EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913286061031424 |
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| author | Pillai, Nisha Das, Athish Ram Ayoola, Moses Gireesan, Ganga Nanduri, Bindu Ramkumar, Mahalingam |
| author_facet | Pillai, Nisha Das, Athish Ram Ayoola, Moses Gireesan, Ganga Nanduri, Bindu Ramkumar, Mahalingam |
| contents | Artificial intelligence (AI) techniques are widely applied in the life sciences. However, applying innovative AI techniques to understand and deconvolute biological complexity is hindered by the learning curve for life science scientists to understand and use computing languages. An open-source, user-friendly interface for AI models, that does not require programming skills to analyze complex biological data will be extremely valuable to the bioinformatics community. With easy access to different sequencing technologies and increased interest in different 'omics' studies, the number of biological datasets being generated has increased and analyzing these high-throughput datasets is computationally demanding. The majority of AI libraries today require advanced programming skills as well as machine learning, data preprocessing, and visualization skills. In this research, we propose a web-based end-to-end pipeline that is capable of preprocessing, training, evaluating, and visualizing machine learning (ML) models without manual intervention or coding expertise. By integrating traditional machine learning and deep neural network models with visualizations, our library assists in recognizing, classifying, clustering, and predicting a wide range of multi-modal, multi-sensor datasets, including images, languages, and one-dimensional numerical data, for drug discovery, pathogen classification, and medical diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications Pillai, Nisha Das, Athish Ram Ayoola, Moses Gireesan, Ganga Nanduri, Bindu Ramkumar, Mahalingam Artificial Intelligence Artificial intelligence (AI) techniques are widely applied in the life sciences. However, applying innovative AI techniques to understand and deconvolute biological complexity is hindered by the learning curve for life science scientists to understand and use computing languages. An open-source, user-friendly interface for AI models, that does not require programming skills to analyze complex biological data will be extremely valuable to the bioinformatics community. With easy access to different sequencing technologies and increased interest in different 'omics' studies, the number of biological datasets being generated has increased and analyzing these high-throughput datasets is computationally demanding. The majority of AI libraries today require advanced programming skills as well as machine learning, data preprocessing, and visualization skills. In this research, we propose a web-based end-to-end pipeline that is capable of preprocessing, training, evaluating, and visualizing machine learning (ML) models without manual intervention or coding expertise. By integrating traditional machine learning and deep neural network models with visualizations, our library assists in recognizing, classifying, clustering, and predicting a wide range of multi-modal, multi-sensor datasets, including images, languages, and one-dimensional numerical data, for drug discovery, pathogen classification, and medical diagnostics. |
| title | EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.18203 |