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The Business Machine in biology-The Commercialization of AI in the Life Sciences

ieee ANNALS OF THE HISTORY OF COMPUTING 2022年第1期44卷 8-18页

作者： Stevens, Hallam Nanyang Technol Univ Singapore 639798 Singapore

This article traces one important trajectory in the history of expert systems. Through a collaboration between Edward Feigenbaum and the geneticist Joshua Lederberg, Nobel Laureate in Medicine, AI became deeply connected to the life sciences. biology was a crucial test bed for some of Feigenbaum's systems and, in the long term, these systems had a transformative effect on biology. In particular, the work of Feigenbaum and his collaborators and students brought biology and computing together in especially powerful ways. We now take for granted that biology can be computerized-we have whole subdisciplines such as bioinformatics, biocomputing, and computational biology devoted to the task of studying life as information. The computer systems and software that Feigenbaum's lab helped to develop played an important role in establishing the possibility of these kinds of work.

关键词： Artificial intelligence Expert systems History biology Business Presses Life sciences expert systems Edward A Feigenbaum IntelliGenetics IntelliCorp Teknowledge Joshua Lederberg MOLGEN

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Evolving Lockdown Strategies to Minimize Infections in an Epidemic

Evolving Lockdown Strategies to Minimize Infections in an Ep...

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ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Sargant, James Dube, Michael Houghten, Sheridan Brock Univ Dept Comp Sci St Catharines ON Canada Univ Guelph Dept Math & Stat Guelph ON Canada

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

In this paper we evaluate the impact of different lockdown strategies upon the total number of infections during an epidemic. The strategies are based upon the percentage of the population infected during a given time step, as well as upon the amount by which interactions must be reduced during lockdown. We use a weighted personal contact network to represent the population, its interactions, and the relative strengths of those interactions. During lockdown edges from this network are removed. We use an evolutionary algorithm to choose the set of edges to be removed so as to minimize infections, comparing different strategies. We show that allowing the evolutionary algorithm to choose which edges to remove significantly reduces the overall number of infections in comparison to random selection. In fact, the EA results for the least stringent conditions were similar or better to the random results for the most stringent conditions, showing that a judicious choice of restrictions during lockdown has the greatest effect on reducing infections. The evolutionary algorithm tends to favour a situation in which during lockdown individuals would reduce their number of contacts, as opposed to lessening the strength of their connections.

关键词： Epidemics Sociology Evolutionary computation Statistics bioinformatics computational biology

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Predicting and Characterizing Legal Claims of Hospitals with computational intelligence: the Legal and Ethical Implications

Predicting and Characterizing Legal Claims of Hospitals with...

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ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Gallese, Chiara Fuchs, Caro Riva, Simone G. Foglia, Emanuela Schettini, Fabrizio Ferrario, Lucrezia Falletti, Elena Nobile, Marco S. LIUC Carlo Cattaneo Univ Sch Ind Engn Castellanza VA Italy Eindhoven Univ Technol Jheronimus Acad Data Sci Shertogenbosch Netherlands Univ Oxford Weatherall Inst Mol Med Radcliffe Dept Med Oxford England LIUC Carlo Cattaneo Univ Ctr Hlth Econ Social & Healthcare Management Castellanza VA Italy LIUC Carlo Cattaneo Univ Sch Law Castellanza VA Italy Ca Foscari Univ Venice Dept Environm Sci Informat & Stat Venice Italy

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre- and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C- Means and swarm intelligence. The dataset that we analyse allows us to compare pre- and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field.

关键词： computational intelligence AI and Law AI Ethics Health Care Covid-19 Fuzzy Logic

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Artificial intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第4期21卷 814-822页

作者： Ahmed, Imran Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin Univ Sch Comp & Informat Sci Cambridge CB1 1PT England Royal Mil Coll Canada Dept Math & Comp Sci Kingston ON K7K 7B4 Canada Incheon Natl Univ Dept Embedded Syst Engn Incheon 406772 South Korea Incheon Natl Univ Energy Excellence & Smart City Lab Incheon 22012 South Korea

Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilized in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualization approach to all of the tests. As a result, the architecture achieves a classification accuracy rate of 0.96, thus producing excellent results.

