A Graph Embedded Feature Decoupling Model for Clustering Single Cell RNA-seq Data

Authors: Yan, Xiaoming, Xu, Liyang, Chi, Haoang, Yang, Huihui, Chen, Huan, Tang, Yuhua

Source: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2024 IEEE International Conference on. :1720-1725 Dec, 2024

Relation: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Target-oriented Reference Construction for supervised cell type identification in scRNA-seq

Authors: Wei, Xin, Ma, Wenjing, Wu, Zhijin, Wu, Hao

Source: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2024 IEEE International Conference on. :643-646 Dec, 2024

Relation: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Depression Diagnosis: Bioinformatics Uncovers Cellular Abnormalities in Female-Specific Genes With Ai-Driven Blood Biomarker-Based Prediction

Authors: Firoz, Neda, Yadav, Nivedita, Beresteneva, Olga Grigorievna, Vladimirovich, Sergey Aksyonov

Source: 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE) Bioinformatics and Bioengineering (BIBE), 2024 IEEE 24th International Conference on. :1-8 Nov, 2024

Relation: 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE)

Automatically Detecting Anchor Cells and Clustering for scRNA-Seq Data Using scTSNN

Authors: Liu, Q., Zhang, D., Wang, D., Wang, G., Wang, Y.

Source: IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. Biomedical and Health Informatics, IEEE Journal of. 28(11):7015-7027 Nov, 2024

Analyzing Large-Scale Single-Cell RNA-Seq Data Using Coreset

Authors: Usman, K., Wan, F., Zhao, D., Peng, J., Zeng, J.

Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics IEEE/ACM Trans. Comput. Biol. and Bioinf. Computational Biology and Bioinformatics, IEEE/ACM Transactions on. 21(6):1784-1793 Jan, 2024

Hybrid Causal Feature Selection for Cancer Biomarker Identification From RNA-Seq Data

Authors: Xu, W., Zhang, H., Xia, Y., Ren, Y., Guan, J., Zhou, S.

Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics IEEE/ACM Trans. Comput. Biol. and Bioinf. Computational Biology and Bioinformatics, IEEE/ACM Transactions on. 21(6):1645-1655 Jan, 2024

Identification Of Hub Genes In Pancreatic Cancer Using Rna Sequence Analysis

Authors: JagadeeswaraRao, G, SivaPrasad, A, Gopal, K Venu, Sunil, B

Source: 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 Smart Computing for Innovation and Advancement in Industry 4.0, 2024 OPJU International Technology Conference (OTCON) on. :1-6 Jun, 2024

Relation: 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0

Decoding SARS-CoV-2 Variants: An in-silico approach to RNA-Seq feature extraction using K-mers and N-grams

Authors: Iqbal, Naiyar, Bhardwaj, Aditya

Source: 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) Electrical, Electronics and Computer Science (SCEECS), 2024 IEEE International Students' Conference on. :1-10 Feb, 2024

Relation: 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

An AI Interface System to NCBI SRA Experiment Data to Support Computational Biology Research

Authors: Le, Anh, Vu, Lan

Source: 2023 International Conference on Computational Science and Computational Intelligence (CSCI) CSCI Computational Science and Computational Intelligence (CSCI), 2023 International Conference on. :540-545 Dec, 2023

Relation: 2023 International Conference on Computational Science and Computational Intelligence (CSCI)

Imputing single-cell RNA-seq data by graph autoencoder with multi-kernel

Authors: Jiang, Kang, Liao, Bo., Papagerakis, Petros, Wu, Fang-Xiang

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :228-232 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

A deep graph convolution network with attention for clustering scRNA-seq data

Authors: Chen, Xiang, Yu, Junnan, Peng, Li, Li, Min

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :320-323 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Exploring Parameter-Efficient Fine-Tuning of a Large-Scale Pre-Trained Model for scRNA-seq Cell Type Annotation

Authors: Liu, Yuhang, Li, Tianhao, Wang, Zixuan, Zhu, Guiquan, Zhang, Yongqing, Zou, Quan

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :580-585 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

Authors: Simoes, Miguel, Pereira, Tania, Silva, Francisco, Machado, Jose Carlos, Oliveira, Helder P.

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :2875-2882 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

A flexible gene regulatory network reconstruction method based on autoencoder and graph attention network

Authors: Zheng, Ruiqing, Zeng, Yanping, Zeng, Weixing, Li, Min

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :215-221 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Spectral clustering based on multi-similarity learning method for single-cell RNA-seq data

Authors: Zhang, L., Ren, Q., Kong, X., Liu, S., Shang, J., Li, F.

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :3241-3248 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Comprehensive evaluation of RNA-seq alignment methods based on long-read sequencing data

Authors: Liu, Yadong, Lu, Zhenhao, Liu, Zhongyu, Guo, Hongzhe, Wang, Yadong, Jiang, Tao

Source: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :339-342 Dec, 2023

Relation: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Integrated Analysis of Bulk and Single-Cell Transcriptomics in Cervical Cancer: Insights into BPGM, EGLN3, and SUN1

Authors: Elsherif, Assem K., Emam, Mohamed, Abushady, Asmaa M., Gamaleldin, Mariam, Al-Atabany, Walid, Elhadidi, Mohamed

Source: 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) BIBE Bioinformatics and Bioengineering (BIBE), 2023 IEEE 23rd International Conference on. :94-99 Dec, 2023

Relation: 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)

Using Random Forest and a Hybrid model to Predict Drug Response in Skin Cutaneous Melanoma

Authors: Alsahly, Wafa, Poterlowicz, Krzysztof, Aldoghaither, Huda

Source: 2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC) Biomedical Instrumentation and Technology Conference (IBITeC), 2023 IEEE International. :46-51 Nov, 2023

Relation: 2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC)

A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection

Authors: Zhang, D., Gao, Y., Zhao, J., Zheng, C., Liu, J.

Source: IEEE Transactions on Neural Networks and Learning Systems IEEE Trans. Neural Netw. Learning Syst. Neural Networks and Learning Systems, IEEE Transactions on. 35(2):2473-2483 Feb, 2024

Trends and Prospects in Pig Genomics and Genetics

Contributors: Piórkowska, Katarzyna, Ropka-Molik, Katarzyna

Subject Terms: SESN3, SMAD3, ssc-miR-124-3p, pig, adipogenesis, Iberian, additive, dominance, genetic correlation, crossbreeding, genomic selection, BCL2L2-PABPN1, chimeric RNA, cis-SAGe, genome-wide analysis, RNA-Seq, cardiomyocyte structure, cytoskeleton organization, extracellular matrix, cell culture, transcriptomic analysis, CREB, alternative splicing variants, function, RNA-seq, fibrinogen-like 1, FGL1, fat deposition, selective markers, corpus luteum number, number of stillborn, gene ontology, genomic regions, gene network, pCADD, protein–protein interaction, wild boar, species identification, genetic markers, genetic methods, primary adipocyte cells, adipose tissue, lncRNA-AS, Sus scrofa, STR, genetic differentiation, adipocytes, ddPCR, ECmiRNAs, mesenchymal stem cells, cardiomyocytes, porcine cardiac muscle, long-term in vitro cell culture, stemness markers, Nero Lucano pig, Southern Italy, inbreeding coefficient (FROH), runs of homozygosity (ROH) islands, umbilical hernia, inguinal hernia, DNA methylation, transcript level, Research and information: general, Biology, life sciences, Technology, Engineering, Agriculture, Industrial processes

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