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ORCIDBioData Mining, Volume 17
Volume 17, Number 1, 2024
- Sandra Batista, Vered Madar, Philip J. Freda
, Priyanka Bhandary, Attri Ghosh, Nicholas Matsumoto, Apurva S. Chitre, Abraham A. Palmer, Jason H. Moore:
Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis. - Xiaohui Yao, Xiaohan Jiang, Haoran Luo, Hong Liang, Xiufen Ye, Yanhui Wei, Shan Cong:
MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder. - Chih-Wei Chung, Seng-Cho Chou, Tzu-Hung Hsiao, Grace Joyce Zhang, Yu-Fang Chung, Yi-Ming Chen:
Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records. - Burcu Yaldiz, Onur Erdogan, Sevda Rafatov, Cem Iyigün, Yesim Aydin Son:
Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies. - Regan Odongo, Asuman Demiroglu-Zergeroglu, Tunahan Çakir:
A network-based drug prioritization and combination analysis for the MEK5/ERK5 pathway in breast cancer. - Thanyawee Srithanyarat, Kittisak Taoma, Thana Sutthibutpong, Marasri Ruengjitchatchawalya, Monrudee Liangruksa, Teeraphan Laomettachit:
Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles. - Xiao-Ce Dai, Yi Yu, Si-Yu Zhou, Shuo Yu, Mei-Xiang Xiang, Hong Ma:
Assessment of the causal relationship between gut microbiota and cardiovascular diseases: a bidirectional Mendelian randomization analysis. - Muhammad Taseer Suleman, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan:
m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models. - André Fonseca, Mikolaj Spytek, Przemyslaw Biecek, Clara Cordeiro, Nuno Sepúlveda:
Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data. - Emily R. Hannon, Carmen J. Marsit, Arlene E. Dent, Paula Embury, Sidney Ogolla, David Midem, Scott M. Williams, James W. Kazura:
Transcriptome- and DNA methylation-based cell-type deconvolutions produce similar estimates of differential gene expression and differential methylation. - Bolin Chen, Jinlei Zhang, Ci Shao, Jun Bian, Ruiming Kang, Xuequn Shang:
QIGTD: identifying critical genes in the evolution of lung adenocarcinoma with tensor decomposition. - Junliang Zhu, Shaowei Pu, Jiaji He, Dongchao Su, Weijie Cai, Xueying Xu, Hongbo Liu:
Processing imbalanced medical data at the data level with assisted-reproduction data as an example. - Yusuf Brima, Marcellin Atemkeng:
Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis. - Amani Almohaimeed, Ishag Adam:
Modeling heterogeneity of Sudanese hospital stay in neonatal and maternal unit: non-parametric random effect models with Gamma distribution. - Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovský, Matej Lexa:
Correction: Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning. - Vanesa Gómez-Martínez, David Chushig-Muzo, Marit B. Veierød, Conceição Granja, Cristina Soguero-Ruíz:
Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability. - Xinyi Xu, Changhong Miao, Shirui Yang, Lu Xiao, Ying Gao, Fangying Wu, Jianbo Xu:
Investigating potential drug targets for IgA nephropathy and membranous nephropathy through multi-queue plasma protein analysis: a Mendelian randomization study based on SMR and co-localization analysis. - Nina Kastendiek, Roberta Coletti, Thilo Gross, Marta B. Lopes:
Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification. - Qixin Yang, Jing Huang, Jiang He, Xueyang Liu, Lu Yu, Yuehua Li:
Transcriptome-based network analysis related to regulatory T cells infiltration identified RCN1 as a potential biomarker for prognosis in clear cell renal cell carcinoma. - Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao:
Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion. - Pradeep Varathan, Bing He, Linhui Xie, Kwangsik Nho, Andrew J. Saykin, Jingwen Yan:
Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation. - Philip J. Freda, Attri Ghosh, Priyanka Bhandary, Nicholas Matsumoto, Apurva S. Chitre, Jiayan Zhou, Molly A. Hall, Abraham A. Palmer, Tayo Obafemi-Ajayi, Jason H. Moore:
PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies. - Laila Musib, Roberta Coletti, Marta B. Lopes, Helena Mouriño, Eunice Carrasquinha:
Priority-Elastic net for binary disease outcome prediction based on multi-omics data. - Lin Wang, Jiaming Su, Zhongjie Liu, Shaowei Ding, Yaotan Li, Baoluo Hou, Yuxin Hu, Zhaoxi Dong, Jingyi Tang, Hongfang Liu, Weijing Liu:
Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis. - Yuan-Xiang Deng, Jyun-Yi Wang, Chia-Hsin Ko, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu:
Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department. - Yunfei Yin, Zheng Yuan, Md Tanvir Islam, Xianjian Bao:
Electronic medical records imputation by temporal Generative Adversarial Network. - Mateja Napravnik, Franko Hrzic, Sebastian Tschauner, Ivan Stajduhar:
Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database. - Yeongmin Kim, Wongyung Choi, Woojeong Choi, Grace Ko, Seonggyun Han, Hwan-Cheol Kim, Dokyoon Kim, Dong-Gi Lee, Dong Wook Shin, Younghee Lee:
A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records. - Andrew Marra:
G4 & the balanced metric family - a novel approach to solving binary classification problems in medical device validation & verification studies. - Jiang Zhao, Qian Zhang, Cunle Zhu, Yuqi Wu, Guohui Zhang, Qianliang Wang, Xingyou Dong, Benyi Li, Xiangwei Wang:
Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs. - Shilpa R. Thandla, Grace Q. Armstrong, Adil Menon, Aashna Shah, David L. Gueye, Clara Harb, Estefania Hernandez, Yasaswini Iyer, Abigail R. Hotchner, Riddhi Modi, Anusha Mudigonda, Maria A. Prokos, Tharun M. Rao, Olivia R. Thomas, Camilo A. Beltran, Taylor Guerrieri, Sydney Leblanc, Skanda Moorthy, Sara G. Yacoub, Jacob E. Gardner, Benjamin M. Greenberg, Alyssa Hubal, Yuliana P. Lapina, Jacqueline Moran, Joseph P. O'brien, Anna C. Winnicki, Christina Yoka, Junwei Zhang, Peter A. Zimmerman:
Comparing new tools of artificial intelligence to the authentic intelligence of our global health students. - Erika Cantor, Sandra Guauque-Olarte, Roberto León, Stéren Chabert, Rodrigo Salas:
Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data. - Jia-Ming Huan, Xiao-Jie Wang, Yuan Li, Shi-Jun Zhang, Yuan-Long Hu, Yun-Lun Li:
The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data. - Mohammed Abo-Zahhad, Ahmed H. Abd El-Malek, Mohammed Sharaf Sayed, Susan Njeri Gitau:
Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review. - Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovský, Matej Lexa:
Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning. - Zhendong Sha, Philip J. Freda, Priyanka Bhandary, Attri Ghosh, Nicholas Matsumoto, Jason H. Moore, Ting Hu:
Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis. - Selcen Ari Yuka, Alper Yilmaz:
Decoding dynamic miRNA: ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes. - Yuan-Chia Chu, Saint Shiou-Sheng Chen, Kuen-Bao Chen, Jui-Sheng Sun, Tzu-Kuei Shen, Li-Kuei Chen:
Enhanced labor pain monitoring using machine learning and ECG waveform analysis for uterine contraction-induced pain. - Panagiota I. Kontou, Pantelis G. Bagos:
The goldmine of GWAS summary statistics: a systematic review of methods and tools. - Jianchang Hu, Silke Szymczak:
Evaluation of network-guided random forest for disease gene discovery. - Vincenzo Bonnici, Davide Chicco:
Seven quick tips for gene-focused computational pangenomic analysis. - Philip J. Freda, Suyu Ye, Robert Zhang, Jason H. Moore, Ryan J. Urbanowicz:
