Representation Learning for the Clustering of Multi-Omics Data

Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward

We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.

The childhood arthritis radiographic score of the hip: the proposal cut-off value using cluster analysis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is a chronic rheumatic disease that affects children. It is crucial to detect and treat hip involvement in JIA early to prevent functional impairment and reduced quality of life. The Childhood Arthritis Radiographic Score of the Hip (CARSH) is a validated radiographic scoring system used to assess hip involvement in JIA. In this study, we aimed to determine cut-off values for CARSH scores using cluster analysis.

METHODS

The study was conducted as a cross-sectional analysis and included JIA patients with hip involvement who underwent a pelvic radiograph. The same pelvic radiograph was interpreted by two experienced pediatric rheumatologists at baseline and after 3 weeks by both readers for reliability. The CARSH scores were calculated for each hip four times (twice by each reader). For the 50 hips, a total of 200 interpretations of the CARSH score were obtained. Model-based clustering was employed to identify distinct groups of CARSH score interpretations and characterize the phenotype of each cluster.

RESULTS

Twenty-five children with hip involvement were included. The mean age was 13.9 ± 4.6 years. JIA subtypes were as follows: ERA in 64%, oligoarthritis in 16%, psoriatic arthritis in 12%, polyarthritis RF + in 4%, and RF - in 4% of patients. For the 200 hip interpretations, three clusters based on the level of the CARSH were identified by model-based clustering. Cluster 1 consisted of 17 CARSH score interpretations with a median score of 7 ± 3 (ranging from 1 to 15). This group primarily comprised patients with enthesitis-related arthritis (ERA) and psoriatic arthritis. Patients in cluster 1 were generally older, experienced longer diagnostic delays, and had a longer disease duration compared to the other clusters. Cluster 2 exhibited a moderate CARSH score, with an average score of 4 ± 3 (1 to 15). Patients in this group had significantly higher body weight compared to the other clusters. Cluster 3 represented the group with the least severe hip involvement, characterized by CARSH scores of 2 ± 1 (ranging from 0 to 9). This cluster had a higher proportion of male patients and higher C-reactive protein (CRP) levels than the other clusters. Regarding the individual items of the CARSH score, cluster 1 showed higher percentages of hip radiograph abnormalities such as joint space narrowing, erosions, growth abnormalities, and subchondral cysts. Cluster 2 was characterized by a high rate of acetabular sclerosis, with little to no abnormalities in other CARSH score items. Cluster 3 was the only group that exhibited hip subluxation, with minimal abnormalities in the other score items. In conclusion, this study identified three distinct groups of CARSH scores, representing varying levels of severity in hip involvement in JIA. These findings provide valuable insights for clinicians in assessing and managing JIA patients with hip involvement, enabling tailored treatment strategies based on the severity of the condition. Key Points • While a Childhood Arthritis Radiographic Score of the Hip (CARSH) is a valid and reliable tool in hip-related juvenile idiopathic arthritis, its use is limited in daily practice due to the lack of available cut-off values. • The cluster analysis defined three clusters based on the CARSH levels. • Cluster 1 exhibited the highest score with more damage and disability. Cluster 2 involved a moderate score and more overweight patients. Cluster 3 included the least level of the score but with an active disease parameter.

CustOmics: A versatile deep-learning based strategy for multi-omics integration

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.