AIMC Topic: Software

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FeatureSelect: a software for feature selection based on machine learning approaches.

BMC bioinformatics
BACKGROUND: Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. M...

CaseSolver: An investigative open source expert system based on EuroForMix.

Forensic science international. Genetics
For very serious crimes, reporting scientists often have to contend with complex cases where literally hundreds of items are submitted by investigators for analysis. In order to efficiently expedite the challenge of comparing reference profiles to ev...

QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research.

BMC medical informatics and decision making
BACKGROUND: While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.

Analysis of a CT patient dose database with an unsupervised clustering approach.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This study investigated the benefits of implementing a cluster analysis technique to extract relevant information from a computed tomography (CT) dose registry archive.

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.

Development and psychopathology
As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expr...

Selene: a PyTorch-based deep learning library for sequence data.

Nature methods
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...

Comparison between two programs for image analysis, machine learning and subsequent classification.

Tissue & cell
In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cyto...

Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.

IEEE transactions on biomedical circuits and systems
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since b...

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.

PLoS computational biology
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing resu...

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...