Artificial Intelligence (AI) is increasingly being used in bioinformatics to analyze large volumes of biological data and to develop predictive models for various biological phenomena. However, as the complexity of these AI systems grows, it becomes more challenging to understand how they arrive at their conclusions or predictions. This lack of interpretability is a significant challenge for bioinformatics, where understanding the rationale behind a decision or prediction is critical for building trust in the model and identifying potential errors or biases. To address this challenge, the field of explainable AI (XAI) has emerged, which aims to develop AI models that can provide a transparent and interpretable rationale for their predictions. In bioinformatics, XAI can play a crucial role in enabling researchers to gain insights into complex biological systems, improve disease diagnosis and treatment, and identify new drug targets. This article explores the significance of XAI for bioinformatics and how it can help researchers understand and interpret the predictions of AI models in the field.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In the article two frameworks for supporting automatic medical consultation, which are doctor-patient dialogue understanding and task-oriented interaction, using machine learning, are proposed. The authors create a new large medical dialogue dataset with fine-grained annotations and establish five independent tasks including named entity recognition, dialogue act classification, symptom label inference, medical report generation, and diagnosis-oriented dialogue policy. The authors report benchmark results for each task, which demonstrate the usability of the dataset and establish a baseline for future studies. The article aims to improve the efficiency of automatic medical consultation and enhance patient experience.
Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review
The article discusses the use of single-cell RNA sequencing (scRNA-seq) to study cell states and phenotypes, as well as the potential applications in understanding biological processes and disease states. It also explores the use of deep learning, an artificial intelligence technique, in scRNA-seq data analysis. The review surveys recent developments in deep learning techniques for scRNA-seq data analysis, identifies key steps that have been advanced by deep learning, and explains the benefits of deep learning over conventional analytic tools. The article also summarizes the challenges faced by current deep learning approaches in scRNA-seq data analysis and discusses potential directions for improving deep learning algorithms in this field.
Mining On Alzheimer’s Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
The article discusses the use of knowledge graphs to identify opportunities for preventing or delaying neurodegenerative diseases, specifically Alzheimer's Disease (AD). The authors constructed a knowledge graph using biomedical annotations and extracted relations using SemRep via SemMedDB. They used a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The filtered triples were used to train knowledge graph completion algorithms to predict candidates that might be helpful for AD treatment or prevention. The results showed that TransE outperformed other models, and time-slicing techniques were used to further evaluate the prediction results. The authors found supporting evidence for most highly ranked candidates predicted by the model, indicating that their approach can inform reliable new knowledge. The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses in the field of neurodegenerative diseases.
Recommendations for extending the GFF3 specification for improved interoperability of genomic data
The article discusses the GFF3 format, which is widely used to represent the structure and function of genes and other mapped features. However, the flexibility of this format has become an obstacle to standardized downstream processing due to the different notations used by common software packages. To address this issue, the AgBioData consortium has developed recommendations for improving the GFF3 format, including providing concrete guidelines for generating GFF3 and creating a standard representation of the most common biological data types. The AgBioData GFF3 working group suggests improvements for each GFF3 field, as well as special cases of modeling functional annotations and standard protein-coding genes, to increase efficiency for AgBioData databases and the genomics research community.
An Artificial Intelligence Technique for Covid-19 Detection with eXplainability using Lungs X-Ray Images
The article discusses how limited healthcare resources and unequal distribution of healthcare facilities have made disease detection critical in averting epidemics, particularly in the case of COVID-19. PCR testing is commonly used to detect the virus, but deep learning approaches can also be used to classify chest X-RAY images. The study aims to detect COVID-19 by using deep learning approaches to analyze chest X-RAY images of COVID-19 patients, viral pneumonia patients, and healthy patients obtained from IEEE and Kaggle. The dataset was subjected to a data augmentation approach before classification, and multi classification deep learning models were used to classify the three groups.
Deep learning for drug repurposing: methods, databases, and applications
The article discusses the potential of repurposing existing drugs for new therapies, specifically for COVID-19, as a way to accelerate drug development and reduce costs. However, effectively utilizing deep learning models for drug repurposing in complex diseases is still challenging. The article provides guidelines for utilizing deep learning methodologies and tools for drug repurposing, including commonly used bioinformatics and pharmacogenomics databases, sequence-based and graph-based representation approaches, and state-of-the-art deep learning-based methods. The article also presents applications of drug repurposing for COVID-19 and outlines future challenges.
Direct Molecular Conformation Generation
The article presents a new method for generating the three-dimensional coordinates of atoms in a molecule, which is important in bioinformatics and pharmacology. The proposed method directly predicts the coordinates of atoms without predicting intermediate values such as interatomic distances or local structures. The method is invariant to roto-translation of coordinates and permutation of symmetric atoms, and adaptively aggregates bond and atom information to iteratively refine the generated conformation. The method achieves the best results on two datasets and improves molecular docking by providing better initial conformations. The article concludes that the direct approach has great potential and provides a link to the released code.
MPVNN: Mutated Pathway Visible Neural Network Architecture For Interpretable Prediction Of Cancer-Specific Survival Risk
The article presents a novel approach for survival risk prediction using gene expression data in cancer, called Mutated Pathway Visible Neural Network (MPVNN). MPVNN is designed using prior knowledge of biological signaling pathways and gene mutation data-based edge randomization, which simulates signal flow disruption. The study uses the PI3K-Akt pathway as a case study and shows improved cancer-specific survival risk prediction results of MPVNN over standard non-NN and other similar sized NN survival analysis methods. The trained MPVNN architecture interpretation is reliable and points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that are important in risk prediction for particular cancer types. The article highlights the importance of interpretability in survival analysis models for making treatment decisions in cancer.
Conclusions
In conclusion, the application of XAI techniques in bioinformatics has the potential to enhance the accuracy, transparency, and interpretability of machine learning models, enabling scientists to make more informed decisions and gain a better understanding of the underlying biological processes. By providing explanations for the predictions made by these models, XAI can facilitate the identification of relevant biomarkers, aid in the diagnosis of diseases, and assist in the development of personalized treatments. However, there are still challenges to be addressed, such as the need for standardized guidelines and approaches to XAI in bioinformatics, as well as the integration of XAI with existing bioinformatics tools and workflows. As research in this area continues, it is expected that XAI will play an increasingly important role in advancing our understanding of complex biological systems and ultimately lead to improved patient outcomes.
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