Assessing the limitations of relief-based algorithms in detecting higher-order interactions. - Zhaoming Kong, Rong Zhou, Xinwei Luo, Songlin Zhao, Ann B. Ragin, Alex D. Leow, Lifang He:
TGNet: tensor-based graph convolutional networks for multimodal brain network analysis. - Jenna l. Ballard, Zexuan Wang, Wenrui Li, Li Shen, Qi Long:
Deep learning-based approaches for multi-omics data integration and analysis. - Carolina Del-Valle-Soto, Ramon A. Briseño, Leonardo J. Valdivia, Juan Arturo Nolazco-Flores:
Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact. - Krishna Prasad, Allen Griffiths, Kavya Agrawal, Michael McEwan, Flavio Macci, Marco Ghisoni, Matthew Stopher, Matthew Napleton, Joel Strickland, David Keating, Thomas M. Whitehead, Gareth John Conduit, Stacey Murray, Lauren Edward:
Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers' physiological measurements and mouth level exposure. - Mitja Briscik, Gabriele Tazza, László Vidács, Marie-Agnès Dillies, Sébastien Déjean:
Supervised multiple kernel learning approaches for multi-omics data integration. - Xueli Zhang, Dantong Li, Siting Ye, Shunming Liu, Shuo Ma, Min Li, Qiliang Peng, Lianting Hu, Xianwen Shang, Mingguang He, Lei Zhang:
Decoding the genetic comorbidity network of Alzheimer's disease. - Chia-Wei Chang, Hsin-Yao Wang, Wan-Ying Lin, Yu-Chiang Wang, Wei-Lin Lo, Ting-Wei Lin, Jia-Ruei Yu, Yi-Ju Tseng:
Identifying heterogeneous subgroups of systemic autoimmune diseases by applying a joint dimension reduction and clustering approach to immunomarkers. - Hangxing Huang, Lu Zhang, Yongyu Yang, Ling Huang, Xikui Lu, Jingyang Li, Huimin Yu, Shuqiao Cheng, Jian Xiao:
Construction and application of medication reminder system: intelligent generation of universal medication schedule. - Yuqi Zhang, Sijin Li, Weijie Wu, Yanqing Zhao, Jintao Han, Chao Tong, Niansang Luo, Kun Zhang:
Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES. - Hong Sun, Yunqin Chen, Liangxiao Ma:
MDVarP: modifier ~ disease-causing variant pairs predictor. - Monique Arnold, Lathan Liou, Mary Regina Boland:
Development, evaluation and comparison of machine learning algorithms for predicting in-hospital patient charges for congestive heart failure exacerbations, chronic obstructive pulmonary disease exacerbations and diabetic ketoacidosis. - Zhiping Paul Wang, Priyanka Bhandary, Yizhou Wang, Jason H. Moore:
Using GPT-4 to write a scientific review article: a pilot evaluation study. - Dani Livne, Sol Efroni:
Pathway metrics accurately stratify T cells to their cells states. - Caroline König, Alfredo Vellido:
Understanding predictions of drug profiles using explainable machine learning models. - Ahmad Al Badawi, Mohd Faizal Bin Yusof:
Private pathological assessment via machine learning and homomorphic encryption. - Deren Xu, Weng Howe Chan, Habibollah Haron, Hui Wen Nies, Kohbalan Moorthy:
From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases. - Dixin Shen, Juan Pablo Lewinger, Eric S. Kawaguchi:
A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies. - Luís B. Elvas, Sara Gomes, João C. Ferreira, Luís Brás Rosário, Tomás Brandão:
Deep learning for automatic calcium detection in echocardiography. - Richa Gupta, Mansi Bhandari, Anhad Grover, Taher Al-Shehari, Mohammed Kadrie, Taha Alfakih, Hussain Alsalman:
Predictive modeling of ALS progression: an XGBoost approach using clinical features. - Yang Chen, Qingqing Zheng, Hui Wang, Peiren Tang, Li Deng, Pu Li, Huan Li, Jianhong Hou, Jie Li, Li Wang, Jun Peng:
Integrating transcriptomics and proteomics to analyze the immune microenvironment of cytomegalovirus associated ulcerative colitis and identify relevant biomarkers.
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