关键词： COVID-19 Blockchains Medical services Pandemics Artificial intelligence Machine learning Monitoring blockchain deep learning

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Artificial intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 2837-2852页

作者： Madadi, Yeganeh Monavarfeshani, Aboozar Chen, Hao Stamer, W. Daniel Williams, Robert W. Yousefi, Siamak Univ Tennessee Dept Ophthalmol Hlth Sci Ctr Memphis TN 38163 USA Harvard Univ Ctr Brain Sci Cambridge MA 02138 USA Harvard Univ Dept Mol & Cellular Biol Cambridge MA 02138 USA Boston Childrens Hosp FM Kirby Neurobiol Ctr Boston MA 02115 USA Univ Tennessee Dept Pharmacol Addict Sci & Toxicol Hlth Sci Ctr Memphis TN 38163 USA Duke Univ Duke Eye Ctr Dept Ophthalmol Durham NC 27708 USA Univ Tennessee Dept Genet Genom & Informat Hlth Sci Ctr Memphis TN 38163 USA

Single-cell RNA sequencing (scRNA-seq) provides ahigh throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. How-ever, scRNA-seq based identification of discrete cell-types is still la-bor intensive and depends on prior molecular knowledge. Artificialintelligence has provided faster, more accurate, and user-friendlyapproaches for cell-type identification. In this review, we discussrecent advances in cell-type identification methods using artificialintelligence techniques based on single-cell and single-nucleus RNAsequencing data in vision science. The main purpose of this review paper is to assist vision scientists not only to select suitable data sets for their problems, but also to be aware of the appropriate computational tools to perform their analysis. Developing novel methods for scRNA-seq data analysis remains to be addressed in future studies.

关键词： Artificial intelligence review single-cell RNA sequencing vision science

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Discovery of Bioactive Constituents for Colitis From Traditional Chinese Medicine Prescription via Deep Neural Network

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 946-955页

作者： Ren, Zhixiang Ren, Yiming Liu, Pengfei Shu, Qi Ma, Huijuan Xu, Huan Peng Cheng Lab Shenzhen 518055 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 528406 Peoples R China East China Univ Sci & Technol Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Pharm Shanghai Key Lab New Drug Design Shanghai 200237 Peoples R China Anhui Univ Sci & Technol Joint Res Ctr Occupat Med & Hlth IHM Hefei 231131 Peoples R China Anhui Univ Sci & Technol Sch Publ Hlth Hefei 231131 Peoples R China

Colitis is a commonly encountered inflammatory disease in colon tissue, which can be triggered by various causes. Although a few ingredients in traditional Chinese medicine (TCM) have been identified as effective for the treatment of colitis, it remains a great challenge to discover the potential therapeutic bioactive constituents and their modes of action among thousands of ingredients in TCM prescriptions. To address this issue, we propose a pipeline that combines deep neural network (DNN) with network pharmacology to discover bioactive constituents. By integrating the herbal information network of 9,845 nodes and 161,950 edges, which includes detailed information on bioactive molecules and protein targets, with a prescription list expanded through a novel data augmentation strategy, the DNN can recommend diverse herbal combinations. Network pharmacology study revealed that the 10 most frequent constituents in recommended prescriptions were associated with multiple inflammatory signaling pathways. To verify the bioactive constituents in the recommended prescriptions, 5 selected constituents were administrated to BALB/c mice with colitis. Suppressive effects of disease progression and pro-inflammatory factors comparable to sulfasalazin were observed with these compounds, revealing the effectiveness of our artificial intelligence strategy in discovering bioactive constituents from TCM prescriptions.

关键词： Training Pharmacology Compounds Diseases Medical diagnostic imaging Data mining computational biology bioinformatics Artificial neural networks Proteins Deep neural network bioactive constituents colitis anti-inflammation network pharmacology traditional Chinese medicine prescriptions

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Datt-AVP: Antiviral Peptide Prediction by Sequence-Based Dual Channel Network With Attention Mechanism

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 537-544页

作者： Sun, Jiawei Qian, Weiye Ma, Nan Liu, Wenjia Yang, Zhiyuan Hangzhou Dianzi Univ Sch Artificial Intelligence Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou 310018 Peoples R China

With the frequent outbreak of viral pandemics, the search for efficient antiviral drugs has become an urgent task. Antiviral peptides (AVPs) have been proven to prevent the infection of host cells by viruses. This study proposes a novel tool called Datt-AVP for AVP prediction based on the peptide sequences. A dual-channel deep learning model of Long Short-Term Memory network and convolutional neural network, along with a pre-trained protein language model as a feature extractor, was applied to capture features in the antiviral peptide sequences simultaneously. Furthermore, self-attention module was added to promote the performance of our prediction results. It showed good recognition ability for antiviral peptides with different lengths. Our tool achieved an accuracy rate of 96.1% on the benchmark dataset, which out-performed state-of-the-art tools.

