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Virtual screening (VS) is a computational technique used in drug discovery. VS process usually works by identifying the ability of structures to bind each other. One of the structure interpretation is molecular fingerprints. Molecular fingerprints are used for computational drug discovery as feature for VS. A variety of fingerprint types has been introduced. Combining two or more fingerprints into...
Nowadays, the trend of drugs leads to multi-target drug. A drug compound may have one or more protein targets. Drugs that have multi-target protein considered to be more potential in the future. Virtual screening (VS) is a computational technique used in drug discovery to find the protein target of drugs. Virtual screening is usually based on compound similarity or database docking. Thus, the identification...
In this paper, multimodal Deep Boltzmann Machines (DBM) is employed to learn important genes (biomarkers) on gene expression data from human carcinoma colorectal. The learning process involves gene expression data and several patient phenotypes such as lymph node and distant metastasis occurrence. The proposed framework in this paper uses multimodal DBM to train records with metastasis occurrence...
In this paper, a framework using deep learning approach is proposed to identify two subtypes of human colorectal carcinoma cancer. The identification process uses information from gene expression and clinical data which is obtained from data integration process. One of deep learning architecture, multimodal Deep Boltzmann Machines (DBM) is used for data integration process. The joint representation...
Bayesian Network is a promising method for modelling probabilistic relationships among causally related variables. This paper presents an application of Bayesian Network for identifying the occurrence of metastasis in a patient with positive or negative breast cancer tumor based on observed clinical parameters. Its structure is built using K2 search algorithm with a topological order obtained from...
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