mitoDataclean: A machine learning approach for the accurate identification of cross‐contamination‐derived tumor mitochondrial DNA mutations

2022 ◽  
Author(s):  
Liping Su ◽  
Shanshan Guo ◽  
Wenjie Guo ◽  
Xiaoying Ji ◽  
Yang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mitochondrial Dna ◽  
Learning Approach ◽  
Cross Contamination ◽  
Accurate Identification ◽  
Mitochondrial Dna Mutations ◽  
Dna Mutations ◽  
Machine Learning Approach

scholarly journals Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Original Data ◽  
High Rate ◽  
Learning Approach ◽  
Accurate Identification ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Deep Learning Neural Network

AbstractIn recent days, Covid-19 coronavirus has been an immense impact on social, economic fields in the world. The objective of this study determines if it is feasible to use machine learning method to evaluate how much prediction results are close to original data related to Confirmed-Negative-Released-Death cases of Covid-19. For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset and the prediction results are tally with the results predicted by clinical doctors. The prediction results are validated against the original data based on some predefined metric. The experimental results showcase that the proposed approach is useful in generating suitable results based on the critical disease outbreak. It also helps doctors to recheck further verification of virus by the proposed method. The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients with in a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors. It could be a promising supplementary confirmation method for frontline clinical doctors. The proposed method has a high prediction rate and works fast for probable accurate identification of the disease. The performance analysis shows that a high rate of accuracy is obtained by the proposed method.

Glycine-induced formation and druggability score prediction of protein surface pockets

2019 ◽  
Vol 17 (05) ◽  
pp. 1950026
Author(s):  
Pietro Bongini ◽  
Neri Niccolai ◽  
Monica Bianchini
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Amino Acid ◽  
Drug Discovery ◽  
Human Health ◽  
Learning Approach ◽  
Natural Amino Acid ◽  
Dna Mutations ◽  
Machine Learning Approach ◽  
Allosteric Sites

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
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