Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease

Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The new algorithm combines K-means with consensus clustering in order build cohort-specific decision trees that improve classification as well as aid the understanding of the underlying differences of the discovered groups. The methods are tested on a real-world freely available breast cancer dataset and data from a London hospital on systemic sclerosis, a rare potentially fatal condition. Results show that “nearest consensus clustering classification” improves the accuracy and the prediction significantly when this algorithm has been compared with competitive similar methods

Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease

Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The new algorithm combines K-means with consensus clustering in order build cohort-specific decision trees that improve classification as well as aid the understanding of the underlying differences of the discovered groups. The methods are tested on a real-world freely available breast cancer dataset and data from a London hospital on systemic sclerosis, a rare potentially fatal condition. Results show that "nearest consensus clustering classification" improves the accuracy and the prediction significantly when this algorithm has been compared with competitive similar methods

Histologic characterization of cellular infiltration in autoimmune subepidermal bullous diseases in a tertiary hospital in Saudi Arabia

Hessah F BinJadeed,1 Alanoud M Alyousef,1 Fahad M Alsaif,2 Ahmed A Alhumidi,3 Homaid O Alotaibi2 1College of Medicine, King Saud University, Riyadh, Saudi Arabia, 2Department of Dermatology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, 3Department of Pathology, College of Medicine, King Saud University, Riyadh, Saudi Arabia Background: Autoimmune subepidermal bullous dermatoses have similar clinical features to those of a spectrum of immune reactants at the dermo-epidermal junction (DEJ). It is difficult to obtain a precise diagnosis without an immunofluorescence assay because of their similar clinical presentations. The aim of this study was to describe the cellular cutaneous infiltration among autoimmune subepidermal bullous dermatoses. Materials and methods: This retrospective analysis was conducted at a hospital in Riyadh, Saudi Arabia using biopsy-based data collected from 65 patients. Results: Spongiotic changes, neutrophils, and lymphocyte infiltrations in the epidermis differed among the subepidermal bullous diseases. The DEJ showed a difference in the extent of neutrophil infiltration. The dermis showed differences in perivascular lymphocytic infiltration, neutrophilic infiltration, eosinophilic infiltration, and dermal edema. Conclusion: The dermal and DEJ showed most of the histopathologic changes in subepidermal autoimmune bullous dermatoses. Keywords: bullous pemphigoid, dermatitis herpetiformis, Saudi Arabia, subepidermal autoimmune bullous disorders, pemphigoid gestationi

Latent class multi-label classification to identify subclasses of disease for improved prediction

Disease subtyping can assist the development of precision medicine but remains a challenge in data analysis by reason of the many different methods to group individuals depending on their data. However, identification of subclasses of disease will help to produce better models which are more specific to patients and will improve prediction and interpretation of underlying characteristics of disease. This paper presents a novel algorithm that integrates latent class models with supervised learning. The new algorithm uses latent class models to cluster patients within groups that results in improved classification as well as aiding the understanding of the dissimilarities of the discovered groups. The methods are tested on data from patients with Systemic Sclerosis (SSc), a rare potentially fatal condition. Results show that the "Latent Class Multi-Label Classification Model" improves accuracy when compared with competitive similar methods

Quality attributes of Bearss Seedless lime (Citrus latifolia Tan) juice during storage.

The composition of three types of date juices, that differs by their couple of extraction and obtained from the rest of the sorting of cultivars Deglet Nour, were studied. The fruits were grown in Djerid region (Tozeur, Tunisia). Juices were characterised by yield, pH, soluble solids, organic acid, minerals content, individual carbohydrates, vitamin C, yeasts and moulds, coliforms and flora total aerobe contents. For the physicochemical parameters, only the J3 presents the best yields with content in citric acid of 2.13 g L-1, in phosphor of 0.083% (dry mater), in glucose 26.529 g L-1, in fructose 39.59 g L-1 and in sucrose 185.883 g L-1. For the bacteriological parameters, the results show that all prepared juices answer the microbiological requirements of hygiene well