Integration strategies of multi-omics data for machine learning analysis

Computational and Structural Biotechnology Journal, Journal Year: 2021, Volume and Issue: 19, P. 3735 - 3746

Published: Jan. 1, 2021

Increased availability of high-throughput technologies has generated an ever-growing number omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these have been obtained by machine learning algorithms produced diagnostic classification biomarkers. Most biomarkers date however only include one omic measurement at a time thus do not take full advantage recent multi-omics experiments now capture the entire complexity systems. Multi-omics integration strategies are needed combine knowledge brought each layer. We summarized most methods/ frameworks into five strategies: early, mixed, intermediate, late hierarchical. In this mini-review, we focus on challenges existing paying special attention applications.

Language: Английский

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Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence

IEEE Reviews in Biomedical Engineering, Journal Year: 2023, Volume and Issue: 17, P. 80 - 97

Published: Oct. 12, 2023

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal data ranging from multi-omics molecular to real-time continuous bio-signals are generated at an unprecedented speed scale every day. For first time, these able make precision medicine close a reality. However, due volume complexity, making good use requires major effort. Researchers clinicians actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery causal inference using variety modalities. These AI-based have demonstrated promising results in various healthcare applications. In this review paper, we summarize state-of-the-art AI models integrating electronic health records (EHRs) medicine. We discuss challenges opportunities with EHRs future directions. hope can inspire research

Language: Английский

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Classifying the multi-omics data of gastric cancer using a deep feature selection method

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 200, P. 116813 - 116813

Published: March 12, 2022

Language: Английский

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Secure tumor classification by shallow neural network using homomorphic encryption

BMC Genomics, Journal Year: 2022, Volume and Issue: 23(1)

Published: April 9, 2022

Disclosure of patients' genetic information in the process applying machine learning techniques for tumor classification hinders privacy personal information. Homomorphic Encryption (HE), which supports operations between encrypted data, can be used as one tools to perform such computation without leakage, but it brings great challenges directly general algorithms due limitations supported by HE. In particular, non-polynomial activation functions, including softmax are difficult implement with HE and require a suitable approximation method minimize loss accuracy. secure genome analysis competition called iDASH 2020, is presented task that multi-label predicts class samples based on using HE.We develop ensure during all computations model inference process. Our solution 1-layer neural network function uses approximate scheme. We present an enables technique efficiently encoding data reduce computational costs. addition, we propose HE-friendly filtering size large-scale data.We aim analyze dataset from The Cancer Genome Atlas (TCGA) dataset, consists 3,622 11 types cancers, features 25,128 genes. preprocessing reduces number genes 4,096 or less achieves microAUC value 0.9882 (85% accuracy) shallow network. Using our model, successfully compute steps test 3.75 minutes. As result exceptionally high values, was awarded co-first place 2020 Track 1: "Secure Tumor Encryption".Our first implementing Also, optimization methods this work enable implementation other challenging applications.

Language: Английский

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Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(13), P. 5660 - 5660

Published: June 28, 2024

Cancer research has increasingly utilized multi-omics analysis in recent decades to obtain biomolecular information from multiple layers, thereby gaining a better understanding of complex biological systems. However, the curse dimensionality is one most significant challenges when handling omics or data. Additionally, integrating by transforming different types into new representation can reduce model’s interpretability, as extracted features may lose context. This paper proposes Iterative Similarity Bagging (ISB), assisted Bayesian Ridge Regression (BRR). BRR serves domain-oriented supervised feature selection method, choosing essential calculating coefficients for each feature. Despite this, output datasets contain many features, leading complexity and high dimensionality. To address ISB was introduced dynamically without losing integrity data, which often occurs with transformation-based integration approaches. The evaluation measures employed were Root Mean Square Error (RMSE), Pearson Correlation Coefficient (PCC), coefficient determination (R2). results demonstrate that proposed method outperforms some current models terms regression performance, achieving an RMSE 0.12, PCC 0.879, R2 0.77 CCLE. For GDSC, it achieved 0.029, 0.90, 0.80.

Language: Английский

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MVNMF: Multiview nonnegative matrix factorization for radio-multigenomic analysis in breast cancer prognosis

Medical Image Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 103566 - 103566

Published: April 1, 2025

Language: Английский

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A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

Frontiers in Medicine, Journal Year: 2023, Volume and Issue: 9

Published: Jan. 10, 2023

Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge diagnosis and treatment patients with cancer. Many are middle late stages when they feel uncomfortable, unfortunately, most them will die Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for era precision medicine. This paper provides survey multi-omics-based using potential application Particularly, we make comprehensive summary analysis from perspective datasets, task types, integration methods. Furthermore, this points out remaining challenges discusses future topics.

Language: Английский

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Advances in AI-based genomic data analysis for cancer survival prediction

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 21, 2024

Language: Английский

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Breast cancer risk estimation with intelligent algorithms and risk factors for Cuban women

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 179, P. 108818 - 108818

Published: July 10, 2024

Breast cancer is the most common malignant neoplasm and leading cause of mortality among women globally. Current prediction models based on risk factors are inefficient in specific populations, so an appropriate calibrated breast model for Cuban essential. This article proposes a conceptual estimation using machine learning algorithms factors. The has three main components: knowledge representation, modeling, predictor evaluation. Nine were used to generate predictors proposed model. Two data sources served as case studies: first comprised collected from women, second included US Hispanic obtained Cancer Surveillance Consortium dataset. results show that effectively estimates could be valuable tool early detection identification patients at risk. According experiment results, best female population corresponds Random Forest algorithm with weighted score 5.981, training accuracy 0.996 AUC 0.997. In experiment, it was demonstrated generated by better values compared population, potentially generalizable other populations. Implementing this economically viable alternative reduce rate type Latin American countries such Cuba.

Language: Английский

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Homomorphic multi-label classification of virus strains

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Journal Year: 2022, Volume and Issue: unknown, P. 289 - 294

Published: Oct. 1, 2022

Detecting the gene sequence of virus strains from patients and classifying them into specific are very important to provide effective treatment. However, there significant barriers sharing strains' data in plaintext privacy concerns patients. Homomorphic encryption is a form that allows users calculate encrypted without decrypting it. Achieving highly accurate viral strain prediction while safeguarding user challenge. We develop secure multi-label classification method using homomorphic scheme. first used statistical genotype frequencies for preprocessing reduce dimension strains. Second, we improved TFHE library proposed by Chillotti et al. accommodate floating-point input neural network make calculation result more accurate. Finally, improve computational speed storage usage packing packs multiple feature information one ciphertext. successfully calculated 2000 inference steps on 128-bit test 0.09 seconds, reaching an accuracy 100 %.

Language: Английский

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