Artificial Intelligence in Biological Data

Chakraborty, Indrajeet; Choudhury, Amarendranath

doi:10.4172/2165-7866.1000207

Cited by 12 publications

(6 citation statements)

References 60 publications

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“…AI refers to the use of computers and technology to simulate intelligent behavior and critical thinking similar to that of a human being (53). Big data analysis and tasks, such as data mining are prime areas of AI implementation, using techniques such as simulation models, decision algorithms and artificial neural network modeling (54). The application of continuously evolving AI computing models in biomedical research has greatly helped to navigate unclear and heterogeneous data (55).…”

Section: Computational Biology In the Era Of Biomedical Big Datamentioning

confidence: 99%

Multiple sclerosis and computational biology (Review)

et al. 2022

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Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

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Section: Computational Biology In the Era Of Biomedical Big Datamentioning

confidence: 99%

Multiple sclerosis and computational biology (Review)

et al. 2022

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“…Since AI-assisted models function with minimal manual interventions, handling large cohorts under stressful circumstances becomes highly feasible (Davenport and Kalakota 2019; Rong et al 2020; Azuaje 2019). Additionally, AI-enabled multilayer models can relieve biologists of complex data interpretations with the use of simpler visualizations for effective medical decisions (Chakraborty et al 2017; Chin-Yee and Upshur 2019).…”

Section: Ai-enabled Covid-19 Vaccine Investigationmentioning

confidence: 99%

Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy

Shafi¹,

Desai²,

Srinivasan³

et al. 2021

Mol Genet Genomics

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Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.

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“…Pulling them with the use of valid scientific ways and processing towards constructing any meaningful conclusions are challenging tasks. AI techniques appear as helpful tools to deal (extract, process and analyze) with such kind of big biological research data [ 4 ]. Knowledge on computing models using AI, advanced analytics of data and various optimization approaches that are used in bioinformatics, bioengineering and biophysics research on designing drugs and related analysis, medical imaging data analysis, biologically inspired artificial learning and adaption for general analytics, etc.…”

Section: Introductionmentioning

confidence: 99%

“…SNPs in promoter regions 3. splice site (+2 to −2 bp in exon boundary at splice junction) 4. splice region (−2 to −15 bp in exon boundary) 5.…”

mentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Ashrafuzzaman

2021

Membranes

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Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

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Artificial Intelligence in Biological Data

Cited by 12 publications

References 60 publications

Multiple sclerosis and computational biology (Review)

Multiple sclerosis and computational biology (Review)

Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

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