Markowitz Portfolio Theory Helps Decrease Medicines’ Side Effect and Speed up Machine Learning

Identification of Anti-cancer Peptides Based on Multi-classifier System

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666191203141102 ◽

2020 ◽

Vol 22 (10) ◽

pp. 694-704 ◽

Cited By ~ 2

Author(s):

Wanben Zhong ◽

Bineng Zhong ◽

Hongbo Zhang ◽

Ziyi Chen ◽

Yan Chen

Keyword(s):

Machine Learning ◽

Side Effect ◽

Learning Models ◽

Normal Cells ◽

Classifier System ◽

Prediction Rate ◽

Anti Cancer ◽

Feature Information ◽

Machine Learning Models ◽

Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

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A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods

Current Drug Targets ◽

10.2174/1389450119666181002143355 ◽

2019 ◽

Vol 20 (5) ◽

pp. 540-550 ◽

Cited By ~ 11

Author(s):

Jiu-Xin Tan ◽

Hao Lv ◽

Fang Wang ◽

Fu-Ying Dao ◽

Wei Chen ◽

...

Keyword(s):

Machine Learning ◽

Catalytic Mechanism ◽

Biological Function ◽

Learning Methods ◽

Biochemical Processes ◽

Machine Learning Methods ◽

Enzyme Family ◽

The Family ◽

Speed Up ◽

Family Classification

Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

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Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Future Internet ◽

10.3390/fi13040094 ◽

2021 ◽

Vol 13 (4) ◽

pp. 94

Author(s):

Haokun Fang ◽

Quan Qian

Keyword(s):

Machine Learning ◽

Homomorphic Encryption ◽

Privacy Preserving ◽

Great Success ◽

Learning Framework ◽

Computational Overhead ◽

Important Concern ◽

Speed Up ◽

Key Length ◽

Core Idea

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

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Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change

10.1101/2021.10.06.463347 ◽

2021 ◽

Author(s):

Abdallah Bari ◽

Hassan Ouabbou ◽

abderrazek Jilal ◽

Hamid Khazaei ◽

Fred Stoddard ◽

...

Keyword(s):

Climate Change ◽

Machine Learning ◽

Crop Varieties ◽

Short Period ◽

Speed Up ◽

Increasing Demand ◽

Mitigate Climate Change ◽

High Yielding Varieties ◽

New Crop ◽

Adaptation And Mitigation

Climate change poses serious challenges to achieving food security in a time of a need to produce more food to keep up with the worlds increasing demand for food. There is an urgent need to speed up the development of new high yielding varieties with traits of adaptation and mitigation to climate change. Mathematical approaches, including ML approaches, have been used to search for such traits, leading to unprecedented results as some of the traits, including heat traits that have been long sought-for, have been found within a short period of time.

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OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques

Database ◽

10.1093/database/baaa067 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Fabio R. Cerqueira ◽

Ana Tereza Ribeiro Vasconcelos

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Open Reading Frames ◽

Machine Learning Techniques ◽

Bacterial Genomes ◽

Small Proteins ◽

Learning Techniques ◽

Speed Up ◽

Computational Procedures ◽

Small Orfs

Abstract Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

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