An interpretable protein localization prediction framework

Mapping Intimacies ◽

10.32469/10355/88123 ◽

2021 ◽

Author(s):

◽

Yuexu Jiang

Keyword(s):

Short Term Memory ◽

Protein Localization ◽

Amino Acid Level ◽

Mitochondrial Proteins ◽

Single Amino Acid ◽

Short Term ◽

Independent Dataset ◽

Matrix Layer ◽

Long Short Term Memory ◽

Localization Prediction

Protein localization is related to many human diseases. Therefore, the prediction of protein localization is an essential task that has been extensively studied. Additionally, the study of the localization mechanism can provide more biological insights and testable hypotheses. In this thesis, we propose MULocDeep, a general deep learning-based localization prediction framework. We designed a matrix layer in its architecture to reflect the hierarchical relationships of localization in cells. This enables MULocDeep, to predict multiple localizations of a protein at both subcellular and suborganellar levels. We collected a dataset with 44 suborganellar localization annotations in 10 major subcellular compartments--the most comprehensive suborganelle localization dataset to date. Our collaborators also experimentally generated an independent dataset of mitochondrial proteins in Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots and made this dataset publicl using the above datasets show that overall, MULocDeep outperforms other major methods at both subcellular and suborganellar levels. We also applied Long short-term memory (LSTM) and the multi-head self-attention in MULocDeep to pursue a single amino acid level resolution when assessing their contributions to localization. This provides insights into the mechanism of protein sorting and localization motifs. Many of the candidate sites or motifs match the existing localization knowledge. A web server can be accessed at https://www.mu-loc.org/.

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Malicious Text Identification: Deep Learning from Public Comments and Emails

Information ◽

10.3390/info11060312 ◽

2020 ◽

Vol 11 (6) ◽

pp. 312 ◽

Cited By ~ 1

Author(s):

Asma Baccouche ◽

Sadaf Ahmed ◽

Daniel Sierra-Sosa ◽

Adel Elmaghraby

Keyword(s):

Social Media ◽

Deep Learning ◽

Language Processing ◽

Short Term Memory ◽

Good Alternative ◽

Classification Problems ◽

Short Term ◽

Independent Dataset ◽

Proposed Model ◽

Long Short Term Memory

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.

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MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation

10.21203/rs.3.rs-40744/v1 ◽

2020 ◽

Author(s):

Yuexu Jiang ◽

Duolin Wang ◽

Yifu Yao ◽

Holger Eubel ◽

Patrick Künzler ◽

...

Keyword(s):

Arabidopsis Thaliana ◽

Deep Learning ◽

Solanum Tuberosum ◽

Protein Function ◽

Protein Localization ◽

Residue Level ◽

Mitochondrial Proteins ◽

Independent Dataset ◽

Learning Framework ◽

Localization Prediction

Abstract Prediction of protein localization plays an important role in understanding protein function and mechanism. In this paper, we propose a general deep learning-based localization prediction framework, MULocDeep, which can predict multiple localizations of a protein at both subcellular and suborganellar levels. We collected a dataset with 45 suborganellar localization annotations in 10 major subcellular compartments, the most comprehensive suborganelle localization dataset to date. We also experimentally generated an independent dataset of mitochondrial proteins in Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots and made this dataset publicly available. Evaluations using the above datasets show that overall MULocDeep outperforms other major methods at both subcellular and suborganellar levels. Furthermore, MULocDeep assesses each amino acid’s contribution to localization, which provides insights into the mechanism of protein sorting and localization motifs. A web server can be accessed at http://mu-loc.org/.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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