关键词： Peptides Long short term memory Vectors Encoding Feature extraction Biological system modeling bioinformatics Drugs Training Tensors Antiviral peptide deep learning attention mechanism

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Deep Transfer Learning for Cross-Species Plant Disease Diagnosis Adapting Mixed Subdomains

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2555-2564页

作者： Yan, Ke Guo, Xinlu Ji, Zhiwei Zhou, Xiaokang China Jiliang Univ Coll Informat Engn Key Lab Electromagnet Wave Informat Technol & Metr Hangzhou 310018 Zhejiang Peoples R China Natl Univ Singapore Coll Design & Engn Singapore 117566 Singapore Nanjing Agr Univ Coll Artificial intelligence Nanjing 210095 Jiangsu Peoples R China Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan

A deep transfer learning framework adapting mixed subdomains is proposed for cross-species plant disease diagnosis. Most existing deep transfer learning studies focus on knowledge transfer between highly correlated domains. These methods may fail to deal with domains that are poorly correlated. In this study, mixed domain images were generated from source and target image groups for improving the correlation between the mixed domain (training dataset) and the target domain (testing dataset). A subdomain alignment mechanism is employed to transfer knowledge from the mixed domain to the target domain. The proposed framework captures the fine-grained information more effectively. Extensive experiments were conducted and prove that the proposed method produces a more effective result compared with existing deep transfer learning technologies for poorly related subdomains.

关键词： Terms-Deep Transfer Learning Plant Disease Diagnosis Domain adaptation Subdomain

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Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 1020-1029页

作者： Nguyen, Tri Minh Quinn, Thomas P. Nguyen, Thin Tran, Truyen Deakin Univ Appl Artificial Intelligence Inst Burwood Vic 3217 Australia

Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the models. Counterfactual explanation is one popular approach to explaining the behaviour of a deep neural network, which works by systematically answering the question "How would the model output change if the inputs were changed in this way?". We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex. Our proposed framework provides human-interpretable counterfactual instances while optimizing both the input drug and target for counterfactual generation at the same time. We benchmark the proposed MACDA framework using the Davis and PDBBind dataset and find that our framework produces more parsimonious explanations with no loss in explanation validity, as measured by encoding similarity. We then present a case study involving ABL1 and Nilotinib to demonstrate how MACDA can explain the behaviour of a DTA model in the underlying substructure interaction between inputs in its prediction, revealing mechanisms that align with prior domain knowledge.

关键词： Drugs Proteins Predictive models Biological system modeling Reinforcement learning Deep learning computational modeling Black box deep learning counterfactual explanation drug-target affinity substructure interaction

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An End-to-End Deep Hybrid Autoencoder Based Method for Single-Cell RNA-Seq Data Analysis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第6期20卷 3889-3900页

作者： Ji, Cunmei Yu, Ning Wang, Yutian Qi, Rong Zheng, Chunhou Qufu Normal Univ Sch Cyber Sci & Engn Qufu 273165 Peoples R China Anhui Univ Sch Artifial Intelligence Hefei 230601 Peoples R China

Single-cell RNA sequencing technology provides powerful support for researchers to understand the complex mechanisms of cells at the single-cell level. Due to the high sparsity, technical noise, and computational complexity of single-cell transcriptome data, the existing data analysis methods are unable to effectively extract the fine-grained characteristics of scRNA-seq data, resulting in inaccurately analyze the heterogeneity of the individual cell from a great quantity of cell mixtures. To address these shortcomings, we proposed an end-to-end analysis method called dhaSCA, which integrates the Graph convolutional neural network (GCN) feature learning and downstream tasks such as classification and imputation into a unified deep learning manner. dhaSCA uses hybrid GCN-MLP deep autoencoder and to capture structural information between cells, and learn the low dimensional cell representation. It also introduces downstream tasks as constraints to guide the model to learn more accurate cell features. We conducted various experiments to evaluate the performance of dhaSCA based on eight real RNA-Seq datasets, including classification, imputation, clustering, and visualization. The results show that dhaSCA outperforms other state-of-the-art methods in these downstream tasks. Therefore, dhaSCA is able to obtain a richer representation of cells, and provides strong support for efficient analysis of single-cell data.

关键词： Cell classification cell type identification deep learning graph convolutional network single cell RNA sequencing

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