Genetics

Deep learning-based clustering approaches for bioinformatics.

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights o...

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the la...

m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.

N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and...

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated ge...

FireProtDB: database of manually curated protein stability data.

The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles...

Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic targe...

Mouse Genome Database (MGD): Knowledgebase for mouse-human comparative biology.

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism knowledgebase for the laboratory mouse, a widely used ...

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease.

In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowled...

Applying Machine Learning for Integration of Multi-Modal Genomics Data and Imaging Data to Quantify Heterogeneity in Tumour Tissues.

With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing s...

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. H...

Artificial Intelligence for Epigenetics: Towards Personalized Medicine.

Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function, not due to modifications of the ...

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction.

Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene express...

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of...

Using machine learning method to identify as a novel marker to predict biochemical recurrence in prostate cancer.

This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negativ...

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological co...

Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.

As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the ...

Overcoming Interpretability in Deep Learning Cancer Classification.

Since its inception, deep learning has revolutionized the field of machine learning and data-driven science. One such data-driven science to be transf...

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer.

About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite dif...

Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.

Escherichia coli is a species of bacteria that can be present in a wide variety of mammalian hosts and potentially soil environments. E. coli has an o...

Brief Survey on Machine Learning in Epistasis.

In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted...