

Antonio Ortega

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Objective Function ◽

Learning Process ◽

Deep Neural Networks ◽

State Of The Art ◽

The Core ◽

Learning Tasks ◽

Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

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Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

Electronics ◽

10.3390/electronics10030230 ◽

2021 ◽

Vol 10 (3) ◽

pp. 230

Author(s):

Jaechan Cho ◽

Yongchul Jung ◽

Seongjoo Lee ◽

Yunho Jung

Keyword(s):

Neural Networks ◽

High Throughput ◽

Deep Neural Networks ◽

State Of The Art ◽

Throughput Performance ◽

Adaptive Parallelism ◽

Sensor Applications ◽

Binary Neural Network ◽

Target Layer ◽

Network Topologies

Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable resources can support various network topologies, but they operate with the same parallelism for all the layers of the BNN, which degrades throughput performance at certain layers. To overcome this problem, we propose a BNN accelerator with adaptive parallelism that offers high throughput performance in all layers. The proposed accelerator analyzes target layer parameters and operates with optimal parallelism using reasonable resources. In addition, this architecture is able to fully compute all types of BNN layers thanks to its reconfigurability, and it can achieve a higher area–speed efficiency than existing accelerators. In performance evaluation using state-of-the-art BNN topologies, the designed BNN accelerator achieved an area–speed efficiency 9.69 times higher than previous FPGA implementations and 24% higher than existing VLSI implementations for BNNs.

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