scholarly journals An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

2022 ◽  
pp. 1-1
Author(s):  
Sahar Iravani ◽  
Tim O. F. Conrad
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Biomarker Detection ◽  
Proteomics Data

scholarly journals An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

2021 ◽  
Author(s):  
Sahar Iravani ◽  
Tim O.F. Conrad
Keyword(s):  
Mass Spectrometry ◽  
Neural Networks ◽  
Deep Learning ◽  
Clinical State ◽  
Biomarker Detection ◽  
Proteomics Data ◽  
Comparable Amount ◽  
Complex Protein ◽  
Propagation Technique ◽  
High Level

AbstractAnalyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Besides, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Next, based on the trained neural networks, biomarkers can be identified using the layer-wise relevance propagation technique. This enables detecting discriminating regions of the data and the design of more robust networks. We show that DLearnMS outperforms conventional LC-MS biomarker detection approaches in detecting fewer false positive peaks while maintaining a comparable amount of true positives peaks. Unlike other methods, no explicit preprocessing step is needed in DLearnMS.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach

Deep Learning Approach for the Detection of Depression in Twitter

2019 ◽  
Author(s):  
Aswathy K S ◽  
Rafeeque P C ◽  
Reena Murali
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Detection Of Depression

A Deep Learning Approach to Estimate Forward Default Intensities

2020 ◽  
Author(s):  
Marc-Aurèle Divernois
Keyword(s):  
Deep Learning ◽  
Learning Approach

Deep Noise Tracking Network: A Hybrid Signal Processing/Deep Learning Approach to Speech Enhancement

2018 ◽  
Author(s):  
Shuai Nie ◽  
Shan Liang ◽  
Bin Liu ◽  
Yaping Zhang ◽  
Wenju Liu ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Speech Enhancement ◽  
Learning Approach

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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