AIMC Topic: Neoplasms

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Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine.

Journal of theoretical biology
At present, the study of gene expression data provides a reference for tumor diagnosis at the molecular level. It is a challenging task to select the feature genes related to the classification from the high-dimensional and small-sample gene expressi...

A Frame-Based NLP System for Cancer-Related Information Extraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep le...

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research.

Journal of biomedical informatics
Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevol...

MSIpred: a python package for tumor microsatellite instability classification from tumor mutation annotation data using a support vector machine.

Scientific reports
Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite lengths due to deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important ...

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.

Computational biology and chemistry
BACKGROUND: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target inte...

Laplacian regularized low-rank representation for cancer samples clustering.

Computational biology and chemistry
Cancer samples clustering based on biomolecular data has been becoming an important tool for cancer classification. The recognition of cancer types is of great importance for cancer treatment. In this paper, in order to improve the accuracy of cancer...

Deep transfer learning-based hologram classification for molecular diagnostics.

Scientific reports
Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images fr...

Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care.

The oncologist
BACKGROUND: Rapid advances in science challenge the timely adoption of evidence-based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI)...

Exploring Active Learning Based on Representativeness and Uncertainty for Biomedical Data Classification.

IEEE journal of biomedical and health informatics
Nowadays, there is an abundance of biomedical data, such as images and genetic sequences, among others. However, there is a lack of annotation to such volume of data, due to the high costs involved to perform this task. Thus, it is mandatory to devel...