A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering

Abstract

In the field of computational bioinformatics, identifying a set of genes which are responsible for a particular cellular mechanism, is very much essential for tasks such as medical diagnosis or disease gene identification. Accurately grouping (clustering) the genes is one of the important tasks in understanding the functionalities of the disease genes. In this regard, ensemble clustering becomes a promising approach to combine different clustering solutions to generate almost accurate gene partitioning. Recently, researchers have used generative model as a smart ensemble method to produce the right consensus solution. In the current paper, we develop a protein-protein interaction-based generative model that can efficiently perform a gene clustering. Utilizing protein interaction information as the generative model's latent variable enables enhance the generative model's efficiency in inferring final probabilistic labels. The proposed generative model utilizes different weak supervision sources rather utilizing any ground truth information. For weak supervision sources, we use a multi-objective optimization based clustering technique together with the world's largest gene ontology based knowledge-base named Gene Ontology Consortium(GOC). These weakly supervised labels are supplied to a generative model that eventually assigns all genes to probabilistic labels. The comparative study with respect to silhouette score, Biological Homogeneity Index (BHI) and Biological Stability Index (BSI) proves that the proposed generative model outperforms than other state-of-the-art techniques.

Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation

“…Such work includes examples from the medical domain (Callahan et al, 2019;Dutta and Saha, 2019;Dutta et al, 2020;Saab et al, 2019Saab et al, , 2020, multi-task learning (Ratner et al, 2018(Ratner et al, , 2019a, information extraction (Ehrenberg et al, 2016), and learning discourse structure (Badene et al, 2019). Like our work, such work often adjusts the Snorkel framework (Ratner et al, 2017) for the task at hand.…”

Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification

“…In the field of Bioinformatics, there are various web-based enriched archives 12 that contain multi-omics biological information regarding protein interactions. The integration of multi-omics information from these aforementioned databases helps in understanding the various physiological characteristics (Sun et al, 2019;Ray et al, 2014;Amemiya et al, 2019;Hsieh et al, 2017;Dutta et al, 2020). Hence, in our current work, along with the textual information from biomedical corpora, we have also incorporated structural properties of protein molecules as biological information for solving PPI task.…”

Improving classification of correct and incorrect protein–protein docking models by augmenting the training set

“…This solution is used to automatically label the abundant sets of unlabeled MRIs, which are then used to train and enhance aortic valve malfunction classifiers. An example of the application of Snorkel in biology is to improve the retrieval of information from the literature and clustering families of genes according to ontology terms ( Dutta et al , 2020 ). In biomedicine/biochemistry, Snorkel was used for extracting chemical reaction relationships from biomedical literature abstracts ( Mallory et al , 2020 ).…”