Výsledky vyhledávání - Machine Learning and Artificial Intelligence in Bioinformatics

Modeling aspects of the language of life through transfer-learning protein sequences Autor Heinzinger, Michael, Elnaggar, Ahmed, Wang, Yu, Dallago, Christian, Nechaev, Dmitrii, Matthes, Florian, Rost, Burkhard

“… years, most state-of-the-art approaches combined machine learning and evolutionary information…”
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ProteinNet: a standardized data set for machine learning of protein structure Autor AlQuraishi, Mohammed

“…Background Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design…”
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Comprehensive ensemble in QSAR prediction for drug discovery Autor Kwon, Sunyoung, Bae, Ho, Jo, Jeonghee, Yoon, Sungroh

“… QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions…”
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DPDDI: a deep predictor for drug-drug interactions Autor Feng, Yue-Hua, Zhang, Shao-Wu, Shi, Jian-Yu

“…Background The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the…”
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FeatureSelect: a software for feature selection based on machine learning approaches Autor Masoudi-Sobhanzadeh, Yosef, Motieghader, Habib, Masoudi-Nejad, Ali

“…Background Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other…”
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Graph-based prediction of Protein-protein interactions with attributed signed graph embedding Autor Yang, Fang, Fan, Kunjie, Song, Dandan, Lin, Huakang

“… Various machine learning methods have been proposed, including a deep learning technique which is sequence-based that has achieved promising results…”
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Novel deep learning model for more accurate prediction of drug-drug interaction effects Autor Lee, Geonhee, Park, Chihyun, Ahn, Jaegyoon

“… for improvement in prediction performance. Results In this study, we propose a novel deep learning model to predict the effect of DDIs more accurately…”
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MiPepid: MicroPeptide identification tool using machine learning Autor Zhu, Mengmeng, Gribskov, Michael

“… Despite the increased amount of data, we still lack bioinformatics tools for specifically identifying micropeptides from DNA sequences…”
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Prediction of heart disease and classifiers’ sensitivity analysis Autor Almustafa, Khaled Mohamad

“…Background Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care…”
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Conditional permutation importance revisited Autor Debeer, Dries, Strobl, Carolin

“…Background Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted…”
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Amino Acid Encoding for Deep Learning Applications Autor ElAbd, Hesham, Bromberg, Yana, Hoarfrost, Adrienne, Lenz, Tobias, Wendorff, Mareike

“…Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available…”
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Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models Autor Abdurahman, Fetulhak, Fante, Kinde Anlay, Aliy, Mohammed

“…Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the…”
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Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides Autor Boone, Kyle, Wisdom, Cate, Camarda, Kyle, Spencer, Paulette, Tamerler, Candan

“…Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems…”
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A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis Autor Lin, Eugene, Mukherjee, Sudipto, Kannan, Sreeram

“…Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at…”
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SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures Autor Zuo, Zhaorui, Wang, Penglei, Chen, Xiaowei, Tian, Li, Ge, Hui, Qian, Dahong

“… mutation, gene expression, and cheminformatics in one machine learning model. Results We presented here a novel deep-learning model that integrates gene expression…”
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Application of network link prediction in drug discovery Autor Abbas, Khushnood, Abbasi, Alireza, Dong, Shi, Niu, Ling, Yu, Laihang, Chen, Bolun, Cai, Shi-Min, Hasan, Qambar

“… Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large…”
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Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data Autor De Velasco Oriol, Javier, Vallejo, Edgar E., Estrada, Karol, Taméz Peña, José Gerardo, Disease Neuroimaging Initiative, The Alzheimer’s

“… Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available…”
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A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data Autor Xu, Jing, Wu, Peng, Chen, Yuehui, Meng, Qingfang, Dawood, Hussain, Dawood, Hassan

“…Background Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in…”
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Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data Autor Smith, Aaron M., Walsh, Jonathan R., Long, John, Davis, Craig B., Henstock, Peter, Hodge, Martin R., Maciejewski, Mateusz, Mu, Xinmeng Jasmine, Ra, Stephen, Zhao, Shanrong, Ziemek, Daniel, Fisher, Charles K.

“… Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance…”
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GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification Autor Hu, Jinlong, Cao, Lijie, Li, Tenghui, Dong, Shoubin, Li, Ping

“… Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD…”
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