AI Medical Compendium Journal:
Methods (San Diego, Calif.)

Showing 151 to 160 of 183 articles

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Methods (San Diego, Calif.)
Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where prema...

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.)
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...

IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques.

Methods (San Diego, Calif.)
Inverse Virtual Screening is a powerful technique in the early stage of drug discovery process. This technique can provide important clues for biologically active molecules, which is useful in the following researches of durg discovery. In this work,...

Enhancer prediction with histone modification marks using a hybrid neural network model.

Methods (San Diego, Calif.)
Enhancer is a DNA sequence of a genome that controls transcription of downstream target genes. Enhancers are known to be associated with certain epigenetic signatures. Machine learning tools, such as CSI-ANN, ChromHMM, and RFECS, were developed for p...

MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Methods (San Diego, Calif.)
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology...

Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

Methods (San Diego, Calif.)
We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulatio...

Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.

Methods (San Diego, Calif.)
Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were pre...

Exploring semi-supervised variational autoencoders for biomedical relation extraction.

Methods (San Diego, Calif.)
The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). Biomedical relation extraction aims to automatically extract biomedical...

Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning.

Methods (San Diego, Calif.)
Due to the complex and intricate nature associated with their production, the acoustic-prosodic properties of a speech signal are modulated with a range of health related effects. There is an active and growing area of machine learning research in th...

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Methods (San Diego, Calif.)
Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare...